pax_global_header00006660000000000000000000000064151133455770014525gustar00rootroot0000000000000052 comment=bca0315ff3e56bb5847509ca32fadb8d87f680e1 onnx-onnx-bca0315/000077500000000000000000000000001511334557700140675ustar00rootroot00000000000000onnx-onnx-bca0315/.clang-format000066400000000000000000000052551511334557700164510ustar00rootroot00000000000000--- Language: Cpp BasedOnStyle: Google AccessModifierOffset: -1 AlignAfterOpenBracket: AlwaysBreak AlignConsecutiveAssignments: false AlignConsecutiveDeclarations: false AlignEscapedNewlines: Left AlignOperands: false AlignTrailingComments: false AllowAllParametersOfDeclarationOnNextLine: false AllowShortBlocksOnASingleLine: false AllowShortCaseLabelsOnASingleLine: false AllowShortFunctionsOnASingleLine: Empty AllowShortIfStatementsOnASingleLine: false AllowShortLoopsOnASingleLine: false AlwaysBreakAfterDefinitionReturnType: None AlwaysBreakAfterReturnType: None AlwaysBreakBeforeMultilineStrings: true AlwaysBreakTemplateDeclarations: true BinPackArguments: false BinPackParameters: false BraceWrapping: AfterClass: false AfterControlStatement: false AfterEnum: false AfterFunction: false AfterNamespace: false AfterObjCDeclaration: false AfterStruct: false AfterUnion: false BeforeCatch: false BeforeElse: false IndentBraces: false BreakBeforeBinaryOperators: None BreakBeforeBraces: Attach BreakBeforeInheritanceComma: false BreakBeforeTernaryOperators: true BreakConstructorInitializersBeforeComma: false BreakConstructorInitializers: BeforeColon BreakStringLiterals: false ColumnLimit: 120 CommentPragmas: '^ IWYU pragma:' CompactNamespaces: false ConstructorInitializerAllOnOneLineOrOnePerLine: true ConstructorInitializerIndentWidth: 4 ContinuationIndentWidth: 4 Cpp11BracedListStyle: true DerivePointerAlignment: false DisableFormat: false ExperimentalAutoDetectBinPacking: false FixNamespaceComments: true ForEachMacros: - FOR_EACH_RANGE - FOR_EACH - BOOST_FOREACH IncludeIsMainRegex: '(Test)?$' IndentCaseLabels: true IndentWidth: 2 IndentWrappedFunctionNames: false JavaScriptQuotes: Leave JavaScriptWrapImports: true KeepEmptyLinesAtTheStartOfBlocks: false MacroBlockBegin: '' MacroBlockEnd: '' MaxEmptyLinesToKeep: 1 NamespaceIndentation: None ObjCBlockIndentWidth: 2 ObjCSpaceAfterProperty: false ObjCSpaceBeforeProtocolList: false PenaltyBreakAssignment: 2 PenaltyBreakBeforeFirstCallParameter: 1 PenaltyBreakComment: 300 PenaltyBreakFirstLessLess: 120 PenaltyBreakString: 1000 PenaltyExcessCharacter: 1000000 PenaltyReturnTypeOnItsOwnLine: 200 PointerAlignment: Left ReflowComments: true SortIncludes: true SpaceAfterCStyleCast: false SpaceAfterTemplateKeyword: true SpaceBeforeAssignmentOperators: true SpaceBeforeParens: ControlStatements SpaceInEmptyParentheses: false SpacesBeforeTrailingComments: 1 SpacesInAngles: false SpacesInContainerLiterals: true SpacesInCStyleCastParentheses: false SpacesInParentheses: false SpacesInSquareBrackets: false StatementMacros: [define_data] TabWidth: 8 UseTab: Never onnx-onnx-bca0315/.clang-tidy000066400000000000000000000040621511334557700161250ustar00rootroot00000000000000Checks: >- -*, bugprone-*, -bugprone-easily-swappable-parameters, -bugprone-lambda-function-name, -bugprone-macro-parentheses, -bugprone-narrowing-conversions, -bugprone-reserved-identifier, clang-diagnostic-*, clang-analyzer-.*, cppcoreguidelines-avoid-goto, cppcoreguidelines-interfaces-global-init, cppcoreguidelines-no-malloc, cppcoreguidelines-prefer-member-initializer, cppcoreguidelines-pro-type-member-init, cppcoreguidelines-pro-type-static-cast-downcast, cppcoreguidelines-slicing, cppcoreguidelines-special-member-functions, performance-*, google-default-arguments, google-global-names-in-headers, google-explicit-constructor, google-readability-casting, misc-*, -misc-const-correctness, -misc-include-cleaner, -misc-no-recursion, -misc-non-private-member-variables-in-classes, -misc-use-anonymous-namespace, modernize-*, -modernize-concat-nested-namespaces, -modernize-return-braced-init-list, -modernize-use-auto, -modernize-use-integer-sign-comparison, -modernize-use-ranges, -modernize-use-trailing-return-type, -modernize-use-nodiscard, readability-container-size-empty, readability-delete-null-pointer, readability-duplicate-include, readability-misplaced-array-index, readability-non-const-parameter, readability-redundant*, readability-simplify*, readability-static-accessed-through-instance, readability-static-definition-in-anonymous-namespace, readability-string-compare, readability-suspicious-call-argument, readability-uniqueptr-delete-release, -readability-redundant-access-specifiers, CheckOptions: # `cppcoreguidelines-special-member-functions` is enabled, refer to https://en.cppreference.com/w/cpp/language/rule_of_three - key: cppcoreguidelines-special-member-functions.AllowSoleDefaultDtor value: True - key: performance-move-const-arg.CheckTriviallyCopyableMove value: False - key: cppcoreguidelines-special-member-functions.AllowMissingMoveFunctionsWhenCopyIsDeleted value: True onnx-onnx-bca0315/.editorconfig000066400000000000000000000001431511334557700165420ustar00rootroot00000000000000root = true [*] trim_trailing_whitespace = true insert_final_newline = true indent_style = space onnx-onnx-bca0315/.git-blame-ignore-revs000066400000000000000000000004701511334557700201700ustar00rootroot00000000000000# Python clean up #3982 35092895d9bf3592e58f4710d098f8131afef259 # Apply clang-format #4084 a525f98d5ed31660b629bab90c680a39658a5c08 # Upgrade python syntax with pyupgrade #4212 529f7cab88eed143fc9f0126147e9732585e4c5e # Format all python code with black and isort #4427 fddb2b6d4ea3fb3dba751d884865042503260899 onnx-onnx-bca0315/.gitattributes000066400000000000000000000001041511334557700167550ustar00rootroot00000000000000# Set the default behavior * text=auto eol=auto *.bat text eol=crlf onnx-onnx-bca0315/.github/000077500000000000000000000000001511334557700154275ustar00rootroot00000000000000onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/000077500000000000000000000000001511334557700176125ustar00rootroot00000000000000onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/bug.md000066400000000000000000000022411511334557700207100ustar00rootroot00000000000000--- name: Bug report about: Create a bug report to help improve the ONNX. title: '' labels: 'bug' assignees: '' --- # Bug Report ### Is the issue related to model conversion? ### Describe the bug ### System information ### Reproduction instructions ### Expected behavior ### Notes onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000012111511334557700215750ustar00rootroot00000000000000blank_issues_enabled: true contact_links: - name: ONNX Runtime Issues url: https://github.com/microsoft/onnxruntime/issues about: issues/questions related to ONNX Runtime - name: ONNX Model Zoo Issues url: https://github.com/onnx/models/issues about: issues/questions related to ONNX Model Zoo - name: PyTorch-ONNX exporters Issues url: https://github.com/pytorch/pytorch/issues about: issues/questions related to PyTorch-ONNX exporters (torch.onnx.export) - name: TensorFlow Converter Issues url: https://github.com/onnx/tensorflow-onnx about: issues/questions converting TensorFlow models to ONNX (tf2onnx) onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/feature_request.yml000066400000000000000000000041611511334557700235420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Feature request description: Create a feature request for a functionality that does not currently exist in the ONNX. title: "[Feature request] " labels: ["enhancement"] body: - type: markdown attributes: value: Thanks for taking the time to create a feature request! - type: textarea id: system-info attributes: label: System information description: "ONNX version (you are using):" validations: required: false - type: textarea id: solves-problem attributes: label: What is the problem that this feature solves? description: Please detail the discrepancy with our current functionality. validations: required: false - type: textarea id: alternatives attributes: label: Alternatives considered description: Describe the alternatives you have considered placeholder: A clear and concise description of any alternative solutions or features you've considered. validations: required: false - type: textarea id: feature attributes: label: Describe the feature description: Why is this feature necessary? What does it accomplish? validations: required: false - type: textarea id: api-impact attributes: label: Will this influence the current api (Y/N)? placeholder: If yes, how? validations: required: false - type: textarea id: feature-area attributes: label: Feature Area description: Which area in ONNX does this impact? e.g., model usage, backend, best practices, converters, shape_inference, version_converter, training, test, operators, IR, ONNX Hub, data preprocessing, CI pipelines. validations: required: false - type: dropdown id: contribute attributes: label: "Are you willing to contribute it (Y/N)" options: - "Yes" - "No" validations: required: false - type: textarea id: notes attributes: label: Notes description: Any additional information validations: required: false onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/operator.md000066400000000000000000000010121511334557700217610ustar00rootroot00000000000000--- name: New Operator about: Create a new operator that does not currently exist in the ONNX. title: '' labels: 'operator' assignees: '' --- # New Operator ### Describe the operator ### Can this operator be constructed using existing onnx operators? ### Is this operator used by any model currently? Which one? ### Are you willing to contribute it? (Y/N) ### Notes onnx-onnx-bca0315/.github/ISSUE_TEMPLATE/question.md000066400000000000000000000010771511334557700220100ustar00rootroot00000000000000--- name: Question about: Ask a question about the ONNX. title: '' labels: 'question' assignees: '' --- # Ask a Question ### Question ### Further information - Relevant Area: - Is this issue related to a specific model? **Model name**: **Model opset**: ### Notes onnx-onnx-bca0315/.github/codeql/000077500000000000000000000000001511334557700166765ustar00rootroot00000000000000onnx-onnx-bca0315/.github/codeql/codeql-config.yml000066400000000000000000000000741511334557700221340ustar00rootroot00000000000000query-filters: - exclude: id: py/import-and-import-from onnx-onnx-bca0315/.github/copilot-instructions.md000066400000000000000000000005131511334557700221630ustar00rootroot00000000000000We use lintrunner as the linter: ```sh # Display all lints and apply the fixes lintrunner -a --output oneline # Or apply fixes only (faster) lintrunner f --output oneline ``` To build ONNX: ```sh python -m pip install --quiet --upgrade pip setuptools wheel export ONNX_BUILD_TESTS=0 export ONNX_ML=1 python -m pip install . ``` onnx-onnx-bca0315/.github/dependabot.yml000066400000000000000000000021301511334557700202530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for all configuration options: # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: "pip" # See documentation for possible values directory: "/" # Location of package manifests schedule: interval: "monthly" ignore: # Only update them manually since updating them might break compatibility - dependency-name: "numpy" - dependency-name: "protobuf" open-pull-requests-limit: 10 labels: - "python" - "run release CIs" - package-ecosystem: "github-actions" # Workflow files stored in the # default location of `.github/workflows` directory: "/" schedule: interval: "monthly" open-pull-requests-limit: 20 labels: - "github_actions" - "run release CIs" onnx-onnx-bca0315/.github/install_test.yml000066400000000000000000000020151511334557700206550ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Test (pip install -e .) on: push: branches: - main pull_request: branches: - main workflow_dispatch: permissions: contents: read jobs: test-install: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-24.04] python-version: ['3.13'] fail-fast: false steps: - name: Checkout code uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 with: persist-credentials: false - name: Set up Python uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies, build ONNX run: | python -m pip install -q --upgrade pip python -m pip install -r requirements-release_build.txt git submodule update --init --recursive source workflow_scripts/protobuf/build_protobuf_unix.sh 3 pip install -e . onnx-onnx-bca0315/.github/pull_request_template.md000066400000000000000000000000461511334557700223700ustar00rootroot00000000000000 ### Motivation and Context Fixes # onnx-onnx-bca0315/.github/release.yml000066400000000000000000000021271511334557700175740ustar00rootroot00000000000000changelog: exclude: authors: - dependabot categories: - title: Breaking Changes and Deprecations labels: - "topic: bc breaking" - "topic: deprecation" - title: Spec and Operator labels: - "module: spec" - "topic: spec clarification" - "topic: operator" - "module: schema" - title: Reference Implementation labels: - "module: reference implementation" - title: Utilities and Tools labels: - "module: checker" - "module: parser" - "module: inliner" - "module: utility" - "module: optimizer" - "module: version converter" - "module: shape inference" - "topic: partial data propagation" - title: ONNX Hub labels: - "module: hub" - title: Build, CI and Tests labels: - "topic: build" - "module: CI pipelines" - "topic: test" - "topic: compiler warning" - title: Documentation labels: - "topic: documentation" - title: Other Changes labels: - "*" onnx-onnx-bca0315/.github/workflows/000077500000000000000000000000001511334557700174645ustar00rootroot00000000000000onnx-onnx-bca0315/.github/workflows/auto_update_doc.yml000066400000000000000000000042631511334557700233530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Auto update documentation/backend test data on: pull_request_target: workflow_dispatch: permissions: # set top-level default permissions as security best practice contents: read # Check https://github.com/ossf/scorecard/blob/7ce8609469289d5f3b1bf5ee3122f42b4e3054fb/docs/checks.md#token-permissions concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: auto-update-doc: if: contains( github.event.pull_request.labels.*.name, 'auto update doc') runs-on: ubuntu-24.04 permissions: contents: write steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: repository: ${{ github.event.pull_request.head.repo.full_name }} # Checkout the branch made in the fork. Will automatically push changes # back to this branch. ref: ${{ github.event.pull_request.head.sha }} persist-credentials: true # cmd_tools contains git commands - name: Setup Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: "3.11" - name: Install ONNX from source and update documentation run: | source workflow_scripts/protobuf/build_protobuf_unix.sh 3 python -m pip install -q --upgrade pip python -m pip install -r requirements-release_build.txt git submodule update --init --recursive export ONNX_ML=1 pip install --verbose -e . python onnx/defs/gen_doc.py python onnx/gen_proto.py -l python onnx/gen_proto.py -l --ml python onnx/backend/test/stat_coverage.py python onnx/backend/test/cmd_tools.py generate-data --diff git diff -- . ':(exclude)onnx/onnx-data.proto' ':(exclude)onnx/onnx-data.proto3' - name: Commit changes with updated files uses: stefanzweifel/git-auto-commit-action@778341af668090896ca464160c2def5d1d1a3eb0 # v6.0.1 with: commit_message: CI:apply auto updated documentation/backend test data commit_options: "--signoff" onnx-onnx-bca0315/.github/workflows/check_urls.yml000066400000000000000000000033721511334557700223360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Check URLs on: push: branches: [ 'rel-*' ] schedule: # Run every month - cron: '0 0 1 * *' workflow_dispatch: permissions: # set top-level default permissions as security best practice contents: read jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: urls-checker-code uses: urlstechie/urlchecker-action@b643b43e2ac605e1475331c7b67247d242b7dce4 # v0.0.34 with: subfolder: onnx file_types: .md,.py,.rst,.ipynb,.cc,.h,.cpp print_all: false timeout: 2 retry_count : 2 exclude_urls: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/,https://media.githubusercontent.com/media/,https://download.onnxruntime.ai/onnx/models # exclude_patterns: https://... force_pass: false - name: urls-checker-docs uses: urlstechie/urlchecker-action@b643b43e2ac605e1475331c7b67247d242b7dce4 # v0.0.34 with: subfolder: docs file_types: .md,.py,.rst,.ipynb,.cc,.h,.cpp print_all: false timeout: 10 retry_count : 2 exclude_urls: https://github.com/onnx/onnx/blob/main/docs/Operators,https://github.com/onnx/onnx/pull/436,http://127.0.0.1:80,http://127.0.0.1:80/simple/ force_pass: false - name: urls-checker-community uses: urlstechie/urlchecker-action@b643b43e2ac605e1475331c7b67247d242b7dce4 # v0.0.34 with: subfolder: community file_types: .md,.py,.rst print_all: false timeout: 2 retry_count : 2 force_pass: false onnx-onnx-bca0315/.github/workflows/clang_tidy_review.yml000066400000000000000000000022631511334557700237100ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Optional_Clang_Tidy_Review on: pull_request: branches: [ "main", rel-* ] paths: - "**.cc" - "**.h" workflow_dispatch: permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 # Pleaes note that it won't cause any failure here; it will only post comments in PRs - name: clang-tidy review uses: ZedThree/clang-tidy-review@4ea7f7b72e7e039588ef5e64de9a845e5a3f8db5 # v0.21.0 with: apt_packages: "libprotobuf-dev,protobuf-compiler" cmake_command: "cmake -S . -B .setuptools-cmake-build -DONNX_USE_PROTOBUF_SHARED_LIBS=ON" build_dir: ".setuptools-cmake-build" exclude: "/third_party/*" split_workflow: true - uses: ZedThree/clang-tidy-review/upload@4ea7f7b72e7e039588ef5e64de9a845e5a3f8db5 # v0.21.0 onnx-onnx-bca0315/.github/workflows/clang_tidy_review_post.yml000066400000000000000000000013751511334557700247600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # This standalone CI is needed for commenting in PRs from forks name: Post clang-tidy review comments on: workflow_run: # The name field of the lint action workflows: ["Optional_Clang_Tidy_Review"] types: - completed permissions: checks: write pull-requests: write concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: build: runs-on: ubuntu-latest steps: - uses: ZedThree/clang-tidy-review/post@4ea7f7b72e7e039588ef5e64de9a845e5a3f8db5 # v0.21.0 with: lgtm_comment_body: "" # Use annotations instead of comments annotations: true max_comments: 10 onnx-onnx-bca0315/.github/workflows/codeql.yml000066400000000000000000000070271511334557700214640ustar00rootroot00000000000000# For most projects, this workflow file will not need changing; you simply need # to commit it to your repository. # # You may wish to alter this file to override the set of languages analyzed, # or to provide custom queries or build logic. # # ******** NOTE ******** # We have attempted to detect the languages in your repository. Please check # the `language` matrix defined below to confirm you have the correct set of # supported CodeQL languages. # name: "CodeQL" on: push: branches: [ "main", rel-* ] pull_request: # The branches below must be a subset of the branches above branches: [ "main", rel-* ] schedule: - cron: '33 6 * * 5' permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: analyze: name: Analyze runs-on: ubuntu-latest permissions: actions: read contents: read security-events: write strategy: fail-fast: false matrix: language: [ 'cpp', 'python' ] # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ] # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support steps: - name: Checkout repository uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: Set up Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: '3.11' - name: Install dependencies run: | python -m pip install --upgrade pip # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL uses: github/codeql-action/init@64d10c13136e1c5bce3e5fbde8d4906eeaafc885 # v3.29.5 with: languages: ${{ matrix.language }} queries: security-extended,security-and-quality # If you wish to specify custom queries, you can do so here or in a config file. # By default, queries listed here will override any specified in a config file. # Prefix the list here with "+" to use these queries and those in the config file. # Details on CodeQL's query packs refer to : https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs config-file: ./.github/codeql/codeql-config.yml # Install onnx so that it is found by the linters - name: Install ONNX run: | source workflow_scripts/protobuf/build_protobuf_unix.sh $(nproc) python -m pip install --quiet -r requirements-release_build.txt git submodule update --init --recursive export ONNX_ML=1 pip install . # â„šī¸ Command-line programs to run using the OS shell. # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun # If the Autobuild fails above, remove it and uncomment the following three lines. # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. # - run: | # echo "Run, Build Application using script" # ./location_of_script_within_repo/buildscript.sh - name: Perform CodeQL Analysis uses: github/codeql-action/analyze@64d10c13136e1c5bce3e5fbde8d4906eeaafc885 # v3.29.5 with: category: "/language:${{matrix.language}}" onnx-onnx-bca0315/.github/workflows/copilot-setup-steps.yml000066400000000000000000000031461511334557700241560ustar00rootroot00000000000000name: "Copilot Setup Steps" # Automatically run the setup steps when they are changed to allow for easy validation, and # allow manual testing through the repository's "Actions" tab on: workflow_dispatch: push: paths: - .github/workflows/copilot-setup-steps.yml pull_request: paths: - .github/workflows/copilot-setup-steps.yml permissions: contents: read jobs: # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot. copilot-setup-steps: runs-on: ubuntu-latest # Set the permissions to the lowest permissions possible needed for your steps. # Copilot will be given its own token for its operations. permissions: # If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete. contents: read # You can define any steps you want, and they will run before the agent starts. # If you do not check out your code, Copilot will do this for you. steps: - name: Checkout code uses: actions/checkout@v5 - name: Setup Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: "3.13" - name: Install dependencies run: | python -m pip install lintrunner>=0.10.7 python -m pip install -r requirements-release_test.txt python -m pip install -r requirements-lintrunner.txt lintrunner init onnx-onnx-bca0315/.github/workflows/create_release.yml000066400000000000000000000257101511334557700231570ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Create Releases on: schedule: # Run weekly on Monday 00:00 UTC - cron: '00 00 * * MON' push: branches: [main, rel-*] pull_request: branches: [main, rel-*] types: - labeled workflow_dispatch: inputs: publish_pypi_weekly: # only from main branch it is possible to publish to pypi-weekly (official weekly preview build) description: 'Publish to pypi-weekly' required: true type: choice options: - 'yes' - 'no' default: 'no' publish_testpypi_weekly: # only from main branch it is possible to publish to testpypi-weekly description: 'Publish to testpypi-weekly' required: true type: choice options: - 'yes' - 'no' default: 'no' publish_testpypi_release: # only from rel branch it is possible to publish to test-pypi (for rc1, rc2, etc.) description: 'Publish to testpypi-release' required: true type: choice options: - 'yes' - 'no' default: 'no' publish_pypi_release: description: 'Caution: Publish to pypi-release' required: true type: choice options: - 'yes' - 'no' default: 'no' build_mode: description: 'Specify the build mode (release or preview)' required: true type: choice options: - 'release' - 'preview' default: 'preview' permissions: contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: call-linux: if: github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'run release CIs') uses: ./.github/workflows/release_linux.yml with: os: "linux" build_mode: ${{ github.event.inputs.build_mode || 'preview' }} call-win_x86: if: github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'run release CIs') uses: ./.github/workflows/release_win_x86_64.yml with: os: "win" build_mode: ${{ github.event.inputs.build_mode || 'preview' }} call-win_arm64: if: github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'run release CIs') uses: ./.github/workflows/release_win_aarch64.yml with: os: "win_arm64" build_mode: ${{ github.event.inputs.build_mode || 'preview' }} call-mac: if: github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'run release CIs') uses: ./.github/workflows/release_mac.yml with: os: "macos" build_mode: ${{ github.event.inputs.build_mode || 'preview' }} call-sdist: if: github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'run release CIs') uses: ./.github/workflows/release_sdist.yml with: os: "macos" build_mode: ${{ github.event.inputs.build_mode || 'preview' }} check_for_publish_release_build_to_pypi: name: Check for Publish release build to pypi runs-on: ubuntu-latest needs: [call-linux, call-mac, call-win_x86, call-win_arm64, call-sdist] if: (!contains(join(needs.*.result, ' '), 'skipped')) && (github.event.inputs.publish_pypi_release == 'yes') && (github.repository_owner == 'onnx') && startsWith(github.ref, 'refs/heads/rel-') && (github.event_name == 'workflow_dispatch') steps: - name: Ensure build mode is release run: | if [ "${{ github.event.inputs.build_mode }}" != "release" ]; then echo "Error: build_mode must be set to 'release' to proceed." exit 1 fi - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false submodules: true - name: Check if package_version matches branch run: | branch_version=${GITHUB_REF#refs/heads/rel-} package_version=$(cat VERSION_NUMBER) echo "Branch version: $branch_version" echo "Package version: $package_version" if [[ "$package_version" != "$branch_version" && "$package_version" != "$branch_version"rc* ]]; then echo "Error: Package version ($package_version) does not match branch version ($branch_version) or expected RC format." exit 1 fi check_for_publish_preview_build_to_testpypi_weekly: name: Check for Publish preview build to test.pypi-weekly runs-on: ubuntu-latest needs: [call-linux, call-mac, call-win_x86, call-win_arm64, call-sdist] if: (!contains(join(needs.*.result, ' '), 'skipped')) && (github.event.inputs.publish_testpypi_weekly == 'yes') && (github.ref == 'refs/heads/main') && (github.repository_owner == 'onnx') && (github.event_name == 'workflow_dispatch') steps: - name: print debug vars run: | echo "All environment variables:" printenv publish_preview_build_to_testpypi_weekly: name: Publish preview build to test.pypi-weekly runs-on: ubuntu-latest needs: [check_for_publish_preview_build_to_testpypi_weekly] environment: name: testpypi-weekly url: https://test.pypi.org/p/onnx-weekly permissions: contents: read id-token: write steps: - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'workflow_dispatch' ) with: pattern: wheels* path: dist merge-multiple: true - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'workflow_dispatch' ) with: pattern: sdist path: dist merge-multiple: true - name: Upload preview build to test.pypi if: (github.ref == 'refs/heads/main') && (github.event.inputs.publish_testpypi_weekly == 'yes') && (github.repository_owner == 'onnx') id: upload_preview_build_to_testpypi_weekly uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e with: repository-url: https://test.pypi.org/legacy/ verbose: true print-hash: true check_for_publish_release_build_to_testpypi: name: Check for Publish release build to test.pypi (rc-candidates) runs-on: ubuntu-latest needs: [call-linux, call-mac, call-win_x86, call-win_arm64, call-sdist] if: (!contains(join(needs.*.result, ' '), 'skipped')) && (github.event.inputs.publish_testpypi_release == 'yes') && startsWith(github.ref, 'refs/heads/rel') && (github.repository_owner == 'onnx') && (github.event_name == 'workflow_dispatch') steps: - name: print debug vars run: | echo "All environment variables:" printenv publish_release_build_to_testpypi: name: Publish release build to test.pypi runs-on: ubuntu-latest needs: [check_for_publish_release_build_to_testpypi] environment: name: testpypi-release url: https://test.pypi.org/p/onnx permissions: contents: read id-token: write steps: - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'workflow_dispatch' ) with: pattern: wheels* path: dist merge-multiple: true - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'workflow_dispatch' ) with: pattern: sdist path: dist merge-multiple: true - name: Upload release build to test.pypi id: upload_release_build_to_testpypi uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e with: repository-url: https://test.pypi.org/legacy/ verbose: true print-hash: true check_for_publish_preview_build_to_pypi_weekly: name: Check for Publish preview build to pypi-weekly runs-on: ubuntu-latest needs: [call-linux, call-mac, call-win_x86, call-win_arm64, call-sdist] if: (!contains(join(needs.*.result, ' '), 'skipped')) && (github.event_name == 'schedule' || github.event.inputs.publish_pypi_weekly == 'yes') && (github.repository_owner == 'onnx') steps: - name: placeholder for debug vars run: | echo "All environment variables:" printenv publish_preview_build_to_pypi_weekly: name: Publish preview build to pypi-weekly runs-on: ubuntu-latest needs: [check_for_publish_preview_build_to_pypi_weekly] environment: name: pypi-weekly url: https://pypi.org/p/onnx-weekly permissions: contents: read id-token: write steps: - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') with: pattern: wheels* path: dist merge-multiple: true - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') with: pattern: sdist path: dist merge-multiple: true - name: Upload preview_build to pypi-weekly id: upload_preview_build_to_pypi_weekly if: (github.ref == 'refs/heads/main') uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e with: repository-url: https://upload.pypi.org/legacy/ verbose: true print-hash: true publish_release_build_to_pypi: name: Publish release build to pypi runs-on: ubuntu-latest needs: [check_for_publish_release_build_to_pypi] environment: name: pypi-release url: https://pypi.org/p/onnx permissions: contents: read id-token: write steps: - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 with: pattern: wheels* path: dist merge-multiple: true - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 with: pattern: sdist path: dist merge-multiple: true - name: Publish release_build to pypi if: (github.repository_owner == 'onnx') uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e with: repository-url: https://upload.pypi.org/legacy/ verbose: true print-hash: true test_source_dist: name: test source distribution needs: [publish_preview_build_to_pypi_weekly, publish_release_build_to_testpypi] if: (needs.publish_preview_build_to_pypi_weekly.result == 'success' || needs.publish_release_build_to_testpypi.result == 'success') uses: ./.github/workflows/preview_source_dist_test.yml with: os: "macos" onnx-onnx-bca0315/.github/workflows/dco_merge_group.yml000066400000000000000000000010561511334557700233510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: DCO on: merge_group: permissions: # set top-level default permissions as security best practice contents: read # Check https://github.com/ossf/scorecard/blob/7ce8609469289d5f3b1bf5ee3122f42b4e3054fb/docs/checks.md#token-permissions jobs: DCO: runs-on: ubuntu-latest if: ${{ github.actor != 'dependabot[bot]' }} steps: - run: echo "dummy DCO workflow (it won't run any check actually) to trigger by merge_group in order to enable merge queue" onnx-onnx-bca0315/.github/workflows/lint.yml000066400000000000000000000107321511334557700211600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Lint on: push: branches: - main pull_request: merge_group: permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: optional-lint: name: Optional Lint runs-on: ubuntu-latest steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: misspell # Check spellings as well uses: reviewdog/action-misspell@9daa94af4357dddb6fd3775de806bc0a8e98d3e4 # v1.26.3 with: github_token: ${{ secrets.github_token }} locale: "US" reporter: github-pr-check level: info filter_mode: diff_context exclude: | ./docs/docsgen/source/_static/* - name: shellcheck # Static check shell scripts uses: reviewdog/action-shellcheck@4c07458293ac342d477251099501a718ae5ef86e # v1.32.0 with: github_token: ${{ secrets.github_token }} reporter: github-pr-check level: info filter_mode: diff_context - name: cpplint # Static check C++ code uses: reviewdog/action-cpplint@b55c593522bc59ec15aa38961c5b810e1e11b7e0 # v1.10.0 with: github_token: ${{ secrets.github_token }} reporter: github-pr-check level: warning flags: --linelength=120 filter: "-runtime/references" enforce-style: name: Enforce style runs-on: ubuntu-latest permissions: security-events: write steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false submodules: recursive - name: Setup Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: "3.13" - name: Install ONNX run: | source workflow_scripts/protobuf/build_protobuf_unix.sh $(nproc) python -m pip install --quiet --upgrade pip setuptools wheel export ONNX_BUILD_TESTS=0 export ONNX_ML=1 export CMAKE_ARGS="-DONNXIFI_DUMMY_BACKEND=ON -DONNX_WERROR=ON" export ONNX_NAMESPACE=ONNX_NAMESPACE_FOO_BAR_FOR_CI python -m pip install . - name: Install dependencies run: | python -m pip install lintrunner>=0.10.7 # Use release_test to pin package versions python -m pip install -r requirements-release_test.txt python -m pip install -r requirements-lintrunner.txt lintrunner init - name: Run lintrunner on all files run: | set +e if ! lintrunner --force-color --all-files --tee-json=lint.json -v; then echo "" echo -e "\e[1m\e[36mYou can reproduce these results locally by using \`lintrunner\`.\e[0m" echo -e "\e[1m\e[36mSee https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md#coding-style for setup instructions.\e[0m" exit 1 fi - name: Produce SARIF if: always() run: | python -m lintrunner_adapters to-sarif lint.json lintrunner.sarif - name: Upload SARIF file # Use always() to always upload SARIF even if lintrunner returns with error code # To toggle linter comments in the files page, press `i` on the keyboard if: always() continue-on-error: true uses: github/codeql-action/upload-sarif@64d10c13136e1c5bce3e5fbde8d4906eeaafc885 # v3.29.5 with: # Path to SARIF file relative to the root of the repository sarif_file: lintrunner.sarif category: lintrunner checkout_path: ${{ github.workspace }} - name: Check auto-gen files are up-to-date run: | echo -e "\n::group:: ===> check auto-gen files are up-to-date..." ONNX_ML=1 python onnx/defs/gen_doc.py python onnx/gen_proto.py -l python onnx/gen_proto.py -l --ml python onnx/backend/test/stat_coverage.py git status git diff --exit-code -- . ':(exclude)onnx/onnx-data.proto' ':(exclude)onnx/onnx-data.proto3' if [ $? -ne 0 ]; then echo "git diff returned failures" exit 1 fi echo -e "::endgroup::" onnx-onnx-bca0315/.github/workflows/main.yml000066400000000000000000000237661511334557700211510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: CI env: ASAN_OPTIONS: detect_leaks=0:symbolize=1:detect_stack_use_after_return=true:strict_init_order=true:detect_odr_violation=1:detect_container_overflow=0:check_initialization_order=true:debug=true:fast_unwind_on_malloc=1:verify_asan_link_order=0 UBSAN_OPTIONS: print_stacktrace=1 on: schedule: - cron: '0 0 * * *' # every day at midnight for reporting code coverage to codecov push: branches: - main pull_request: merge_group: workflow_dispatch: permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: test: name: Test ${{ matrix.os }}, ${{ matrix.python_version }}, ${{ matrix.protobuf_type }}, debug=${{ matrix.debug_build }}, unity_build=${{ matrix.unity_build }}, onnx_ml=${{ matrix.onnx_ml }}, doc=${{ matrix.documentation }} continue-on-error: ${{ matrix.os == 'windows-11-arm' }} strategy: fail-fast: false matrix: os: [ubuntu-24.04, windows-latest, macos-latest] python_version: ['3.14', '3.13t', '3.13', '3.12', '3.11', '3.10'] include: - python_version: '3.14' onnx_ml: 1 debug_build: 1 documentation: 0 protobuf_type: 'Internal' - python_version: '3.13' onnx_ml: 1 debug_build: 1 unity_build: 1 documentation: 1 protobuf_type: 'Internal' - python_version: '3.13t' onnx_ml: 1 debug_build: 1 unity_build: 0 documentation: 0 protobuf_type: 'Internal' - python_version: '3.13' onnx_ml: 1 debug_build: 1 unity_build: 0 documentation: 1 protobuf_type: 'Internal' - python_version: '3.12' onnx_ml: 1 debug_build: 1 unity_build: 0 documentation: 1 protobuf_type: 'Internal' - python_version: '3.11' onnx_ml: 1 debug_build: 0 unity_build: 0 documentation: 0 protobuf_type: 'External' - python_version: '3.10' onnx_ml: 0 debug_build: 0 unity_build: 0 documentation: 0 protobuf_type: 'Internal' exclude: - os: windows-11-arm python_version: '3.10' - os: macos-latest python_version: '3.11' runs-on: ${{ matrix.os }} steps: - name: Checkout repository uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 with: submodules: recursive - name: Set up Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c with: python-version: ${{ matrix.python_version }} - name: Show versions run: | python --version cmake --version - name: Install external protobuf - Linux if: matrix.protobuf_type == 'External' && startsWith(matrix.os,'ubuntu') run: | sudo apt-get install libprotobuf-dev protobuf-compiler - name: Install external protobuf - MacOS if: matrix.protobuf_type == 'External' && matrix.os == 'macos-latest' run: | source workflow_scripts/protobuf/build_protobuf_unix.sh 3 $(pwd)/protobuf/protobuf_install - name: Set up MSBuild (arm64) if: startsWith(matrix.os,'windows-11-arm') uses: microsoft/setup-msbuild@6fb02220983dee41ce7ae257b6f4d8f9bf5ed4ce # v2.0.0 with: msbuild-architecture: arm64 - name: Set up MSBuild (x64) if: startsWith(matrix.os,'windows-latest') uses: microsoft/setup-msbuild@6fb02220983dee41ce7ae257b6f4d8f9bf5ed4ce # v2.0.0 with: msbuild-architecture: x64 - name: Install external protobuf - Windows if: matrix.protobuf_type == 'External' && startsWith(matrix.os, 'windows') run: | if ($matrix.os -like "windows-11-arm*") { $cmake_arch = "ARM64" } else { $cmake_arch = "x64" } workflow_scripts/protobuf/build_protobuf_win.ps1 -cmake_arch $cmake_arch shell: pwsh - name: Build and install ONNX - Linux if: startsWith(matrix.os,'ubuntu') run: | export SOURCE_DATE_EPOCH=$(git log -1 --pretty=%ct) if [ "${{ matrix.python_version }}" == "3.14" ]; then sudo apt-get install libjpeg-dev zlib1g-dev libpng-dev fi if [ "${{ matrix.protobuf_type }}" == "External" ]; then export CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DONNX_USE_PROTOBUF_SHARED_LIBS=ON" fi pip install -e ".[reference]" -v env: DEBUG: ${{ matrix.debug_build }} ONNX_ML: ${{ matrix.onnx_ml }} ONNX_BUILD_TESTS: 1 CMAKE_ARGS: "-DONNX_WERROR=ON -DONNX_USE_ASAN=${{ matrix.debug_build }} -DONNX_USE_UNITY_BUILD=${{ matrix.unity_build }}" - name: Build and install ONNX - MacOS if: matrix.os == 'macos-latest' run: | pip install -e ".[reference]" -v env: DEBUG: ${{ matrix.debug_build }} ONNX_ML: ${{ matrix.onnx_ml }} ONNX_BUILD_TESTS: 1 CMAKE_ARGS: "-DONNX_WERROR=ON -DONNX_USE_UNITY_BUILD=${{ matrix.unity_build }}" - name: Build and install ONNX - Windows if: startsWith(matrix.os,'windows') run: | pip install -e . -v env: DEBUG: ${{ matrix.debug_build }} ONNX_ML: ${{ matrix.onnx_ml }} ONNX_BUILD_TESTS: 1 CMAKE_ARGS: "-DONNX_WERROR=ON -DONNX_USE_PROTOBUF_SHARED_LIBS=OFF -DONNX_USE_LITE_PROTO=ON -DONNX_USE_UNITY_BUILD=${{ matrix.unity_build }}" - name: pip freeze run: | pip freeze - name: Setup GCC ASAN LD_PRELOAD if: startsWith(matrix.os,'ubuntu') && (matrix.python_version != '3.13t') run: | export LD_PRELOAD="$(/usr/bin/c++ -print-file-name=libasan.so):$LD_PRELOAD" - name: Install test dependencies run: | python -m pip install -r requirements-release_test.txt - name: Run Python tests run: | pytest -sv --cov=onnx --cov-report=xml --cov-append --cov-branch --junitxml junit.xml -n auto --dist loadscope - name: Run C++ tests if: startsWith(matrix.os,'ubuntu') || matrix.os == 'macos-latest' run: | export LD_LIBRARY_PATH="./.setuptools-cmake-build/:$LD_LIBRARY_PATH" ./.setuptools-cmake-build/onnx_gtests - name: Run C++ extension test if: (startsWith(matrix.os,'ubuntu') || matrix.os == 'macos-latest') && matrix.debug_build == 0 && matrix.protobuf_type == 'Internal' run: | cd ./.setuptools-cmake-build/ sudo cmake --build . --target install cd .././onnx/test/cmake mkdir build cd build cmake -DONNX_ML=${{ matrix.onnx_ml }} .. cmake --build . ./main - name: Upload coverage to Codecov if: github.repository_owner == 'onnx' uses: codecov/codecov-action@5a1091511ad55cbe89839c7260b706298ca349f7 with: token: ${{ secrets.CODECOV_TOKEN }} - name: Upload test results to Codecov if: github.repository_owner == 'onnx' && !cancelled() uses: codecov/test-results-action@v1 with: token: ${{ secrets.CODECOV_TOKEN }} # Note that the test data should be generated with numpy>=2.0. # numpy 1.x and numpy 2.0 produce slightly different numerical values. - name: Test backend test data if: matrix.documentation == 1 && startsWith(matrix.os,'ubuntu') run: | python onnx/backend/test/cmd_tools.py generate-data --clean git status git diff --exit-code -- . ':!onnx/onnx-data.proto' ':!onnx/onnx-data.proto3' ':!*output_*.pb' ':!*input_*.pb' if [ $? -ne 0 ]; then echo "git diff for test generation returned failures. Please check updated node test files" exit 1 fi git diff --exit-code --diff-filter=ADR -- . ':!onnx/onnx-data.proto' ':!onnx/onnx-data.proto3' if [ $? -ne 0 ]; then echo "Test generation returned failures. Please check the number of node test files (input_*.pb or output_*.pb)" exit 1 fi pip uninstall -y pillow python onnx/backend/test/cmd_tools.py generate-data --clean git status git diff --exit-code -- . ':!onnx/onnx-data.proto' ':!onnx/onnx-data.proto3' ':!*output_*.pb' ':!*input_*.pb' if [ $? -ne 0 ]; then echo "git diff for test generation without pillow returned failures. Please check updated node test files" exit 1 fi - name: Test documentation if: matrix.documentation == 1 run: | pip install -r docs/docsgen/source/requirements.txt cd docs/docsgen && make text continue-on-error: false - name: Run Python tests with numpy<2.0 (win, mac) # Python 3.13 support was added at numpy 2.1.0 if: (matrix.python_version == '3.11' || matrix.python_version == '3.12') && (matrix.os == 'windows-latest' || matrix.os == 'macos-latest') run: | pip install "numpy<2.0" pillow pytest -s - name: Run Python tests with numpy<2.0 (ubuntu, python<3.13) # python 3.13 support was added at numpy 2.1.0 if: (matrix.python_version == '3.11' || matrix.python_version == '3.12') && startsWith(matrix.os,'ubuntu') run: | # 2024.10.15: Error message: The headers or library files could not be found for jpeg, a required dependency when compiling Pillow from source. sudo apt-get install libjpeg-dev zlib1g-dev libpng-dev pip install --prefer-binary "numpy<2.0" pillow pytest -s onnx-onnx-bca0315/.github/workflows/manylinux/000077500000000000000000000000001511334557700215105ustar00rootroot00000000000000onnx-onnx-bca0315/.github/workflows/manylinux/entrypoint.sh000066400000000000000000000045441511334557700242660ustar00rootroot00000000000000#!/bin/bash # Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 set -e -x # CLI arguments PY_VERSION=$1 PLAT=$2 BUILD_MODE=$3 # build mode (release or preview) SOURCE_DATE_EPOCH_ARG=$4 # https://reproducible-builds.org/docs/source-date-epoch/ # Set SOURCE_DATE_EPOCH for reproducible builds if [ -n "$SOURCE_DATE_EPOCH_ARG" ]; then export SOURCE_DATE_EPOCH=$SOURCE_DATE_EPOCH_ARG fi echo "SOURCE_DATE_EPOCH: $SOURCE_DATE_EPOCH" echo "Python version: $PY_VERSION" echo "Platform: $PLAT" echo "Build mode: $BUILD_MODE" export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib declare -A python_map=(["3.10"]="cp310-cp310" ["3.11"]="cp311-cp311" ["3.12"]="cp312-cp312" ["3.13"]="cp313-cp313" ["3.13t"]="cp313-cp313t" ["3.14-dev"]="cp314-cp314") PY_VER=${python_map[$PY_VERSION]} PIP_INSTALL_COMMAND="/opt/python/${PY_VER}/bin/pip install --only-binary google-re2 --no-cache-dir -q" PYTHON_COMMAND="/opt/python/${PY_VER}/bin/python" # Update pip $PIP_INSTALL_COMMAND --upgrade pip $PIP_INSTALL_COMMAND cmake # Build protobuf from source yum install -y wget source workflow_scripts/protobuf/build_protobuf_unix.sh "$(nproc)" "$(pwd)"/protobuf/protobuf_install # set ONNX build environments export ONNX_ML=1 export CMAKE_ARGS="-DONNX_USE_LITE_PROTO=ON" $PIP_INSTALL_COMMAND -v -r requirements-release_build.txt || { echo "Installing Python requirements failed."; exit 1; } if [ "$BUILD_MODE" != "release" ]; then echo "Building preview wheels..." sed -i 's/name = "onnx"/name = "onnx-weekly"/' 'pyproject.toml' export ONNX_PREVIEW_BUILD=1 else echo "Building release wheels..." fi # Build the wheels if ! $PYTHON_COMMAND -m build --wheel; then echo "Building wheels failed." exit 1 fi # Bundle external shared libraries into the wheels # find -exec does not preserve failed exit codes, so use an output file for failures failed_wheels=$PWD/failed-wheels rm -f "$failed_wheels" find . -type f -iname "*-linux*.whl" -exec sh -c "auditwheel repair '{}' -w \$(dirname '{}') --plat '${PLAT}' || { echo 'Repairing wheels failed.'; auditwheel show '{}' >> '$failed_wheels'; }" \; if [[ -f "$failed_wheels" ]]; then echo "Repairing wheels failed:" cat failed-wheels exit 1 fi # Remove useless *-linux*.whl; only keep manylinux*.whl rm -f dist/*-linux*.whl echo "Successfully build wheels:" find . -type f -iname "*manylinux*.whl" onnx-onnx-bca0315/.github/workflows/pages.yml000066400000000000000000000042101511334557700213030ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Generate and publish ONNX docs on: pull_request: branches: ["main"] # Runs on pushes targeting the default branch push: branches: ["main"] # Allows you to run this workflow manually from the Actions tab workflow_dispatch: # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages permissions: contents: read pages: write id-token: write jobs: build: runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: Setup Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: '3.10' - name: Install Dependencies run: | python -m pip install --quiet --upgrade pip setuptools wheel python -m pip install -r docs/docsgen/source/requirements.txt python -m pip install protobuf - name: Uninstall onnx run: python -m pip uninstall -y onnx - name: Install onnx development version run: | sudo apt-get remove libprotobuf-dev protobuf-compiler git submodule update --init --recursive export CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_WERROR=ON" export ONNX_ML=1 pip install . - name: Build Docs run: | cd docs/docsgen make html - name: Upload artifact uses: actions/upload-pages-artifact@7b1f4a764d45c48632c6b24a0339c27f5614fb0b # v4.0.0 with: path: 'docs/docsgen/build/html' deploy: needs: 'build' if: github.event_name != 'pull_request' environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} runs-on: ubuntu-latest steps: - name: Setup Pages uses: actions/configure-pages@983d7736d9b0ae728b81ab479565c72886d7745b # v5.0.0 - name: Deploy to GitHub Pages id: deployment uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4.0.5 onnx-onnx-bca0315/.github/workflows/pixi_build.yml000066400000000000000000000043161511334557700223430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Pixi CI on: schedule: # run weekly on Sunday 23:59 - cron: '29 23 * * SUN' push: paths: - '.github/workflows/pixi_build.yml' - 'CMakeLists.txt' - 'pixi.lock' - 'pixi.toml' permissions: contents: read issues: write # Needed to create an issue on failure concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: false jobs: install-lint: name: Install and test (${{ matrix.os }}, ${{ matrix.environment }}) runs-on: ${{ matrix.os }} strategy: fail-fast: true matrix: os: - ubuntu-latest - macos-latest - windows-latest environment: - default - oldies steps: - name: Checkout branch uses: actions/checkout@v5 with: submodules: recursive fetch-depth: 0 - name: Set up pixi uses: prefix-dev/setup-pixi@v0.9.1 with: environments: ${{ matrix.environment }} - name: Install repository run: pixi run -e ${{ matrix.environment }} install - name: gtests run: pixi run -e ${{ matrix.environment }} gtest - name: pytest run: pixi run -e ${{ matrix.environment }} pytest - name: Issue on failure uses: actions/github-script@v8 if: ${{ failure() && github.ref == 'refs/heads/main' }} with: script: | github.rest.issues.listForRepo({ owner: context.repo.owner, repo: context.repo.repo, state: "open", labels: "[bot] pixi CI" }).then((issues) => { if (issues.data.length === 0){ github.rest.issues.create({ owner: context.repo.owner, repo: context.repo.repo, title: "Scheduled pixi CI failed", body: "The scheduled pixi-based CI failed. See https://github.com/${{ github.repository }}/actions/runs/${{github.run_id}} for details.", assignees: ["cbourjau"], labels: ["[bot] pixi CI"] }) } }); onnx-onnx-bca0315/.github/workflows/pr_checks.yml000066400000000000000000000023031511334557700221460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: PR Checks on: pull_request: branches: - main permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: auto-apply-fixes: name: Suggest fixes runs-on: ubuntu-latest permissions: contents: read pull-requests: write steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: Setup Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: "3.10" - name: Install dependencies run: | python -m pip install -r requirements-dev.txt lintrunner init - name: Run lintrunner on all files run: | set +e lintrunner f --all-files -v exit 0 - uses: parkerbxyz/suggest-changes@v3 with: comment: 'You can commit the suggested changes from lintrunner.' onnx-onnx-bca0315/.github/workflows/preview_source_dist_test.yml000066400000000000000000000036141511334557700253360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Test source dist of preview build at onnx-weekly on: # Specifies the event triggering the workflow schedule: # Run weekly on Tuesday 00:00 UTC - cron: '00 00 * * 2' workflow_call: # Indicates that this is a reusable workflow inputs: os: required: true type: string workflow_dispatch: inputs: os: required: true type: string permissions: contents: read jobs: test_sdist_preview: strategy: matrix: os: [ubuntu-24.04, ubuntu-22.04, windows-latest] python-version: ['3.10','3.12'] target-architecture: ['arm64', 'x86_64'] fail-fast: false runs-on: ${{ matrix.os }} steps: - name: Checkout repository uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 - name: Test preview build source distribution from PyPI if: (github.event_name == 'workflow_dispatch' || github.event_name == 'schedule') run: | python -m pip uninstall -y onnx-weekly python -m pip install --upgrade pip python -m pip install setuptools python -m pip install --use-deprecated=legacy-resolver --no-binary onnx-weekly onnx-weekly python -m pip install pytest ml_dtypes pillow parameterized google-re2 pytest - name: Test preview build source distribution from test.PyPI if: (github.event_name == 'workflow_dispatch' || github.event_name == 'schedule') run: | python -m pip uninstall -y onnx-weekly python -m pip install setuptools python -m pip install -i https://test.pypi.org/pypi/ --use-deprecated=legacy-resolver --no-binary onnx-weekly onnx-weekly python -m pip install pytest ml_dtypes pillow parameterized google-re2 pytest onnx-onnx-bca0315/.github/workflows/release_linux.yml000066400000000000000000000157531511334557700230610ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: LinuxRelease on: workflow_call: inputs: os: required: true type: string build_mode: required: true type: string permissions: contents: read jobs: build: if: github.event_name != 'pull_request' || startsWith( github.base_ref, 'rel-') || contains( github.event.pull_request.labels.*.name, 'run release CIs') strategy: matrix: python-version: ['3.13t', '3.12', '3.11', '3.10'] architecture: ['x64', 'arm64'] fail-fast: false env: MANYLINUX_WHEEL_X64: "manylinux_2_28_x86_64" MANYLINUX_WHEEL_ARM64: "manylinux_2_28_aarch64" runs-on: ${{ matrix.architecture == 'x64' && 'ubuntu-24.04' || 'ubuntu-24.04-arm' }} steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: true submodules: true - name: Configure Git safe directory run: | git config --global --add safe.directory /github/workspace - name: Set source date epoch variable run: | echo "SOURCE_DATE_EPOCH=$(git log -1 --pretty=%ct)" >> $GITHUB_ENV - name: Build wheel for x86_64 if: matrix.architecture == 'x64' id: build_wheel_x86 uses: docker://quay.io/pypa/manylinux_2_28_x86_64:2025.10.10-1 with: entrypoint: bash args: .github/workflows/manylinux/entrypoint.sh ${{ matrix.python-version }} manylinux_2_28_x86_64 ${{ inputs.build_mode }} ${{ env.SOURCE_DATE_EPOCH }} - name: Build wheel for arm64 if: matrix.architecture == 'arm64' id: build_wheel_arm64 uses: docker://quay.io/pypa/manylinux_2_28_aarch64:2025.10.10-1 with: entrypoint: bash args: .github/workflows/manylinux/entrypoint.sh ${{ matrix.python-version }} manylinux_2_28_aarch64 ${{ inputs.build_mode }} ${{ env.SOURCE_DATE_EPOCH }} - name: Debug Python version and artifact name run: | echo "PYTHON VERSION: ${{ matrix.python-version }}" if [ "${{ matrix.python-version }}" = "3.12" ]; then echo "Artifact name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-3.12-abi3" else echo "Artifact name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-${{ matrix.python-version }}" fi - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: (steps.build_wheel_arm64.outcome == 'success' || steps.build_wheel_x86.outcome == 'success') && (inputs.build_mode == 'preview' || !contains(matrix.python-version, 'dev')) && (matrix.python-version == '3.12') with: name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-3.12-abi3 path: | ./dist/*.whl - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: (steps.build_wheel_arm64.outcome == 'success' || steps.build_wheel_x86.outcome == 'success') && (inputs.build_mode == 'preview' || !contains(matrix.python-version, 'dev')) && (matrix.python-version != '3.12') with: name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-${{ matrix.python-version }} path: | ./dist/*.whl test: needs: build runs-on: ${{ matrix.architecture == 'x64' && 'ubuntu-24.04' || 'ubuntu-24.04-arm' }} env: MANYLINUX_WHEEL_X64: "manylinux_2_28_x86_64" MANYLINUX_WHEEL_ARM64: "manylinux_2_28_aarch64" strategy: matrix: python-version: ['3.10', '3.11', '3.12', '3.13', '3.13t', '3.14-dev'] architecture: ['x64', 'arm64'] fail-fast: false steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: true submodules: true - uses: actions/download-artifact@v5 if: matrix.python-version == '3.12' || matrix.python-version == '3.13' || matrix.python-version == '3.14-dev' with: name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-3.12-abi3 path: ./dist - uses: actions/download-artifact@v5 if: matrix.python-version != '3.12' && matrix.python-version != '3.13' && matrix.python-version != '3.14-dev' with: name: wheels-${{ inputs.os }}-${{ matrix.architecture }}-${{ matrix.python-version }} path: ./dist - uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} architecture: ${{ matrix.architecture }} - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} architecture: ${{ matrix.architecture }} - name: Install Python dependencies run: | python -m pip install -q --upgrade pip python -m pip install -q -r requirements-release_test.txt - name: Check abi3 compatibility with abi3audit run: | if [ $(ls dist/*.whl | wc -l) -eq 1 ]; then echo "Exactly one wheel file found." else echo "Multiple or no wheel files found." fi for whl in dist/*${{ matrix.architecture == 'x64' && env.MANYLINUX_WHEEL_X64 || env.MANYLINUX_WHEEL_ARM64 }}*.whl; do echo "Checking abi3 compatibility for $whl" python -m abi3audit -v "$whl" done - name: Install protobuf in the GitHub Action environment for testing the wheel run: | source workflow_scripts/protobuf/build_protobuf_unix.sh $(nproc) - name: Test wheel with Python ${{ matrix.python-version }} if: matrix.python-version != '3.14-dev' run: | # example file name: ./dist/onnx_weekly-1.19.0.dev20250528-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl python -m pip install dist/*${{ matrix.architecture == 'x64' && env.MANYLINUX_WHEEL_X64 || env.MANYLINUX_WHEEL_ARM64 }}*.whl pytest - name: Verify ONNX with the latest numpy if: matrix.python-version != '3.14-dev' run: | python -m pip uninstall -y numpy onnx && python -m pip install numpy python -m pip install dist/*${{ matrix.architecture == 'x64' && env.MANYLINUX_WHEEL_X64 || env.MANYLINUX_WHEEL_ARM64 }}*whl pytest - name: Verify ONNX with the latest protobuf if: matrix.python-version != '3.14-dev' run: | python -m pip uninstall -y protobuf onnx && python -m pip install protobuf python -m pip install dist/*${{ matrix.architecture == 'x64' && env.MANYLINUX_WHEEL_X64 || env.MANYLINUX_WHEEL_ARM64 }}*whl pytest - name: Verify ONNX with the minimumly supported packages if: matrix.python-version != '3.14-dev' run: | python -m pip uninstall -y numpy protobuf onnx && python -m pip install -r requirements-min.txt python -m pip install dist/*${{ matrix.architecture == 'x64' && env.MANYLINUX_WHEEL_X64 || env.MANYLINUX_WHEEL_ARM64 }}*whl pytest onnx-onnx-bca0315/.github/workflows/release_mac.yml000066400000000000000000000130061511334557700224470ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: MacRelease on: workflow_call: inputs: os: required: true type: string build_mode: required: true type: string env: MACOSX_DEPLOYMENT_TARGET: "12.0" permissions: contents: read jobs: build: if: github.event_name != 'pull_request' || startsWith( github.base_ref, 'rel-') || contains( github.event.pull_request.labels.*.name, 'run release CIs') runs-on: macos-14 strategy: matrix: python-version: ['3.14', '3.13t', '3.12', '3.11', '3.10'] fail-fast: true steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false submodules: true - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} - name: Install Python dependencies run: | python -m pip install -q --upgrade pip python -m pip install -q -r requirements-release_build.txt - name: Build wheel id: build_wheel env: CC: "clang" CXX: "clang++" ONNX_ML: 1 CMAKE_OSX_ARCHITECTURES: "arm64;x86_64" CMAKE_ARGS: "-DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON" run: | # Install Protobuf from source export NUM_CORES=`sysctl -n hw.logicalcpu` source workflow_scripts/protobuf/build_protobuf_unix.sh $NUM_CORES $(pwd)/protobuf/protobuf_install if [ '${{ inputs.build_mode }}' != 'release' ]; then sed -i '' 's/name = "onnx"/name = "onnx-weekly"/' 'pyproject.toml' export ONNX_PREVIEW_BUILD=1 fi python -m build --wheel - name: Debug Python version and artifact name run: | echo "PYTHON VERSION: ${{ matrix.python-version }}" if [ "${{ matrix.python-version }}" = "3.12" ] || [ "${{ matrix.python-version }}" = "3.14" ]; then echo "Artifact name: wheels-${{ inputs.os }}-3.12-abi3" else echo "Artifact name: wheels-${{ inputs.os }}-${{ matrix.python-version }}" fi - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: steps.build_wheel.outcome == 'success' && (matrix.python-version == '3.12') with: name: wheels-${{ inputs.os }}-3.12-abi3 path: dist/*.whl - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: steps.build_wheel.outcome == 'success' && (matrix.python-version == '3.10' || matrix.python-version == '3.11' || matrix.python-version == '3.13t') with: name: wheels-${{ inputs.os }}-${{ matrix.python-version }} path: dist/*.whl test: needs: build runs-on: ${{ (matrix.target-architecture == 'x86_64') && 'macos-15' || 'macos-14' }} continue-on-error: true strategy: matrix: python-version: ['3.14', '3.13t', '3.13', '3.12', '3.11', '3.10'] target-architecture: ['x86_64', 'arm64'] fail-fast: false steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} - name: Install Python dependencies run: | arch -${{ matrix.target-architecture }} python -m pip install -q --upgrade pip arch -${{ matrix.target-architecture }} python -m pip install -q -r requirements-release_test.txt - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: matrix.python-version == '3.12' || matrix.python-version == '3.13' || matrix.python-version == '3.14' with: name: wheels-${{ inputs.os }}-3.12-abi3 path: dist - uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 if: matrix.python-version == '3.10' || matrix.python-version == '3.11' || matrix.python-version == '3.13t' with: name: wheels-${{ inputs.os }}-${{ matrix.python-version }} path: dist - name: Test the wheel run: | arch -${{ matrix.target-architecture }} python -m pip install --upgrade dist/*.whl arch -${{ matrix.target-architecture }} pytest - name: Verify ONNX with the latest numpy run: | arch -${{ matrix.target-architecture }} python -m pip uninstall -y numpy onnx arch -${{ matrix.target-architecture }} python -m pip install numpy arch -${{ matrix.target-architecture }} python -m pip install --upgrade dist/*.whl arch -${{ matrix.target-architecture }} pytest - name: Verify ONNX with the latest protobuf run: | arch -${{ matrix.target-architecture }} python -m pip uninstall -y protobuf onnx arch -${{ matrix.target-architecture }} python -m pip install protobuf arch -${{ matrix.target-architecture }} python -m pip install --upgrade dist/*.whl arch -${{ matrix.target-architecture }} pytest - name: Verify ONNX with the minimumly supported packages run: | arch -${{ matrix.target-architecture }} python -m pip uninstall -y numpy protobuf onnx arch -${{ matrix.target-architecture }} python -m pip install -r requirements-min.txt arch -${{ matrix.target-architecture }} python -m pip install --upgrade dist/*.whl arch -${{ matrix.target-architecture }} pytest onnx-onnx-bca0315/.github/workflows/release_sdist.yml000066400000000000000000000035001511334557700230330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: sdistRelease on: workflow_call: inputs: os: required: true type: string build_mode: required: true type: string permissions: contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }}-sdist jobs: build: if: github.event_name != 'pull_request' || startsWith( github.base_ref, 'rel-') || contains( github.event.pull_request.labels.*.name, 'run release CIs') runs-on: ubuntu-24.04 strategy: matrix: python-version: ['3.10'] target-architecture: ['arm64'] steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false submodules: true - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} - name: Install Python dependencies run: | python -m pip install -q --upgrade pip python -m pip install -q -r requirements-release_build.txt - name: Build source distribution (preview build / weekly) if: ${{ inputs.build_mode != 'release' }} run: | git clean -xdf sed -i 's/name = "onnx"/name = "onnx-weekly"/' 'pyproject.toml' ONNX_PREVIEW_BUILD=1 python -m build --sdist - name: Build source distribution (for release) if: ${{ inputs.build_mode == 'release' }} run: | git clean -xdf python -m build --sdist - name: Upload sdist uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 with: name: sdist path: | ./dist/*.tar.gz onnx-onnx-bca0315/.github/workflows/release_win_aarch64.yml000066400000000000000000000057111511334557700240200ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: WindowsRelease_aarch64 on: workflow_call: inputs: os: required: true type: string build_mode: required: true type: string permissions: contents: read jobs: build: if: github.event_name != 'pull_request' || startsWith(github.base_ref, 'rel-') || contains(github.event.pull_request.labels.*.name, 'run release CIs') runs-on: windows-11-arm strategy: matrix: python-version: ['3.13t', '3.12', '3.11'] architecture: ['arm64'] steps: - name: Checkout ONNX uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: submodules: 'recursive' persist-credentials: false - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} architecture: ${{ matrix.architecture }} - name: Add msbuild to PATH uses: microsoft/setup-msbuild@6fb02220983dee41ce7ae257b6f4d8f9bf5ed4ce # v2.0.0 with: msbuild-architecture: ${{ matrix.architecture }} - name: Install Python dependencies run: | python -m pip install -q --upgrade pip python -m pip install -r requirements-release_build.txt - name: Build ONNX wheel id: build_wheel run: | $cmake_arch = 'ARM64' .\workflow_scripts\protobuf\build_protobuf_win.ps1 -cmake_arch $cmake_arch echo "Install ONNX" $Env:ONNX_ML=1 $Env:CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=OFF -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON" if ( '${{ inputs.build_mode }}' -ne 'release') { echo "Build preview build whl package" (Get-Content -Path 'pyproject.toml') | ForEach-Object { $_ -replace 'name = "onnx"', 'name = "onnx-weekly"' } | Set-Content -Path 'pyproject.toml' $Env:ONNX_PREVIEW_BUILD=1 } python -m build --wheel Get-ChildItem -Path dist/*.whl | foreach {python -m pip install --upgrade $_.fullname --no-deps} - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: (steps.build_wheel.outcome == 'success') && (inputs.build_mode == 'preview' || !contains(matrix.python-version, 'dev')) with: name: wheels-${{ inputs.os }}-${{ matrix.python-version }}-${{matrix.architecture}} path: ./dist - name: Check abi3 compatibility with abi3audit if: steps.build_wheel.outcome == 'success' run: | if [ $(ls dist/*.whl | wc -l) -eq 1 ]; then echo "Exactly one wheel file found." else echo "Multiple or no wheel files found." fi for whl in dist/*.whl; do echo "Checking abi3 compatibility for $whl" python -m pip install -q abi3audit python -m abi3audit -v "$whl" done shell: bash onnx-onnx-bca0315/.github/workflows/release_win_x86_64.yml000066400000000000000000000123501511334557700235230ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: WindowsRelease_x86_64 on: workflow_call: inputs: os: required: true type: string build_mode: required: true type: string permissions: contents: read jobs: build-and-test: if: github.event_name != 'pull_request' || startsWith( github.base_ref, 'rel-') || contains( github.event.pull_request.labels.*.name, 'run release CIs') runs-on: windows-2022 strategy: fail-fast: false matrix: python-version: ['3.13t', '3.12', '3.11', '3.10'] architecture: ['x64', 'x86'] exclude: - python-version: '3.13t' architecture: 'x86' - python-version: '3.14-dev' architecture: 'x86' steps: - name: Checkout ONNX uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: submodules: 'recursive' persist-credentials: false - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} architecture: ${{ matrix.architecture }} - name: Add msbuild to PATH uses: microsoft/setup-msbuild@6fb02220983dee41ce7ae257b6f4d8f9bf5ed4ce # v2.0.0 with: msbuild-architecture: ${{ matrix.architecture }} - name: Install Python dependencies run: | python -m pip install -q --upgrade pip if ('${{ matrix.architecture }}' -eq 'x86') { echo "Skip installing dependencies for reference, because they don't have prebuilt wheel on x86" sed -i '' '/-r requirements-reference.txt/d' requirements-release_build.txt } python -m pip install -q -r requirements-release_build.txt python -m pip install cmake - name: Build ONNX wheel id: build_wheel run: | $cmake_arch = 'x64' if ('${{ matrix.architecture }}' -eq 'x86') { $cmake_arch = 'Win32' } . .\workflow_scripts\protobuf\build_protobuf_win.ps1 -cmake_arch $cmake_arch echo "Install ONNX" $Env:ONNX_ML=1 $Env:CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=OFF -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON" if ( '${{ inputs.build_mode }}' -ne 'release') { echo "Build preview build whl package" (Get-Content -Path 'pyproject.toml') | ForEach-Object { $_ -replace 'name = "onnx"', 'name = "onnx-weekly"' } | Set-Content -Path 'pyproject.toml' $Env:ONNX_PREVIEW_BUILD=1 } python -m build --wheel Get-ChildItem -Path dist/*.whl | foreach {python -m pip install --upgrade $_.fullname} - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 if: steps.build_wheel.outcome == 'success' && (inputs.build_mode == 'preview' || !contains(matrix.python-version, 'dev')) # dev-builds should not be uploaded when release-builds are created with: name: wheels-${{ inputs.os }}-${{ matrix.python-version }}-${{matrix.architecture}} path: ./dist - name: Check abi3 compatibility with abi3audit if: steps.build_wheel.outcome == 'success' run: | if [ $(ls dist/*.whl | wc -l) -eq 1 ]; then echo "Exactly one wheel file found." else echo "Multiple or no wheel files found." fi for whl in dist/*.whl; do echo "Checking abi3 compatibility for $whl" python -m pip install -q abi3audit python -m abi3audit -v "$whl" done shell: bash - name: Test the installed wheel if: steps.build_wheel.outcome == 'success' && matrix.python-version != '3.13t' && matrix.python-version != '3.14-dev' # TODO: reevaluate 3.13t/3.14 for onnx 1.20 (3.13t was already working, but it's failing since a github runner update...) run: | python -m pip install -q -r requirements-release_test.txt pytest - name: Verify ONNX with the latest numpy if: steps.build_wheel.outcome == 'success' && matrix.python-version != '3.13t' && matrix.python-version != '3.14-dev' # TODO: reevaluate 3.13t/3.14 for onnx 1.20 (3.13t was already working, but it's failing since a github runner update...) run: | python -m pip uninstall -y numpy onnx python -m pip install numpy Get-ChildItem -Path dist/*.whl | foreach {python -m pip install --upgrade $_.fullname} pytest - name: Verify ONNX with the latest protobuf if: steps.build_wheel.outcome == 'success' && matrix.python-version != '3.13t' && matrix.python-version != '3.14-dev' run: | python -m pip uninstall -y protobuf onnx python -m pip install protobuf Get-ChildItem -Path dist/*.whl | foreach {python -m pip install --upgrade $_.fullname} pytest - name: Verify ONNX with the minimumly supported packages if: steps.build_wheel.outcome == 'success' && matrix.python-version != '3.13t' && matrix.python-version != '3.14-dev' run: | python -m pip uninstall -y protobuf numpy onnx python -m pip install -r requirements-min.txt Get-ChildItem -Path dist/*.whl | foreach {python -m pip install --upgrade $_.fullname} pytest onnx-onnx-bca0315/.github/workflows/reuse.yml000066400000000000000000000010571511334557700213350ustar00rootroot00000000000000# SPDX-FileCopyrightText: 2022 Free Software Foundation Europe e.V. # # SPDX-License-Identifier: CC0-1.0 name: REUSE Compliance Check on: [push, pull_request] permissions: # set top-level default permissions as security best practice contents: read jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: REUSE Compliance Check uses: fsfe/reuse-action@bb774aa972c2a89ff34781233d275075cbddf542 onnx-onnx-bca0315/.github/workflows/scorecard.yml000066400000000000000000000057551511334557700221700ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # This workflow uses actions that are not certified by GitHub. They are provided # by a third-party and are governed by separate terms of service, privacy # policy, and support documentation. name: Scorecard supply-chain security on: # For Branch-Protection check. Only the default branch is supported. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection branch_protection_rule: # To guarantee Maintained check is occasionally updated. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained schedule: - cron: '24 10 * * 6' push: branches: [ "main" ] # Declare default permissions as read only. permissions: read-all jobs: analysis: name: Scorecard analysis runs-on: ubuntu-latest permissions: # Needed to upload the results to code-scanning dashboard. security-events: write # Needed to publish results and get a badge (see publish_results below). id-token: write # Uncomment the permissions below if installing in a private repository. # contents: read # actions: read steps: - name: "Checkout code" uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v3.1.0 with: persist-credentials: false - name: "Run analysis" uses: ossf/scorecard-action@4eaacf0543bb3f2c246792bd56e8cdeffafb205a # v2.4.3 with: results_file: results.sarif results_format: sarif # (Optional) "write" PAT token. Uncomment the `repo_token` line below if: # - you want to enable the Branch-Protection check on a *public* repository, or # - you are installing Scorecard on a *private* repository # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action#authentication-with-pat. # repo_token: ${{ secrets.SCORECARD_TOKEN }} # Public repositories: # - Publish results to OpenSSF REST API for easy access by consumers # - Allows the repository to include the Scorecard badge. # - See https://github.com/ossf/scorecard-action#publishing-results. # For private repositories: # - `publish_results` will always be set to `false`, regardless # of the value entered here. publish_results: true # Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF # format to the repository Actions tab. - name: "Upload artifact" uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 with: name: SARIF file path: results.sarif retention-days: 5 # Upload the results to GitHub's code scanning dashboard. - name: "Upload to code-scanning" uses: github/codeql-action/upload-sarif@64d10c13136e1c5bce3e5fbde8d4906eeaafc885 # v3.29.5 with: sarif_file: results.sarif onnx-onnx-bca0315/.github/workflows/stale.yml000066400000000000000000000020401511334557700213130ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time. # # You can adjust the behavior by modifying this file. # For more information, see: # https://github.com/actions/stale name: Mark stale issues and pull requests on: schedule: - cron: '39 6 * * *' permissions: # set top-level default permissions as security best practice contents: read jobs: stale: runs-on: ubuntu-latest permissions: issues: write pull-requests: write steps: - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10.1.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} days-before-stale: 365 days-before-close: 21 ascending: true exempt-issue-labels: bug,no-stale exempt-pr-labels: no-stale,contributions welcome remove-issue-stale-when-updated: true remove-pr-stale-when-updated: true exempt-all-milestones: true onnx-onnx-bca0315/.github/workflows/weekly_modelzoo.yml000066400000000000000000000033541511334557700234240ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # Runs model checker/shape_inference/version_converter for all models name: Weekly CI with the latest ONNX and ONNX Model Zoo on: schedule: # run weekly on Sunday 23:59 - cron: '59 23 * * SUN' pull_request: branches: [main, rel-*] workflow_dispatch: permissions: contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: build: if: github.event_name != 'pull_request' || contains( github.event.pull_request.labels.*.name, 'test ONNX Model Zoo') runs-on: ubuntu-latest steps: - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 name: Checkout repo with: persist-credentials: false submodules: recursive - name: Set up Python uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: '3.12' - name: Install dependencies shell: bash run: | set -e python -m pip install -q --upgrade pip python -m pip install -q -r requirements-release_build.txt - name: Build and install ONNX shell: bash run: | # Install protobuf from source export NUM_CORES=`sysctl -n hw.logicalcpu` source workflow_scripts/protobuf/build_protobuf_unix.sh $NUM_CORES $(pwd)/protobuf/protobuf_install # Build ONNX export CC=clang export CXX=clang++ export ONNX_ML=1 pip install -e . -v - name: Test all models with onnx.checker, onnx.shape_inference, onnx.version_converter run: | python workflow_scripts/test_model_zoo.py onnx-onnx-bca0315/.github/workflows/win_no_exception_ci.yml000066400000000000000000000054401511334557700242340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: Windows_No_Exception_CI on: push: branches: [ main, rel-* ] pull_request: branches: [ main, rel-* ] permissions: # set top-level default permissions as security best practice contents: read concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name == 'workflow_dispatch' }} cancel-in-progress: true jobs: build: runs-on: windows-latest strategy: matrix: python-version: ['3.10', '3.11', '3.12', '3.13', '3.14'] architecture: ['x64'] steps: - name: Checkout ONNX uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: path: ./onnx submodules: 'recursive' persist-credentials: false - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: ${{ matrix.python-version }} architecture: ${{ matrix.architecture }} - name: Add msbuild to PATH uses: microsoft/setup-msbuild@6fb02220983dee41ce7ae257b6f4d8f9bf5ed4ce # v2.0.0 with: msbuild-architecture: ${{ matrix.architecture }} - name: Install dependencies run: | python -m pip install --upgrade pip python -m pip install cmake - name: Build and test ONNX binaries run: | . .\onnx\workflow_scripts\protobuf\build_protobuf_win.ps1 -cmake_arch ${{ matrix.architecture }} cd onnx echo "Build ONNX" cmake -G "Visual Studio 17 2022" -A ${{ matrix.architecture }} -DONNX_USE_PROTOBUF_SHARED_LIBS=OFF -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON -DONNX_DISABLE_EXCEPTIONS=ON -DCMAKE_BUILD_TYPE=Release -DONNX_USE_MSVC_STATIC_RUNTIME=OFF -DONNX_ML=1 -DONNX_BUILD_TESTS=ON -S . -B .setuptools-cmake-build\ cd .setuptools-cmake-build\ cmake --build . --config Release echo "Run gtests" Release\onnx_gtests.exe if($lastexitcode -ne 0) { EXIT 1 } cd .. git clean -xdf set ONNX_BUILD_TESTS=1 echo "Build ONNX with non-static registration for testing selective ONNX schema loading" cmake -G "Visual Studio 17 2022" -A ${{ matrix.architecture }} -DONNX_USE_PROTOBUF_SHARED_LIBS=OFF -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON -DCMAKE_BUILD_TYPE=Release -DONNX_USE_MSVC_STATIC_RUNTIME=OFF -DONNX_ML=1 -DONNX_BUILD_TESTS=ON -DONNX_DISABLE_STATIC_REGISTRATION=ON -S . -B .setuptools-cmake-build\ cd .setuptools-cmake-build\ cmake --build . --config Release echo "Only test selective ONNX schema loading" Release\onnx_gtests.exe --gtest_filter="SchemaRegistrationTest*" if($lastexitcode -ne 0) { EXIT 1 } onnx-onnx-bca0315/.github/workflows/zizmor.yml000066400000000000000000000013211511334557700215360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 name: GitHub Actions Security Analysis with zizmor 🌈 on: push: branches: ["main"] pull_request: branches: ["**"] permissions: {} jobs: zizmor: runs-on: ubuntu-latest permissions: security-events: write contents: read # only needed for private repos actions: read # only needed for private repos steps: - name: Checkout repository uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 with: persist-credentials: false - name: Run zizmor 🌈 uses: zizmorcore/zizmor-action@e673c3917a1aef3c65c972347ed84ccd013ecda4 # v0.2.0 onnx-onnx-bca0315/.gitignore000066400000000000000000000025231511334557700160610ustar00rootroot00000000000000## General # Compiled Object files *.slo *.lo *.o *.cuo # Compiled Dynamic libraries *.so *.dylib *.pyd # Compiled Static libraries *.lai *.la *.a # Compiled protocol buffers *.pb.h *.pb.cc onnx/*_pb2.py onnx/*_pb.py onnx/*_pb2.pyi onnx/*_pb.pyi # Compiled python *.pyc # Compiled MATLAB *.mex* # IPython notebook checkpoints .ipynb_checkpoints # Editor temporaries *.swn *.swo *.swp *~ # Sublime Text settings *.sublime-workspace *.sublime-project # Eclipse Project settings *.*project .settings # QtCreator files *.user # PyCharm files .idea # Visual Studio Code files .vscode !/.vscode/settings.json # OSX dir files .DS_Store ## ONNX # build, distribute, and bins (+ python proto bindings) build build_* .build_debug/* .build_release/* .setuptools-cmake-build/* # setup.py intermediates .eggs dist onnx.egg-info *.ninja .ninja_deps .ninja_log compile_commands.json # generated files onnx/version.py compile_commands.json # test generated files .cache .coverage onnx/examples/.coverage.nbval .pytest_cache test_report test-output.xml # autocomplete .ycm_extra_conf.py # test coverage data files *.gcov .mypy_cache virtualenv venv # direnv, posh-direnv .envrc .psenvrc # documentation docs/docsgen/source/onnx-api/modules/ docs/docsgen/source/operators/ docs/docsgen/**/*.onnx docs/docsgen/**/*.pb # PyEnv files .python-version .pixi/** onnx-onnx-bca0315/.gitmodules000066400000000000000000000000001511334557700162320ustar00rootroot00000000000000onnx-onnx-bca0315/.lintrunner.toml000066400000000000000000000103171511334557700172440ustar00rootroot00000000000000# Configuration for lintrunner https://github.com/suo/lintrunner # You can install the dependencies and initialize with # # ```sh # pip install lintrunner lintrunner-adapters # lintrunner init # ``` # # This will install lintrunner on your system and download all the necessary # dependencies to run linters locally. # If you want to see what lintrunner init will install, run # `lintrunner init --dry-run`. # # To lint local changes: # # ```bash # lintrunner # ``` # # To lint all files: # # ```bash # lintrunner --all-files # ``` # # To format files: # # ```bash # lintrunner -a # ``` # # To read more about lintrunner, see [wiki](https://github.com/pytorch/pytorch/wiki/lintrunner). # To update an existing linting rule or create a new one, modify this file or create a # new adapter following examples in https://github.com/justinchuby/lintrunner-adapters. merge_base_with = 'main' [[linter]] code = 'RUFF' include_patterns = [ '**/*.py', '**/*.pyi', ] exclude_patterns = [ '*_pb2*', '.setuptools-cmake-build/*', 'docs/**', ] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'ruff_linter', '--config=pyproject.toml', '@{{PATHSFILE}}' ] init_command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'pip_init', '--dry-run={{DRYRUN}}', '--requirement=requirements-lintrunner.txt', ] is_formatter = true [[linter]] code = 'MYPY' include_patterns = [ 'onnx/**/*.py', 'tools/**/*.py', ] exclude_patterns = [ 'onnx/backend/test/**', 'onnx/reference/ops/**', # FIXME: Enable this once typing is fixed 'onnx/test/**', # Disable mypy for tests 'onnx/reference/reference_evaluator.py', ] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'mypy_linter', '--config=pyproject.toml', '--show-disable', '--', '@{{PATHSFILE}}' ] init_command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'pip_init', '--dry-run={{DRYRUN}}', '--requirement=requirements-lintrunner.txt', ] [[linter]] code = 'RUFF-FORMAT' include_patterns = [ '**/*.py', ] exclude_patterns = [ '*_pb2*', '.setuptools-cmake-build/*', 'cmake/**', 'docs/**', ] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'ruff_format_linter', '--', '@{{PATHSFILE}}' ] init_command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'pip_init', '--dry-run={{DRYRUN}}', '--requirement=requirements-lintrunner.txt', ] is_formatter = true [[linter]] code = 'NAMESPACE' include_patterns = ['**/*.cc', '**/*.h'] exclude_patterns = ['third_party/**'] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'grep_linter', '--pattern=namespace onnx|onnx::', '--linter-name=NAMESPACE', '--error-name=hardcoded onnx namespace', """--error-description=\ Do not hardcode onnx's namespace in the c++ source code, so that \ other libraries that statically link with onnx can hide onnx symbols \ in a private namespace.\ """, '--', '@{{PATHSFILE}}' ] [[linter]] code = 'CLANGFORMAT' include_patterns = [ 'onnx/**/*.h', 'onnx/**/*.cc', ] exclude_patterns = [ ] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'clangformat_linter', '--binary=clang-format', '--fallback', '--', '@{{PATHSFILE}}' ] init_command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'pip_init', '--dry-run={{DRYRUN}}', '--requirement=requirements-lintrunner.txt', ] is_formatter = true [[linter]] code = 'EDITORCONFIG-CHECKER' include_patterns=[ '**/*.py', '**/*.pyi', '**/*.cc', '**/*.h', '**/*.md', '**/*.cpp', '**/*.yml', ] exclude_patterns = [ '*_pb2*', '.setuptools-cmake-build/*', 'cmake/**', 'docs/Changelog*', 'docs/Operators*', 'docs/TestCoverage*', 'community/sc-election-guidelines.md', ] command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'editorconfig_checker_linter', '--', '@{{PATHSFILE}}' ] init_command = [ 'python', '-m', 'lintrunner_adapters', 'run', 'pip_init', '--dry-run={{DRYRUN}}', '--requirement=requirements-lintrunner.txt', ] onnx-onnx-bca0315/CMakeLists.txt000066400000000000000000000520641511334557700166360ustar00rootroot00000000000000# Minimum CMake required cmake_minimum_required(VERSION 3.26) # Project project(onnx LANGUAGES CXX) include(cmake/Utils.cmake) # Honor visibility properties for all target types. cmake_policy(SET CMP0063 NEW) # Modules FindPython3 and FindPython use LOCATION for lookup strategy. cmake_policy(SET CMP0094 NEW) # Set default build type get_property(isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG) if(NOT isMultiConfig AND NOT CMAKE_BUILD_TYPE) message(STATUS "Build type not set - defaulting to Release") set( CMAKE_BUILD_TYPE "Release" CACHE STRING "Choose the type of build from: Debug Release RelWithDebInfo MinSizeRel Coverage." FORCE) endif() # https://reproducible-builds.org/docs/source-date-epoch/ if(DEFINED ENV{SOURCE_DATE_EPOCH}) execute_process( COMMAND "date" "-u" "-d" "@$ENV{SOURCE_DATE_EPOCH}" "+%Y-%m-%d" OUTPUT_VARIABLE BUILD_DATE OUTPUT_STRIP_TRAILING_WHITESPACE) else() execute_process( COMMAND "date" "+%Y-%m-%d" OUTPUT_VARIABLE BUILD_DATE OUTPUT_STRIP_TRAILING_WHITESPACE) endif() if(NOT BUILD_SHARED_LIBS) # by default, cmake builds static libraries set(BUILD_SHARED_LIBS OFF) endif() option(ONNX_BUILD_PYTHON "Build Python binaries" OFF) option(ONNX_BUILD_CUSTOM_PROTOBUF "Build and use ONNX's own protobuf" OFF) option(ONNX_USE_PROTOBUF_SHARED_LIBS "Build ONNX using protobuf shared library." OFF) option(ONNX_GEN_PB_TYPE_STUBS "Generate protobuf python type stubs" ON) option(ONNX_WERROR "Build with Werror" OFF) option(ONNX_COVERAGE "Build with coverage instrumentation" OFF) option(ONNX_BUILD_TESTS "Build ONNX C++ APIs Tests" OFF) option(ONNX_USE_ASAN "Build ONNX with ASAN" OFF) option(ONNX_USE_LITE_PROTO "Use lite protobuf instead of full." OFF) option(ONNX_DISABLE_EXCEPTIONS "Disable exception handling." OFF) option(ONNX_DISABLE_STATIC_REGISTRATION "Disable static registration for ONNX operator schemas." OFF) option(ONNX_USE_UNITY_BUILD "Enable Unity (Jumbo) build for" OFF) if(WIN32) option(ONNX_USE_MSVC_STATIC_RUNTIME "Build with MSVC static runtime" OFF) endif() set(CMAKE_EXPORT_COMPILE_COMMANDS ON) if(NOT DEFINED ONNX_ML) if(DEFINED ENV{ONNX_ML}) set(DEFAULT_ONNX_ML $ENV{ONNX_ML}) else() set(DEFAULT_ONNX_ML ON) endif() option(ONNX_ML "Enable traditional ML API." ${DEFAULT_ONNX_ML}) endif() if(NOT DEFINED ONNX_VERIFY_PROTO3) if(DEFINED ENV{ONNX_VERIFY_PROTO3}) set(PROTO3_ENABLED $ENV{ONNX_VERIFY_PROTO3}) else() set(PROTO3_ENABLED OFF) endif() option(ONNX_VERIFY_PROTO3 "Generate code by proto3" ${PROTO3_ENABLED}) endif() if(NOT DEFINED CMAKE_CXX_STANDARD) set(CMAKE_CXX_STANDARD 17) else() if(CMAKE_CXX_STANDARD VERSION_LESS 17) message(FATAL_ERROR "At least C++17 is required.") endif() endif() include(GNUInstallDirs) set(ONNX_ROOT ${onnx_SOURCE_DIR}) # Read ONNX version file(READ "${ONNX_ROOT}/VERSION_NUMBER" ONNX_VERSION) string(STRIP "${ONNX_VERSION}" ONNX_VERSION) if(NOT MSVC) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor") set(CMAKE_C_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0") if(ONNX_COVERAGE) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage") endif() endif() if(NOT ONNX_NAMESPACE) set(ONNX_NAMESPACE "onnx") endif() if(MSVC) if(NOT ONNX_DISABLE_EXCEPTIONS) string(APPEND CMAKE_CXX_FLAGS " /EHsc /wd26812") string(APPEND CMAKE_C_FLAGS " /EHsc /wd26812") endif() add_compile_options(/MP /utf-8 /nologo) add_compile_options( /wd5287 # https://developercommunity.visualstudio.com/t/warning-C5287:-operands-are-different-e/10877942?sort=newest /Zc:lambda # https://developercommunity.visualstudio.com/t/fatal--error-C1001:-Internal-compiler-er/10906076 ) endif() if(ONNX_DISABLE_EXCEPTIONS) add_compile_definitions("ONNX_NO_EXCEPTIONS") # Disable C++ exceptions. if(MSVC) string(REGEX REPLACE "/EHsc" "/EHs-c-" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") add_definitions(-D_HAS_EXCEPTIONS=0) else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables") endif() endif() if(ONNX_BUILD_PYTHON) set(python_dev_component Development.Module Development.SABIModule) endif() if(CMAKE_CROSSCOMPILING) # When cross-compiling, the interpreter and the compiling/linking steps # must use a different package. See the discussion about this at # https://gitlab.kitware.com/cmake/cmake/-/issues/25145 if(ONNX_BUILD_PYTHON) find_package(Python3 REQUIRED COMPONENTS ${python_dev_component}) endif() find_package(Python REQUIRED COMPONENTS Interpreter) set(ONNX_PYTHON_INTERPRETER Python::Interpreter) else() find_package(Python3 REQUIRED COMPONENTS Interpreter ${python_dev_component}) # Find Python for nanobind set(Python_EXECUTABLE ${Python3_EXECUTABLE}) find_package(Python REQUIRED COMPONENTS Interpreter ${python_dev_component}) set(ONNX_PYTHON_INTERPRETER Python3::Interpreter) endif() if(CMAKE_SYSTEM_NAME STREQUAL "AIX") set(CMAKE_NO_SYSTEM_FROM_IMPORTED 1) endif() # Build the libraries with -fPIC including the protobuf lib. if(NOT DEFINED CMAKE_POSITION_INDEPENDENT_CODE) set(CMAKE_POSITION_INDEPENDENT_CODE ON) endif() list(APPEND CMAKE_MODULE_PATH ${ONNX_ROOT}/cmake/external) if(NOT ONNX_BUILD_CUSTOM_PROTOBUF) if((ONNX_USE_LITE_PROTO AND TARGET protobuf::libprotobuf-lite) OR ((NOT ONNX_USE_LITE_PROTO) AND TARGET protobuf::libprotobuf)) # Sometimes we need to use protoc compiled for host architecture while linking # libprotobuf against target architecture. See https://github.com/caffe2/caffe # 2/blob/96f35ad75480b25c1a23d6e9e97bccae9f7a7f9c/cmake/ProtoBuf.cmake#L92-L99 if(EXISTS "${ONNX_CUSTOM_PROTOC_EXECUTABLE}") message(STATUS "Using custom protoc executable") set(ONNX_PROTOC_EXECUTABLE ${ONNX_CUSTOM_PROTOC_EXECUTABLE}) else() if(TARGET protobuf::protoc) set(ONNX_PROTOC_EXECUTABLE $) endif() endif() else() # Customized version of find Protobuf. We need to avoid situations mentioned # in https://github.com/caffe2/caffe2/blob/b7d983f255ef5496474f1ea188edb5e0ac4 # 42761/cmake/ProtoBuf.cmake#L82-L92 The following section is stolen from # cmake/ProtoBuf.cmake in Caffe2 find_program(Protobuf_PROTOC_EXECUTABLE NAMES protoc DOC "The Google Protocol Buffers Compiler") # Only if protoc was found, seed the include directories and libraries. We # assume that protoc is installed at PREFIX/bin. We use get_filename_component # to resolve PREFIX. if(Protobuf_PROTOC_EXECUTABLE) set(ONNX_PROTOC_EXECUTABLE ${Protobuf_PROTOC_EXECUTABLE}) get_filename_component(_PROTOBUF_INSTALL_PREFIX ${Protobuf_PROTOC_EXECUTABLE} DIRECTORY) get_filename_component(_PROTOBUF_INSTALL_PREFIX ${_PROTOBUF_INSTALL_PREFIX}/.. REALPATH) find_library(Protobuf_PROTOC_LIBRARY NAMES protoc PATHS ${_PROTOBUF_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR} NO_DEFAULT_PATH) if(ONNX_USE_LITE_PROTO) find_library(Protobuf_LITE_LIBRARY NAMES protobuf-lite PATHS ${_PROTOBUF_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR} NO_DEFAULT_PATH) else() find_library(Protobuf_LIBRARY NAMES protobuf PATHS ${_PROTOBUF_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR} NO_DEFAULT_PATH) endif(ONNX_USE_LITE_PROTO) find_path(Protobuf_INCLUDE_DIR google/protobuf/service.h PATHS ${_PROTOBUF_INSTALL_PREFIX}/include NO_DEFAULT_PATH) if(ONNX_USE_PROTOBUF_SHARED_LIBS) set(Protobuf_USE_STATIC_LIBS OFF) else() set(Protobuf_USE_STATIC_LIBS ON) endif() find_package(Protobuf) if(Protobuf_FOUND) set(PROTOBUF_DIR "${_PROTOBUF_INSTALL_PREFIX}") set(Build_Protobuf OFF) if("${Protobuf_VERSION}" VERSION_GREATER_EQUAL "4.22.0") # There are extra dependencies for protobuf. find_package(absl REQUIRED) find_package(utf8_range) message(STATUS "absl_VERSION: ${absl_VERSION}") set(protobuf_ABSL_USED_TARGETS absl::absl_check absl::absl_log absl::algorithm absl::base absl::bind_front absl::bits absl::btree absl::cleanup absl::cord absl::core_headers absl::debugging absl::die_if_null absl::dynamic_annotations absl::flags absl::flat_hash_map absl::flat_hash_set absl::function_ref absl::hash absl::layout absl::log_initialize absl::log_severity absl::memory absl::node_hash_map absl::node_hash_set absl::optional absl::span absl::status absl::statusor absl::strings absl::synchronization absl::time absl::type_traits absl::utility absl::variant utf8_range::utf8_range utf8_range::utf8_validity ) endif() endif() endif() endif() endif() if(NOT ONNX_PROTOC_EXECUTABLE) set(Build_Protobuf ON) set(protobuf_MSVC_STATIC_RUNTIME ${ONNX_USE_MSVC_STATIC_RUNTIME}) include(FetchContent) set(ABSL_PROPAGATE_CXX_STD 1) set(ONNX_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}) set(ONNX_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}) # Use this setting to build third-party libs. set(BUILD_SHARED_LIBS ${ONNX_USE_PROTOBUF_SHARED_LIBS}) set(ProtobufURL https://github.com/protocolbuffers/protobuf/releases/download/v31.1/protobuf-31.1.tar.gz) set(ProtobufSHA1 da10aaa3bf779735a8a9acde1256a47ce5d148be) FetchContent_Declare( Protobuf URL ${ProtobufURL} URL_HASH SHA1=${ProtobufSHA1} ) set(protobuf_BUILD_TESTS OFF CACHE BOOL "Build protobuf tests" FORCE) message(STATUS "Download and build Protobuf from ${ProtobufURL}") FetchContent_MakeAvailable(Protobuf) set(ONNX_PROTOC_EXECUTABLE $) set(Protobuf_VERSION "6.31.1") # Change back the BUILD_SHARED_LIBS to control the onnx project. set(BUILD_SHARED_LIBS ${ONNX_BUILD_SHARED_LIBS}) set(PROTOBUF_DIR "${protobuf_BINARY_DIR}") set(CMAKE_CXX_FLAGS ${ONNX_CMAKE_CXX_FLAGS}) endif() message(STATUS "ONNX_PROTOC_EXECUTABLE: ${ONNX_PROTOC_EXECUTABLE}") # function(RELATIVE_PROTOBUF_GENERATE_CPP SRCS HDRS ROOT_DIR) from https://githu # b.com/tensorflow/tensorflow/blob/d2c3b873c6f8ff999a2e4ee707a84ff00d9c15a5/tens # orflow/contrib/cmake/tf_core_framework.cmake to solve the problem that # customized dir can't be specified when calling PROTOBUF_GENERATE_CPP. function(RELATIVE_PROTOBUF_GENERATE_CPP SRCS) if(NOT ARGN) message( SEND_ERROR "Error: RELATIVE_PROTOBUF_GENERATE_CPP() called without any proto files" ) return() endif() set(${SRCS}) set(GEN_PROTO_PY "${ONNX_ROOT}/onnx/gen_proto.py") set(GENERATED_FILES) foreach(INFILE ${ARGN}) set(ABS_FILE "${ONNX_ROOT}/${INFILE}") get_filename_component(FILE_DIR ${ABS_FILE} DIRECTORY) get_filename_component(FILE_WE ${INFILE} NAME_WE) # "onnx-data" check is because we do not want to create/compile an "onnx-data-ml.proto" file if(ONNX_ML AND NOT(FILE_WE STREQUAL "onnx-data")) if(ONNX_NAMESPACE STREQUAL "onnx") set(GENERATED_FILE_WE "${FILE_WE}-ml") else() set(GENERATED_FILE_WE "${FILE_WE}_${ONNX_NAMESPACE}-ml") endif() else() if(ONNX_NAMESPACE STREQUAL "onnx") set(GENERATED_FILE_WE "${FILE_WE}") else() set(GENERATED_FILE_WE "${FILE_WE}_${ONNX_NAMESPACE}") endif() endif() file(RELATIVE_PATH REL_DIR "${ONNX_ROOT}" "${FILE_DIR}") set(OUTPUT_PROTO_DIR "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}") set(OUTPUT_PB_SRC "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.pb.cc") set(GENERATED_PROTO "${OUTPUT_PROTO_DIR}/${GENERATED_FILE_WE}.proto") list(APPEND ${SRCS} "${OUTPUT_PB_SRC}") if(NOT EXISTS "${OUTPUT_PROTO_DIR}") file(MAKE_DIRECTORY "${OUTPUT_PROTO_DIR}") endif() set(GEN_PROTO_ARGS -p "${ONNX_NAMESPACE}" -o "${OUTPUT_PROTO_DIR}" "${FILE_WE}") if(ONNX_ML) list(APPEND GEN_PROTO_ARGS -m) endif() if(ONNX_USE_LITE_PROTO) list(APPEND GEN_PROTO_ARGS -l) endif() if(ONNX_VERIFY_PROTO3) if(NOT ONNX_PROTOC_EXECUTABLE) message(FATAL_ERROR "Protobuf compiler not found") endif() list(APPEND GEN_PROTO_ARGS --protoc_path) list(APPEND GEN_PROTO_ARGS "${ONNX_PROTOC_EXECUTABLE}") endif() # Use add_custom_command to avoid re-generate of PROTO files add_custom_command(OUTPUT "${GENERATED_PROTO}" COMMAND ${ONNX_PYTHON_INTERPRETER} "${GEN_PROTO_PY}" ${GEN_PROTO_ARGS} DEPENDS ${INFILE} COMMENT "Running gen_proto.py on ${INFILE}") message("Generated: ${GENERATED_PROTO}") set(PROTOC_ARGS ${GENERATED_PROTO} -I ${CMAKE_CURRENT_BINARY_DIR} --cpp_out ${CMAKE_CURRENT_BINARY_DIR}) if(ONNX_BUILD_PYTHON) list(APPEND PROTOC_ARGS --python_out) if(ONNX_GEN_PB_TYPE_STUBS) list(APPEND PROTOC_ARGS pyi_out:${CMAKE_CURRENT_BINARY_DIR}) else() list(APPEND PROTOC_ARGS ${CMAKE_CURRENT_BINARY_DIR}) endif() endif() list(APPEND GENERATED_FILES "${GENERATED_PROTO}") add_custom_command(OUTPUT "${OUTPUT_PB_SRC}" COMMAND "${ONNX_PROTOC_EXECUTABLE}" ${PROTOC_ARGS} DEPENDS ${GENERATED_FILES} COMMENT "Running C++ protocol buffer compiler on ${GENERATED_PROTO}") endforeach() set(${SRCS} ${${SRCS}} PARENT_SCOPE) endfunction() relative_protobuf_generate_cpp(ONNX_PROTO_SRCS onnx/onnx.in.proto onnx/onnx-operators.in.proto onnx/onnx-data.in.proto) add_library(onnx_proto_object OBJECT ${ONNX_PROTO_SRCS}) file(GLOB_RECURSE __tmp_srcs "${ONNX_ROOT}/onnx/*.h" "${ONNX_ROOT}/onnx/*.cc") file(GLOB_RECURSE onnx_gtests_src "${ONNX_ROOT}/onnx/test/cpp/*.h" "${ONNX_ROOT}/onnx/test/cpp/*.cc" "${ONNX_ROOT}/onnx/backend/test/cpp/*.cc" "${ONNX_ROOT}/onnx/backend/test/cpp/*.h") list(REMOVE_ITEM __tmp_srcs "${ONNX_ROOT}/onnx/cpp2py_export.cc") list(REMOVE_ITEM __tmp_srcs ${onnx_gtests_src} "${ONNX_ROOT}/onnx/test/cmake/main.cc") list(APPEND ONNX_SRCS ${__tmp_srcs}) set(LINKED_PROTOBUF_TARGET protobuf::libprotobuf) if(ONNX_USE_LITE_PROTO) if(TARGET protobuf::libprotobuf-lite) set(LINKED_PROTOBUF_TARGET protobuf::libprotobuf-lite) endif() endif() add_onnx_global_defines(onnx_proto_object) target_include_directories(onnx_proto_object PUBLIC $) if(MSVC) # For disabling Protobuf related warnings set(protobuf_warnings /wd4146 # unary minus operator applied to unsigned type, # result still unsigned /wd4244 # 'argument': conversion from 'google:: # protobuf::uint64' to 'int', possible # loss of data /wd4267 # Conversion from 'size_t' to 'int', # possible loss of data /wd4141 # 'inline': used more than once ) endif() add_library(onnx_proto) target_link_libraries(onnx_proto PUBLIC $) if(ONNX_ML) target_compile_definitions(onnx_proto PUBLIC ONNX_ML=1) endif() target_compile_definitions(onnx_proto PUBLIC ONNX_NAMESPACE=${ONNX_NAMESPACE}) # onnx_object and onnx_proto_object are collections of C++ object files. # They are introduced to help onnx_cpp2py_export bypass library boundaries and use ONNX private symbols. add_library(onnx_object OBJECT ${ONNX_SRCS}) add_dependencies(onnx_object onnx_proto_object) target_include_directories(onnx_object PUBLIC $) set_target_properties(onnx_object PROPERTIES CXX_VISIBILITY_PRESET hidden) set_target_properties(onnx_object PROPERTIES VISIBILITY_INLINES_HIDDEN ON) target_include_directories(onnx_object PUBLIC $) add_onnx_global_defines(onnx_object) target_link_libraries(onnx_proto_object PUBLIC ${LINKED_PROTOBUF_TARGET}) target_link_libraries(onnx_object PUBLIC ${LINKED_PROTOBUF_TARGET}) foreach(ABSL_USED_TARGET IN LISTS protobuf_ABSL_USED_TARGETS) if(TARGET ${ABSL_USED_TARGET}) target_link_libraries(onnx_proto_object PUBLIC ${ABSL_USED_TARGET}) target_link_libraries(onnx_object PUBLIC ${ABSL_USED_TARGET}) endif() endforeach() add_library(onnx) target_link_libraries(onnx PUBLIC $ $) target_include_directories(onnx PUBLIC $) if(ONNX_ML) target_compile_definitions(onnx PUBLIC ONNX_ML=1) endif() target_compile_definitions(onnx PUBLIC ONNX_NAMESPACE=${ONNX_NAMESPACE}) target_compile_options(onnx_object PUBLIC ${protobuf_warnings}) target_compile_options(onnx_proto_object PUBLIC ${protobuf_warnings}) if(ONNX_BUILD_PYTHON) # find system nanobind find_package(nanobind) if(NOT nanobind_FOUND) include(FetchContent) FetchContent_Declare( nanobind GIT_REPOSITORY https://github.com/wjakob/nanobind.git GIT_TAG v2.8.0 GIT_SHALLOW TRUE ) FetchContent_MakeAvailable(nanobind) endif() # Configure nanobind: https://nanobind.readthedocs.io/en/latest/api_cmake.html nanobind_add_module( onnx_cpp2py_export NB_STATIC NB_DOMAIN onnx STABLE_ABI FREE_THREADED LTO "${ONNX_ROOT}/onnx/cpp2py_export.cc") target_link_libraries(onnx_cpp2py_export PRIVATE $ $) # Prevent "undefined symbol: _ZNSt10filesystem7__cxx114path14_M_split_cmptsEv" # (std::filesystem::__cxx11::path::_M_split_cmpts()) on gcc 8 if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0) target_link_libraries(onnx_cpp2py_export PRIVATE "-lstdc++fs") endif() endif() if(MSVC) add_msvc_runtime_flag(onnx_proto_object) add_msvc_runtime_flag(onnx_object) if(TARGET onnx_cpp2py_export) add_msvc_runtime_flag(onnx_cpp2py_export) endif() if(ONNX_WERROR) target_compile_options(onnx_object PRIVATE "/WX") endif() else() target_compile_options(onnx_object PRIVATE -Wall -Wextra) if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 13) target_compile_options(onnx_object PRIVATE "-Wno-stringop-overflow") endif() if(ONNX_WERROR) target_compile_options(onnx_object PRIVATE "-Werror") endif() endif() if(ONNX_USE_ASAN AND NOT MSVC) find_package(Sanitizer REQUIRED) if(TARGET Sanitizer::address) target_link_libraries(onnx PRIVATE Sanitizer::address) message(STATUS "Use ASAN") endif() if(TARGET Sanitizer::undefined) target_link_libraries(onnx PRIVATE Sanitizer::undefined) message(STATUS "Use UBSAN") endif() endif() install(DIRECTORY ${ONNX_ROOT}/onnx DESTINATION ${CMAKE_INSTALL_INCLUDEDIR} FILES_MATCHING PATTERN "*.h" PATTERN "backend/test/case" EXCLUDE PATTERN "backend/test/data" EXCLUDE) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/onnx DESTINATION ${CMAKE_INSTALL_INCLUDEDIR} FILES_MATCHING PATTERN "*.h") configure_file( ${PROJECT_SOURCE_DIR}/cmake/ONNXConfigVersion.cmake.in ${PROJECT_BINARY_DIR}/ONNXConfigVersion.cmake @ONLY) configure_file( ${PROJECT_SOURCE_DIR}/cmake/ONNXConfig.cmake.in ${PROJECT_BINARY_DIR}/ONNXConfig.cmake @ONLY) install(FILES ${PROJECT_BINARY_DIR}/ONNXConfigVersion.cmake ${PROJECT_BINARY_DIR}/ONNXConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ONNX COMPONENT dev) if(ONNX_USE_UNITY_BUILD) # If ONNX_USE_UNITY_BUILD is set to ON, set ONNX target to use Unity builds. # We set Unity build to use groups, it allows us to set some specific files to be compiled individually set_target_properties(onnx PROPERTIES UNITY_BUILD ON UNITY_BUILD_MODE GROUP ) set(NEW_LIST __unity_src_files) list(APPEND __unity_src_files ${ONNX_SRCS}) # These files have an issue with template explicit specialization after instantiation: # We take them out of the unity group so that they are compiled individually. list(REMOVE_ITEM __unity_src_files "${ONNX_ROOT}/onnx/defs/schema.cc") list(REMOVE_ITEM __unity_src_files "${ONNX_ROOT}/onnx/defs/tensor_proto_util.cc") set_source_files_properties(${__unity_src_files} PROPERTIES UNITY_GROUP "Unity_Group" ) # With unity build object file could get big, need this switch in MSVC. if(MSVC) target_compile_options(onnx PRIVATE /bigobj) endif() # should be enabled for onnx_proto when protobuf can support Unity builds endif() if(ONNX_BUILD_TESTS) find_package(GTest) if(NOT GTest_FOUND) include(googletest) endif() endif() install(TARGETS onnx onnx_proto EXPORT ONNXTargets DESTINATION ${CMAKE_INSTALL_LIBDIR}) install(EXPORT ONNXTargets DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/ONNX" NAMESPACE ONNX:: ) if(ONNX_BUILD_TESTS) include(${ONNX_ROOT}/cmake/unittest.cmake) endif() include(cmake/summary.cmake) onnx_print_configuration_summary() onnx-onnx-bca0315/CODEOWNERS000066400000000000000000000011531511334557700154620ustar00rootroot00000000000000* @onnx/sig-archinfra-approvers /community @onnx/steering-committee /onnx/defs @onnx/sig-operators-approvers /onnx/defs/parser.* @onnx/sig-archinfra-approvers /onnx/defs/printer.* @onnx/sig-archinfra-approvers /onnx/backend/test @onnx/sig-operators-approvers /onnx/reference/ops @onnx/sig-operators-approvers /docs/AddNewOp.md @onnx/sig-operators-approvers /docs/TestCoverage*.md @onnx/sig-operators-approvers /docs/Operators*.md @onnx/sig-operators-approvers /docs/OpConventions.md @onnx/sig-operators-approvers /docs/Broadcasting.md @onnx/sig-operators-approvers /docs/Changelog*.md @onnx/sig-operators-approvers onnx-onnx-bca0315/CODE_OF_CONDUCT.md000066400000000000000000000001141511334557700166620ustar00rootroot00000000000000The ONNX Code Of Conduct is described at https://onnx.ai/codeofconduct.html onnx-onnx-bca0315/CONTRIBUTING.md000066400000000000000000000211431511334557700163210ustar00rootroot00000000000000 # ONNX Community Involvement and Contribution Guidelines ONNX is a community project and we welcome your contributions! In addition to contributing code, you can also contribute in many other ways: - Meetings and Discussions Join SIGS, Working Groups, Community meetings to learn about what is needed and then where there is a good fit to interest and areas of expertise, find ways to actively contribute. Participate in [ONNX technical discussions](https://github.com/onnx/onnx/discussions) on GitHub. Join the ONNX Slack channels at LF AI and Data, help answer questions and welcome new members. - Use Cases and Tools Develop use cases for ONNX and advocate for ONNX in developer conferences and meetups. Develop tools that import and export using the ONNX spec, and help grow the community of ONNX users. Become a champion for ONNX in your company or organization. - Roadmap and Features Understand the ONNX roadmap document, feature priorities, and help implement them. Become an ONNX code and documentation contributor, and work towards committer status on important repos. - Releases and Model Zoo Help in achieving a release of ONNX, including increasing the number of models in the ONNX Model Zoo that exercise ONNX features. - Publications and Blogs Add to the growing number of arXiv papers that refer to ONNX. Create blogs, presentations, books, articles and other materials that help increase the adoption of ONNX, and grow the community of users and contributors. - Steering Committee Attend ONNX Steering Committee meetings - they are open to all in the community. Help out where needed and appropriate on SC to-do items. Note that SIG and Working Groups leaders as well as others with demonstrated commitment and contributions to ONNX community may want to self-nominate during the annual SC election cycle. ## Adding a new operator or creating a new version of an existing operator ONNX is an open standard, and we encourage developers to contribute high quality operators to ONNX specification. Before proposing a new operator, please read [the tutorial](docs/AddNewOp.md). ## Contributing code You can submit a pull request (PR) with your code. The [SIG](community/sigs.md) or [Working Group](community/working-groups.md) that is responsible for the area of the project your PR touches will review it and merge once any comments are addressed. ### Development To build ONNX from source please follow the instructions listed [here](https://github.com/onnx/onnx/blob/main/INSTALL.md#build-onnx-from-source). Then, after you have made changes to Python and C++ files: - `Python files`: The changes are effective immediately in your installation. You don't need to install these again. - `C++ files`: You need to install these again to trigger the native extension build. Assuming build succeed in the initial step, simply running ```sh pip install -e . -v ``` from onnx root dir should work. ### Folder structure - `onnx/`: the main folder that all code lies under - `onnx.proto`: the protobuf that contains all the structures - `checker.py`: a utility to check whether a serialized ONNX proto is legal - `shape_inference.py`: a utility to infer types and shapes for ONNX models - `version_converter.py`: a utility to upgrade or downgrade version for ONNX models - `parser.py`: a utility to create an ONNX model or graph from a textual representation - `hub.py`: a utility for downloading models from [ONNX Model Zoo](https://github.com/onnx/models) - `compose.py`: a utility to merge ONNX models - `helper.py`: tools for graph operation - `defs/`: a subfolder that defines the ONNX operators - `test/`: test files ### Generated operator documentation Operator docs ([Operators.md](Operators.md), [Operators-ml.md](Operators-ml.md)) and Changelog docs ([Changelog.md](Changelog.md), [Changelog-ml.md](Changelog-ml.md)) are automatically generated based on C++ operator definitions and backend Python snippets. To refresh all these docs, run the following commands from the repo root and commit the results by setting "ONNX_ML=1". By contrast, setting `ONNX_ML=0` will only update `Operators.md` and `Changelog.md`. ```pwsh # Windows set ONNX_ML=1 ``` ```sh # UNIX export ONNX_ML=1 pip install -e . -v python onnx/defs/gen_doc.py ``` ### Coding style We adopted the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html) for this project. We use `lintrunner` to drive multiple linters defined in `.lintrunner.toml` to lint the codebase. To run these checks locally, install `lintrunner` and the linters with ```sh pip install lintrunner lintrunner-adapters lintrunner init ``` Then lint with ```sh lintrunner ``` format with ```sh # Display all lints and apply the fixes lintrunner -a # Or apply fixes only (faster) lintrunner f ``` Run `lintrunner --help` and see the `.lintrunner.toml` file for more usage examples, as well as instructions on how to adopt new linters. ### Testing ONNX uses [pytest](https://docs.pytest.org) as a test driver. To run tests, you'll first need to install pytest: ```sh pip install pytest ``` After installing pytest, run from the root of the repo: ```sh pytest ``` to run the tests. You'll need to regenerate test coverage too, by running this command from the root of the repo: ```sh python onnx/backend/test/stat_coverage.py ``` #### Cpp tests (googletest) Some functionalities are tested with googletest. Those tests are listed in `test/cpp`, and include tests for shape inference, data propagation, parser, and others. To run them, first build ONNX with `-DONNX_BUILD_TESTS=1` or `ONNX_BUILD_TESTS=1 pip install -e . -v`. ##### Linux and MacOS The cpp tests require dynamically linking to built libraries. ```sh export LD_LIBRARY_PATH="./.setuptools-cmake-build/:$LD_LIBRARY_PATH" .setuptools-cmake-build/onnx_gtests ``` ##### Windows ```pwsh # If you set DEBUG=1, use `.setuptools-cmake-build\Debug\onnx_gtests.exe` instead .setuptools-cmake-build\Release\onnx_gtests.exe ``` ### DCO ONNX has adopted the [DCO](https://en.wikipedia.org/wiki/Developer_Certificate_of_Origin). All code repositories under ONNX require a DCO. (ONNX previously used a CLA, which is being replaced with the DCO.) DCO is provided by including a sign-off-by line in commit messages. Using the `-s` flag for `git commit` will automatically append this line. For example, running `git commit -s -m 'commit info.'` it will produce a commit that has the message `commit info. Signed-off-by: My Name `. The DCO bot will ensure commits are signed with an email address that matches the commit author before they are eligible to be merged. If you are using a GUI like the GitHub web site or GitHub Desktop, you'll need to append the `Signed-off-by: My Name ` manually to each commit message. For the onnx organization [sign-off](https://github.blog/changelog/2022-06-08-admins-can-require-sign-off-on-web-based-commits/) for web based commits is enabled. When this is activated you will see "Sign off and propose changes" instead of "Propose changes" when you are editing files directly at github. It is recommended to set this setting for your own fork as well. Since in the review process commits are made on this fork. NOTE: the sign-off is needed for each commit in the PR, not at the PR level. If you have old commits that are not signed, use the following commands to squash the old PR (original branch) into a single commit. This is an easier way to signoff old commits in old PR. ```bash git checkout main git checkout -b temporary_patch # create a new branch as temporary git merge --squash original_patch # copy from old branch git branch -d original_patch # remove old branch git checkout -b original_patch # create a new branch with the same name (override) git commit -m 'type your own commit msg' -s # signoff that single commit git push origin original_patch -f # forcibly override the old branch` ``` ## CI Pipelines Every PR needs to pass CIs before merge. CI pipelines details are [here](docs/CIPipelines.md). ## Other developer documentation - [How to implement ONNX backend (ONNX to something converter)](docs/ImplementingAnOnnxBackend.md) - [Backend test infrastructure and how to add tests](docs/OnnxBackendTest.md) ## License [Apache License v2.0](/LICENSE) ## Code of Conduct [ONNX Open Source Code of Conduct](http://onnx.ai/codeofconduct.html) onnx-onnx-bca0315/CPPLINT.cfg000066400000000000000000000000231511334557700156540ustar00rootroot00000000000000filter=-whitespace onnx-onnx-bca0315/INSTALL.md000066400000000000000000000237601511334557700155270ustar00rootroot00000000000000 # Installation ## Official Python packages ONNX released packages are published in PyPi. ```sh pip install onnx # or pip install onnx[reference] for optional reference implementation dependencies ``` [ONNX weekly packages](https://pypi.org/project/onnx-weekly/) are published in PyPI to enable experimentation and early testing. ## vcpkg packages ONNX is in the maintenance list of [vcpkg](https://github.com/microsoft/vcpkg), you can easily use vcpkg to build and install it. ```sh git clone https://github.com/microsoft/vcpkg.git cd vcpkg ./bootstrap-vcpkg.bat # For powershell ./bootstrap-vcpkg.sh # For bash ./vcpkg install onnx ``` ## Conda packages A binary build of ONNX is available from [Conda](https://conda.io), in [conda-forge](https://conda-forge.org/): ```sh conda install -c conda-forge onnx ``` ## Build ONNX from Source Before building from source uninstall any existing versions of ONNX via `pip uninstall onnx`. C++17 or higher C++ compiler version is required to build ONNX from source. Still, users can specify their own `CMAKE_CXX_STANDARD` version for building ONNX. Protobuf is required for ONNX. If you don't have Protobuf installed, ONNX will internally download and build Protobuf for ONNX build. Or, you can manually install [Protobuf C/C++ libraries and tools](https://github.com/protocolbuffers/protobuf) with specified version before proceeding forward. Then depending on how you installed Protobuf, you need to set environment variable CMAKE_ARGS to "-DONNX_USE_PROTOBUF_SHARED_LIBS=ON" or "-DONNX_USE_PROTOBUF_SHARED_LIBS=OFF". For example, you may need to run the following command: Linux or Mac: ```sh export CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON" ``` Windows: ```bat set CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON" ``` The ON/OFF depends on what kind of Protobuf library you have. Shared libraries are files ending with \*.dll/\*.so/\*.dylib. Static libraries are files ending with \*.a/\*.lib. This option depends on how you get your Protobuf library and how it was built. Because its default value is OFF, you don't need to run the commands above if you'd prefer to use a static Protobuf library. ### Windows ``` git clone https://github.com/onnx/onnx.git cd onnx git submodule update --init --recursive # prefer lite proto set CMAKE_ARGS='-DONNX_USE_LITE_PROTO=ON -DONNX_USE_PROTOBUF_SHARED_LIBS=ON' pip install -e . -v ``` ### Conda-forge-based development environment A conda-forge-based development environment is also provided. After installing the [pixi package manager](https://prefix.dev/), users may directly execute any of the following commands. Upon doing so pixi will install the required dependencies automatically in isolated environments. Running ```sh pixi run install ``` builds and installs the `onnx` package into the default environment. After the installation has completed one can run the gtest and pytest suites via the pixi-tasks of the same name: ```sh pixi run gtest ``` and ```sh pixi run pytest ``` Further task for re-generating the operator documentation (`pixi run gen-docs`), setting-up lintrunner (`pixi run lintrunner-init`), and executing lintrunner (`pixi run lintrunner-run`) are also available. #### Old instructions If you are building ONNX from source, it is recommended that you also build Protobuf locally as a static library. The version distributed with conda-forge is a DLL, but ONNX expects it to be a static library. Building Protobuf locally also lets you control the version of Protobuf. The tested and recommended version is 5.29.2. The instructions in this README assume you are using Visual Studio 2019. It is recommended that you run all the commands from a shell started from "x64 Native Tools Command Prompt for VS 2019" and keep the build system generator for cmake (e.g., cmake -G "Visual Studio 16 2019") consistent while building Protobuf as well as ONNX. You can build Protobuf from source by running the following commands: ```bat git clone https://github.com/protocolbuffers/protobuf.git cd protobuf git checkout v5.29.2 git submodule update --init --recursive cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX= -Dprotobuf_MSVC_STATIC_RUNTIME=OFF -Dprotobuf_BUILD_SHARED_LIBS=OFF -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_EXAMPLES=OFF cmake --build . --config Release --target install ``` Then it will be built as a static library and installed to . Please add the bin directory(which contains protoc.exe) to your PATH. ```bat set CMAKE_PREFIX_PATH=;%CMAKE_PREFIX_PATH% ``` Please note: if your protobuf_install_dir contains spaces, **do not** add quotation marks around it. Alternative: if you have local Protobuf executable and want to use it for ONNX, you can set ONNX_PROTOC_EXECUTABLE instead. ```bat set CMAKE_ARGS=-DONNX_PROTOC_EXECUTABLE= ``` Then you can build ONNX as: ``` git clone https://github.com/onnx/onnx.git cd onnx git submodule update --init --recursive # prefer lite proto set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON pip install -e . -v ``` ### Linux First, you need to install Protobuf. The minimum Protobuf compiler (protoc) version required by ONNX is 4.25.1. Please note that old protoc versions might not work with `CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON`. Ubuntu 20.04 (and newer) users may choose to install Protobuf (which is usually lower than 4.25.1) via ```sh apt-get install python3-pip python3-dev libprotobuf-dev protobuf-compiler ``` In this case, ONNX is able to detect and use the system Profobuf. Users of other Linux distributions can use their system package manager to install Profobuf libraries similarly. A better way is to build and install the required Protobuf version from source. See the instructions below for more details.
Installing Protobuf from source ```sh git clone https://github.com/protocolbuffers/protobuf.git cd protobuf git checkout v5.29.2 git submodule update --init --recursive mkdir build_source && cd build_source cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=/usr -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_POSITION_INDEPENDENT_CODE=ON .. cmake --build . --target install ``` Here "-DCMAKE_POSITION_INDEPENDENT_CODE=ON" is crucial. By default static libraries are built without "-fPIC" flag, they are not position independent code. But shared libraries must be position independent code. Python C/C++ extensions(like ONNX) are shared libraries. So if a static library was not built with "-fPIC", it can't be linked to such a shared library. Once build is successful, update PATH to include Protobuf paths so that ONNX can find Protobuf.
Then you can build ONNX as: ```sh git clone https://github.com/onnx/onnx.git cd onnx git submodule update --init --recursive # Optional: prefer lite proto export CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON pip install -e . -v ``` ### Mac ```sh brew update brew install cmake git clone https://github.com/protocolbuffers/protobuf.git cd protobuf git checkout v5.29.2 git submodule update --init --recursive mkdir build_source && cd build_source cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_POSITION_INDEPENDENT_CODE=ON .. cmake --build . --target install ``` Once build is successful, update PATH to include Protobuf paths so that ONNX can find Protobuf. Then you can build ONNX as: ```sh git clone --recursive https://github.com/onnx/onnx.git cd onnx # Optional: prefer lite proto set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON pip install -e . -v ``` ## Verify Installation After installation, run ```sh python -c "import onnx" ``` to verify it works. ## Common Build Options For full list refer to CMakeLists.txt ### Environment variables * `USE_MSVC_STATIC_RUNTIME` should be 1 or 0, not ON or OFF. When set to 1 ONNX links statically to runtime library. **Default**: `USE_MSVC_STATIC_RUNTIME=0` * `DEBUG` should be 0 or 1. When set to 1 ONNX is built in debug mode. For debug versions of the dependencies, you need to open the [CMakeLists file](https://github.com/onnx/onnx/blob/main/CMakeLists.txt) and append a letter `d` at the end of the package name lines. For example, `NAMES protobuf-lite` would become `NAMES protobuf-lited`. **Default**: `Debug=0` ### CMake variables * `ONNX_USE_PROTOBUF_SHARED_LIBS` should be `ON` or `OFF`. **Default**: `ONNX_USE_PROTOBUF_SHARED_LIBS=OFF USE_MSVC_STATIC_RUNTIME=0` `ONNX_USE_PROTOBUF_SHARED_LIBS` determines how ONNX links to Protobuf libraries. * When set to `ON` - ONNX will dynamically link to Protobuf shared libs, PROTOBUF_USE_DLLS will be defined as described [here](https://github.com/protocolbuffers/protobuf/blob/main/cmake/README.md#dlls-vs-static-linking). * When set to `OFF` - ONNX will link statically to Protobuf. * `ONNX_USE_LITE_PROTO` should be `ON` or `OFF`. When set to `ON` ONNX uses lite Protobuf instead of full Protobuf. **Default**: `ONNX_USE_LITE_PROTO=OFF` * `ONNX_WERROR` should be `ON` or `OFF`. When set to `ON` warnings are treated as errors. **Default**: `ONNX_WERROR=OFF` in local builds, `ON` in CI and release pipelines. ## Common Errors * Note: the `import onnx` command does not work from the source checkout directory; in this case you'll see `ModuleNotFoundError: No module named 'onnx.onnx_cpp2py_export'`. Change into another directory to fix this error. * If you run into any issues while building Protobuf as a static library, please ensure that shared Protobuf libraries, like libprotobuf, are not installed on your device or in the conda environment. If these shared libraries exist, either remove them to build Protobuf from source as a static library, or skip the Protobuf build from source to use the shared version directly. * If you run into any issues while building ONNX from source, and your error message reads, `Could not find pythonXX.lib`, ensure that you have consistent Python versions for common commands, such as `python` and `pip`. Clean all existing build files and rebuild ONNX again. onnx-onnx-bca0315/LICENSE000066400000000000000000000261361511334557700151040ustar00rootroot00000000000000 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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[![PyPI - Version](https://img.shields.io/pypi/v/onnx.svg)](https://pypi.org/project/onnx) [![CI](https://github.com/onnx/onnx/actions/workflows/main.yml/badge.svg)](https://github.com/onnx/onnx/actions/workflows/main.yml) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/3313/badge)](https://bestpractices.coreinfrastructure.org/projects/3313) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/onnx/onnx/badge)](https://api.securityscorecards.dev/projects/github.com/onnx/onnx) [![REUSE compliant](https://api.reuse.software/badge/github.com/onnx/onnx)](https://api.reuse.software/info/github.com/onnx/onnx) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![abi3 compatible](https://img.shields.io/badge/abi3-compatible-brightgreen)](https://docs.python.org/3/c-api/stable.html) [Open Neural Network Exchange (ONNX)](https://onnx.ai) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring). ONNX is [widely supported](http://onnx.ai/supported-tools) and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. We invite the community to join us and further evolve ONNX. # Use ONNX * [Documentation of ONNX Python Package](https://onnx.ai/onnx/) * [Tutorials for creating ONNX models](https://github.com/onnx/tutorials) * [Pre-trained ONNX models](https://github.com/onnx/models) # Learn about the ONNX spec * [Overview](https://github.com/onnx/onnx/blob/main/docs/Overview.md) * [ONNX intermediate representation spec](https://github.com/onnx/onnx/blob/main/docs/IR.md) * [Versioning principles of the spec](https://github.com/onnx/onnx/blob/main/docs/Versioning.md) * [Operators documentation](https://github.com/onnx/onnx/blob/main/docs/Operators.md) * [Operators documentation](https://onnx.ai/onnx/operators/index.html) (latest release) * [Python API Overview](https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md) # Programming utilities for working with ONNX Graphs * [Shape and Type Inference](https://github.com/onnx/onnx/blob/main/docs/ShapeInference.md) * [Graph Optimization](https://github.com/onnx/optimizer) * [Opset Version Conversion](https://github.com/onnx/onnx/blob/main/docs/docsgen/source/api/version_converter.md) # Contribute ONNX is a community project and the open governance model is described [here](https://github.com/onnx/onnx/blob/main/community/readme.md). We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the [Special Interest Groups](https://github.com/onnx/onnx/blob/main/community/sigs.md) and [Working Groups](https://github.com/onnx/onnx/blob/main/community/working-groups.md) to shape the future of ONNX. Check out our [contribution guide](https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md) to get started. If you think some operator should be added to ONNX specification, please read [this document](https://github.com/onnx/onnx/blob/main/docs/AddNewOp.md). # Community meetings The schedules of the regular meetings of the Steering Committee, the working groups and the SIGs can be found [here](https://onnx.ai/calendar) Community Meetups are held at least once a year. Content from previous community meetups are at: * 2020.04.09 * 2020.10.14 * 2021.03.24 * 2021.10.21 * 2022.06.24 * 2023.06.28 # Discuss We encourage you to open [Issues](https://github.com/onnx/onnx/issues), or use [Slack](https://lfaifoundation.slack.com/) (If you have not joined yet, please use this [link](https://join.slack.com/t/lfaifoundation/shared_invite/zt-o65errpw-gMTbwNr7FnNbVXNVFkmyNA) to join the group) for more real-time discussion. # Follow Us Stay up to date with the latest ONNX news. [[Facebook](https://www.facebook.com/onnxai/)] [[Twitter/X](https://twitter.com/onnxai)] # Roadmap A roadmap process takes place every year. More details can be found [here](https://github.com/onnx/steering-committee/tree/main/roadmap) # Installation ONNX released packages are published in PyPi. ```sh pip install onnx # or pip install onnx[reference] for optional reference implementation dependencies ``` [ONNX weekly packages](https://pypi.org/project/onnx-weekly/) are published in PyPI to enable experimentation and early testing. Detailed install instructions, including Common Build Options and Common Errors can be found [here](https://github.com/onnx/onnx/blob/main/INSTALL.md) # Python ABI3 Compatibility This package provides [abi3](https://docs.python.org/3/c-api/stable.html)-compatible wheels, allowing a single binary wheel to work across multiple Python versions (from 3.12 onwards). # Testing ONNX uses [pytest](https://docs.pytest.org) as test driver. In order to run tests, you will first need to install `pytest`: ```sh pip install pytest ``` After installing pytest, use the following command to run tests. ```sh pytest ``` # Development Check out the [contributor guide](https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md) for instructions. # Reproducible Builds (Linux) This project provides reproducible builds for Linux. A *reproducible build* means that the same source code will always produce identical binary outputs, no matter who builds it or where it is built. To achieve this, we use the [`SOURCE_DATE_EPOCH`](https://reproducible-builds.org/docs/source-date-epoch/) standard. This ensures that build timestamps and other time-dependent information are fixed, making the output bit-for-bit identical across different environments. ### Why this matters - **Transparency**: Anyone can verify that the distributed binaries were created from the published source code. - **Security**: Prevents tampering or hidden changes in the build process. - **Trust**: Users can be confident that the binaries they download are exactly what the maintainers intended. If you prefer, you can use the prebuilt reproducible binaries instead of building from source yourself. # License [Apache License v2.0](LICENSE) # Trademark Checkout [https://trademarks.justia.com](https://trademarks.justia.com/877/25/onnx-87725026.html) for the trademark. [General rules of the Linux Foundation on Trademark usage](https://www.linuxfoundation.org/legal/trademark-usage) # Code of Conduct [ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html) onnx-onnx-bca0315/RELEASE-MANAGEMENT.md000066400000000000000000000122501511334557700171030ustar00rootroot00000000000000 # ONNX release management This describes the process by which versions of ONNX are officially released to the public. Release Cadence --------------- Branch cuts for a new release are planned every 4 months. However, the times can be changed as required. | Minor Version | Release branch cut | Release date | First patch release date | | --- | --- | --- | --- | | 1.17.0 | XYZ | XYZ | Not planned | | 1.18.0 | Mar 2025 | May 2025 | Not planned | | 1.19.0 | 31. July 2025 | 27. August 2025 | 9. October 2025 | | 1.20.0 (tbd) | November 2025 | December 2025 | Not planned | Release Compatibility Matrix ---------------------------- *Support for a Python version that went eol will be discontinued in the following ONNX release.* *ONNX does NOT follow https://scientific-python.org/specs/spec-0000/ or https://protobuf.dev/support/version-support/* Changes are discussed in the community. Please do not hesitate to contact us if you have any requests. Planned changes for future releases as listed in the table below are subject to change. |ONNX version | Python wheels | C++ | Min Cmake Version | Min Protobuf | manylinux | | --- | --- | --- | --- | --- | --- | | 1.18 | 3.9-3.13, 3.13t (win, mac) | --- | 3.18 | v25.1 | manylinux2014 | | 1.19 | 3.9-3.13, 3.13t (win, mac, linux) | --- | 3.24 | v25.1 | manylinux2014 | | 1.19.1 | 3.9-3.13, 3.13t (win, mac, linux) | --- | 3.24 | v25.1 | manylinux2014 | | *1.20* | *3.10-3.13, 3.13t (win, mac, linux), 3.14* | --- | --- | *v25.1* | *manylinux2_28* | | *1.21* | | | | *manylinux2_28* | Releases -------- Releases are versioned according to [ONNX Versioning](docs/Versioning.md). This describes IR and operator versioning policies, as well as propose how models themselves should be versioned. On a regular basis, new versions of ONNX are published, representing the aggregate of changes in the IR and operator sets. Such releases use semantic versioning to describe the progression of the standard. The GitHub repo for ONNX provides release branches where the project is stabilized as per the process described here. Release notes are used to communicate the stability and status of a release. The main branch will be used to continue work for subsequent releases. Major, minor and patch releases will have branch names and version numbers reflecting the nature of the change as per semantic versioning definitions. Workflow -------- The following workflow describes the steps taken to release an update of ONNX, and can be undertaken regardless of whether a major, minor or patch release is to be produced. - The trigger for the workflow will typically be a time-based trigger based on elapsed time (say every three months). - The release manager will announce the intent of the process (to produce major, minor or patch update) and the overall timeline (documented in our wiki: [example](https://github.com/onnx/onnx/wiki/Logistics-for-ONNX-Release-1.19.0). A release branch is created with the name rel-major#.minor#(.patch#), and any version references in build scripts or version checks are updated. - The release manager announces the initial commit for testing. The first period lasts two weeks; any regressions found should be fixed, typically via the main branch. Incomplete features should be done or excised during this period. A distribution can be made available with an -RC1 suffix. - The release manager announces a second round of testing (unless it's only a patch update with no regressions found). Only critical bugs are fixed at this point, or those introduced by patches from the first week. A third week may be introduced at the release manager's discretion if significant fixes need to be taken. Distributions with -RCn suffixes can be made available if convenient. - Release notes are updated with final changes, and a file with sources is provided along with a release on the GitHub project page. Testing ------- The release process really consists of communicating, testing to establish a known state of the project, and distributing files. This section deals with the second task. At the very least, the tests that are part of the /test folder should be run under a variety of configurations. Issues fixed should ensure coverage in this suite to avoid regressions. Send a Pull Request for updates to this section to include a configuration you can help test if you care about one that's missing. The community is encouraged to perform additional testing during the test periods. Bugs and issues should be filed in the ONNX GitHub repo. # ONNX Weekly Builds on PyPI In addition to stable releases, we publish **weekly development builds** to a separate PyPI package: [`onnx-weekly`](https://pypi.org/project/onnx-weekly/). ## Why a Separate Package? - **Avoid accidental installs:** Pre-release versions can be installed unintentionally; `onnx-weekly` ensures stable users are unaffected. - **Enable safe testing:** Try upcoming features without impacting stable installs. Both packages can coexist. - **Simplify automation:** Weekly builds are pushed automatically from `main` without polluting the main release history. ## Installation ```bash pip install onnx-weekly onnx-onnx-bca0315/REUSE.toml000066400000000000000000000202261511334557700156510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 version = 1 SPDX-PackageName = "onnx" SPDX-PackageSupplier = "onnx-technical-discuss@lists.lfaidata.foundation" SPDX-PackageDownloadLocation = "onnx.ai" [[annotations]] path = "codecov.yml" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = [".github/**/**.md", ".github/pull_request_template.md"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ".github/**/**.yml" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/simple/**/test_data_set_0/**pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/node/**/test_data_set_0/**.pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/simple/**/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/pytorch-operator/**/test_data_set_0/**.pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/pytorch-operator/**/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/pytorch-operator/**/**.pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/node/**/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/pytorch-converted/**/test_data_set_0/**.pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/light/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/light/**.pb" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/real/**/**.json" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/pytorch-converted/**/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/backend/test/data/light/light/**.onnx" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/onnx-operators**", "onnx/onnx-ml**", "onnx/onnx-data**", "onnx/onnx**"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["docs/docsgen/source/api/**md", "docs/docsgen/source/_static/**", "docs/docsgen/source/requirements.txt", "docs/docsgen/source/onnx-favicon.png"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/backend/test/stat_coverage.py", "onnx/defs/gen_doc.py"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "docs/Change**.md" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "docs/Test**.md" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "docs/Operator**.md" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = [".gitignore", "**/**/.gitignore", ".gitmodules", ".lintrunner.toml", ".gitattributes", ".git-blame-ignore-revs", ".clang**", ".editorconfig"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["docs/docsgen/source/intro/images/**", "docs/docsgen/source/intro/**md"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["examples/**ipynb", "examples/resources/**"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["MANIFEST.in", "VERSION_NUMBER", "CODE_OF_CONDUCT.md", "CODEOWNERS", "pyproject**.toml", "requirements**.txt"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/onnx_cpp2py_export/**pyi", "onnx/py.typed"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/defs/**/**.cc", "onnx/defs/**/**.h"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "onnx/reference/ops/aionnxml/**py" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/defs/**.cc", "onnx/defs/**.h"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["cmake/ONNXConfig**.in", "CMakeLists.txt", "cmake/**cmake", "cmake/", "onnx/test/cmake/CMakeLists.txt"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = [".vscode/settings.json", "docs/docsgen/source/_templates/layout.html", "docs/docsgen/source/_templates/sidebar-nav-bs.html", "docs/images/onnx_hub_arch.svg", "docs/onnx-horizontal-color.png", "tools/protoc-gen-mypy.sh.in"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/gen_proto.py", "tools/protoc-gen-mypy.bat", "onnx/reference/op_run.py"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["onnx/backend/sample/ops/abs.py", "onnx/gen_proto.py"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["docs/proposals/images/composing_broadcast_axes.png"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = ["pixi.lock"] precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "CPPLINT.cfg" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" [[annotations]] path = "docs/docsgen/source/technical/**.png" precedence = "aggregate" SPDX-FileCopyrightText = "Copyright (c) ONNX Project Contributors" SPDX-License-Identifier = "Apache-2.0" onnx-onnx-bca0315/SECURITY.md000066400000000000000000000017041511334557700156620ustar00rootroot00000000000000 # Security Policy ## Reporting a Vulnerability If you think you have found a security vulnerability, please send a report to onnx-security@lists.lfaidata.foundation. Please do not post security vulnerabilities on Slack. We don't currently have a PGP key, unfortunately. An ONNX committer will send you a response indicating the next steps in handling your report. After the initial reply to your report, the committer will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance. Important: Please don't disclose the vulnerability before it has been fixed and announced, to protect our users. ## Security announcements Please subscribe to the [announcements mailing list](https://lists.lfaidata.foundation/g/onnx-announce), where we post notifications and remediation details for security vulnerabilities. onnx-onnx-bca0315/VERSION_NUMBER000066400000000000000000000000071511334557700162040ustar00rootroot000000000000001.20.0 onnx-onnx-bca0315/backend.py000066400000000000000000000031351511334557700160320ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 """PEP 517 build backend for onnx This is a thin wrapper over setuptools' PEP 517 build backend that automatically adds ``cmake`` to build dependencies if there is no CMake executable in PATH. This approach ensures that the package uses system CMake (that may contain downstream patches) when one is available, and pulls in the CMake package from PyPI when it is not. """ from __future__ import annotations import shutil from setuptools.build_meta import ( build_editable, build_sdist, build_wheel, get_requires_for_build_sdist, prepare_metadata_for_build_editable, prepare_metadata_for_build_wheel, ) from setuptools.build_meta import ( get_requires_for_build_editable as _get_requires_for_build_editable, ) from setuptools.build_meta import ( get_requires_for_build_wheel as _get_requires_for_build_wheel, ) __all__ = [ "build_editable", "build_sdist", "build_wheel", "get_requires_for_build_editable", "get_requires_for_build_sdist", "get_requires_for_build_wheel", "prepare_metadata_for_build_editable", "prepare_metadata_for_build_wheel", ] def _get_cmake_dep() -> list[str]: if shutil.which("cmake3") or shutil.which("cmake"): return [] return ["cmake>=3.18"] def get_requires_for_build_editable(*args, **kwargs) -> list[str]: return _get_requires_for_build_editable(*args, **kwargs) + _get_cmake_dep() def get_requires_for_build_wheel(*args, **kwargs) -> list[str]: return _get_requires_for_build_wheel(*args, **kwargs) + _get_cmake_dep() onnx-onnx-bca0315/cmake/000077500000000000000000000000001511334557700151475ustar00rootroot00000000000000onnx-onnx-bca0315/cmake/ONNXConfig.cmake.in000066400000000000000000000005071511334557700204700ustar00rootroot00000000000000# - Config file for the ONNX package # It defines ONNX targets for other cmake libraries to use. # library version information set(ONNX_VERSION "@ONNX_VERSION@") if(NOT @ONNX_USE_PROTOBUF_SHARED_LIBS@) find_package(Protobuf REQUIRED CONFIG) endif() # import targets include ("${CMAKE_CURRENT_LIST_DIR}/ONNXTargets.cmake") onnx-onnx-bca0315/cmake/ONNXConfigVersion.cmake.in000066400000000000000000000005441511334557700220370ustar00rootroot00000000000000set(PACKAGE_VERSION "@ONNX_VERSION@") # Check whether the requested PACKAGE_FIND_VERSION is compatible if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION) set(PACKAGE_VERSION_COMPATIBLE FALSE) else() set(PACKAGE_VERSION_COMPATIBLE TRUE) if (PACKAGE_VERSION VERSION_EQUAL PACKAGE_FIND_VERSION) set(PACKAGE_VERSION_EXACT TRUE) endif() endif() onnx-onnx-bca0315/cmake/Utils.cmake000066400000000000000000000015001511334557700172450ustar00rootroot00000000000000# SPDX-License-Identifier: Apache-2.0 # # Add MSVC RunTime Flag function(add_msvc_runtime_flag lib) if(ONNX_USE_MSVC_STATIC_RUNTIME) target_compile_options(${lib} PRIVATE $<$>:/MT> $<$:/MTd>) else() target_compile_options(${lib} PRIVATE $<$>:/MD> $<$:/MDd>) endif() endfunction() function(add_onnx_global_defines target) target_compile_definitions(${target} PUBLIC "ONNX_NAMESPACE=${ONNX_NAMESPACE}") if(ONNX_ML) target_compile_definitions(${target} PUBLIC "ONNX_ML=1") endif() if(ONNX_USE_LITE_PROTO) target_compile_definitions(${target} PUBLIC "ONNX_USE_LITE_PROTO=1") endif() if(ONNX_DISABLE_STATIC_REGISTRATION) target_compile_definitions(${target} PUBLIC "__ONNX_DISABLE_STATIC_REGISTRATION") endif() endfunction() onnx-onnx-bca0315/cmake/external/000077500000000000000000000000001511334557700167715ustar00rootroot00000000000000onnx-onnx-bca0315/cmake/external/FindSanitizer.cmake000066400000000000000000000121651511334557700225510ustar00rootroot00000000000000# SPDX-License-Identifier: Apache-2.0 # Find sanitizers # # This module sets the following targets: # Sanitizer::address # Sanitizer::thread # Sanitizer::undefined # Sanitizer::memory include_guard(GLOBAL) option(UBSAN_FLAGS "additional UBSAN flags" OFF) option(MSAN_FLAGS "additional MSAN flags" OFF) get_property(languages GLOBAL PROPERTY ENABLED_LANGUAGES) set(_source_code [==[ #include int main() { printf("hello world!"); return 0; } ]==]) set(_bug_address_code [==[ #include int main(int argc, char **argv) { int *array = (int*)malloc(100*sizeof(int)); array[0] = 0; int res = array[argc + 100]; // BOOM free(array); return res; } ]==]) set(_bug_undefined_code [==[ int main(int argc, char **argv) { int k = 0x7fffffff; k += argc; return 0; } ]==]) include(CMakePushCheckState) foreach(lang IN LISTS languages) if(lang STREQUAL C) include(CheckCSourceCompiles) include(CheckCSourceRuns) elseif(lang STREQUAL CXX) include(CheckCXXSourceCompiles) include(CheckCXXSourceRuns) else() continue() endif() foreach(sanitizer_name IN ITEMS address thread undefined memory) if(TARGET Sanitizer::${sanitizer_name}_${lang}) continue() endif() if(CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC") if(sanitizer_name STREQUAL "address") set(SANITIZER_FLAGS "/fsanitize=${sanitizer_name}") else() continue() endif() else() set(SANITIZER_FLAGS "-fsanitize=${sanitizer_name};-fno-omit-frame-pointer") endif() if(sanitizer_name STREQUAL "undefined" AND UBSAN_FLAGS) list(APPEND SANITIZER_FLAGS "${UBSAN_FLAGS}") endif() if(sanitizer_name STREQUAL "memory") list(APPEND SANITIZER_FLAGS "-fsanitize-memory-track-origins=2") if(MSAN_FLAGS) list(APPEND SANITIZER_FLAGS "${MSAN_FLAGS}") endif() endif() cmake_push_check_state(RESET) set(CMAKE_REQUIRED_QUIET ON) string(REPLACE ";" " " CMAKE_REQUIRED_FLAGS "${SANITIZER_FLAGS}") set(SANITIZER_LINK_FLAGS) if(CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC") list(APPEND SANITIZER_LINK_FLAGS "/INCREMENTAL:NO") else() list(APPEND SANITIZER_LINK_FLAGS "-fsanitize=${sanitizer_name}") endif() set(CMAKE_REQUIRED_LINK_OPTIONS "${SANITIZER_LINK_FLAGS}") unset(__res CACHE) if(lang STREQUAL C) if(CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC") check_c_source_compiles("${_source_code}" __res) else() check_c_source_runs("${_source_code}" __res) endif() else() if(CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC") check_cxx_source_compiles("${_source_code}" __res) else() check_cxx_source_runs("${_source_code}" __res) endif() endif() if(NOT __res) message(WARNING "Can't find ${sanitizer_name} in ${lang}") cmake_pop_check_state() continue() endif() unset(__res CACHE) if(NOT CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC" AND (sanitizer_name STREQUAL "address") OR (sanitizer_name STREQUAL "undefined")) set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} -fno-sanitize-recover=all") if(lang STREQUAL C) check_c_source_runs("${_bug_${sanitizer_name}_code}" __res) else() check_cxx_source_runs("${_bug_${sanitizer_name}_code}" __res) endif() if(__res) message(WARNING "Buffer overflow bug is not detected in ${lang} ${sanitizer_name}") cmake_pop_check_state() continue() endif() endif() add_library(Sanitizer::${sanitizer_name}_${lang} INTERFACE IMPORTED GLOBAL) if(NOT TARGET Sanitizer::${sanitizer_name}) add_library(Sanitizer::${sanitizer_name} INTERFACE IMPORTED GLOBAL) endif() target_link_libraries(Sanitizer::${sanitizer_name} INTERFACE Sanitizer::${sanitizer_name}_${lang}) foreach(SANITIZER_FLAG IN LISTS SANITIZER_FLAGS) target_compile_options( Sanitizer::${sanitizer_name}_${lang} INTERFACE $<$:${SANITIZER_FLAG}>) endforeach() foreach(SANITIZER_FLAG IN LISTS SANITIZER_LINK_FLAGS) target_link_options(Sanitizer::${sanitizer_name}_${lang} INTERFACE $<$:${SANITIZER_FLAG}>) endforeach() if(CMAKE_${lang}_COMPILER_ID STREQUAL "Clang") target_compile_options( Sanitizer::${sanitizer_name}_${lang} INTERFACE $<$:-shared-libsan>) endif() if(sanitizer_name STREQUAL "address" AND lang STREQUAL CXX) if(CMAKE_${lang}_COMPILER_ID STREQUAL "MSVC") target_compile_definitions( Sanitizer::${sanitizer_name}_${lang} INTERFACE $<$:_DISABLE_VECTOR_ANNOTATION> $<$:_DISABLE_STRING_ANNOTATION>) else() target_compile_definitions( Sanitizer::${sanitizer_name}_${lang} INTERFACE $<$:_GLIBCXX_SANITIZE_VECTOR> $<$:_GLIBCXX_SANITIZE_STD_ALLOCATOR>) endif() endif() cmake_pop_check_state() endforeach() endforeach() onnx-onnx-bca0315/cmake/external/googletest.cmake000066400000000000000000000007241511334557700221520ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 include(FetchContent) FetchContent_Declare( googletest # Specify the commit you depend on and update it regularly. URL https://github.com/google/googletest/releases/download/v1.17.0/googletest-1.17.0.tar.gz ) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) onnx-onnx-bca0315/cmake/summary.cmake000066400000000000000000000101471511334557700176510ustar00rootroot00000000000000# SPDX-License-Identifier: Apache-2.0 # Prints accumulated ONNX configuration summary function(onnx_print_configuration_summary) message(STATUS "") message(STATUS "******** Summary ********") message(STATUS " CMake version : ${CMAKE_VERSION}") message(STATUS " CMake command : ${CMAKE_COMMAND}") message(STATUS " System : ${CMAKE_SYSTEM_NAME}") message(STATUS " C++ compiler : ${CMAKE_CXX_COMPILER}") message(STATUS " C++ compiler version : ${CMAKE_CXX_COMPILER_VERSION}") message(STATUS " Build type : ${CMAKE_BUILD_TYPE}") message(STATUS " CMAKE_INSTALL_PREFIX : ${CMAKE_INSTALL_PREFIX}") if(CMAKE_MODULE_PATH) message(STATUS " CMAKE_MODULE_PATH : ${CMAKE_MODULE_PATH}") endif() message(STATUS "") message(STATUS " ONNX version : ${ONNX_VERSION}") message(STATUS " ONNX NAMESPACE : ${ONNX_NAMESPACE}") message(STATUS " ONNX_USE_LITE_PROTO : ${ONNX_USE_LITE_PROTO}") message(STATUS " ONNX_USE_PROTOBUF_SHARED_LIBS : ${ONNX_USE_PROTOBUF_SHARED_LIBS}") message(STATUS " ONNX_DISABLE_EXCEPTIONS : ${ONNX_DISABLE_EXCEPTIONS}") message(STATUS " ONNX_DISABLE_STATIC_REGISTRATION : ${ONNX_DISABLE_STATIC_REGISTRATION}") message(STATUS " ONNX_WERROR : ${ONNX_WERROR}") message(STATUS " ONNX_BUILD_TESTS : ${ONNX_BUILD_TESTS}") message(STATUS " ONNX_USE_UNITY_BUILD : ${ONNX_USE_UNITY_BUILD}") message(STATUS " BUILD_SHARED_LIBS : ${BUILD_SHARED_LIBS}") message(STATUS "") get_target_property(tmp onnx_object COMPILE_OPTIONS) message(STATUS " onnx compile options : ${tmp}") get_target_property(tmp onnx_proto_object COMPILE_OPTIONS) if(tmp) message(STATUS " onnx_proto compile options : ${tmp}") endif() get_target_property(tmp onnx_object COMPILE_DEFINITIONS) message(STATUS " onnx compile definitions : ${tmp}") get_target_property(tmp onnx_proto_object COMPILE_DEFINITIONS) message(STATUS " onnx_proto compile definitions : ${tmp}") get_target_property(tmp onnx_object LINK_OPTIONS) if(tmp) message(STATUS " onnx link options : ${tmp}") endif() get_target_property(tmp onnx_proto_object LINK_OPTIONS) if(tmp) message(STATUS " onnx_proto link options : ${tmp}") endif() get_target_property(tmp onnx_object LINK_LIBRARIES) if(tmp) message(STATUS " onnx link libraries : ${tmp}") endif() get_target_property(tmp onnx_proto_object LINK_LIBRARIES) if(tmp) message(STATUS " onnx_proto link libraries : ${tmp}") endif() message(STATUS "") message(STATUS " Protobuf version : ${Protobuf_VERSION}") if(EXISTS "${ONNX_PROTOC_EXECUTABLE}") message(STATUS " Protobuf compiler : ${ONNX_PROTOC_EXECUTABLE}") else() if(TARGET protobuf::protoc) get_target_property(tmp protobuf::protoc IMPORTED_LOCATION) if(tmp) message(STATUS " Protobuf compiler : ${tmp}") endif() endif() endif() get_target_property(tmp ${LINKED_PROTOBUF_TARGET} IMPORTED_LOCATION) if(tmp) message(STATUS " Protobuf libraries : ${tmp}") endif() message(STATUS " ONNX_BUILD_PYTHON : ${ONNX_BUILD_PYTHON}") if(ONNX_BUILD_PYTHON) message(STATUS " Python3 version : ${Python3_VERSION}") message(STATUS " Python3 executable : ${Python3_EXECUTABLE}") message(STATUS " Python3 includes : ${Python3_INCLUDE_DIRS}") message(STATUS " Python3 libraries : ${Python3_LIBRARIES}") if(Python3_PyPy_VERSION) message(STATUS " Python3 PyPy version : ${Python3_PyPy_VERSION}") endif() message(STATUS " Python3 interpreter ID : ${Python3_INTERPRETER_ID}") if(Python_SOABI) message(STATUS " Python SOABI : ${Python_SOABI}") endif() endif() endfunction() onnx-onnx-bca0315/cmake/unittest.cmake000066400000000000000000000013771511334557700200400ustar00rootroot00000000000000# SPDX-License-Identifier: Apache-2.0 include(CTest) set(ONNX_ROOT ${PROJECT_SOURCE_DIR}) set(UT_NAME ${PROJECT_NAME}_gtests) file(GLOB_RECURSE test_src "${ONNX_ROOT}/onnx/test/cpp/*.cc") add_executable(${UT_NAME} ${test_src}) find_package(Threads REQUIRED) target_link_libraries(${UT_NAME} PRIVATE onnx Threads::Threads) if(TARGET GTest::gtest) target_link_libraries(${UT_NAME} PRIVATE GTest::gtest) else() target_link_libraries(${UT_NAME} PRIVATE gtest) endif() set(TEST_ARGS) if(ONNX_GENERATE_TEST_REPORTS) # generate a report file next to the test program list( APPEND TEST_ARGS "--gtest_output=xml:$.$.results.xml>") endif() add_test(NAME ${UT_NAME} COMMAND ${UT_NAME} ${TEST_ARGS}) onnx-onnx-bca0315/codecov.yml000066400000000000000000000002021511334557700162260ustar00rootroot00000000000000coverage: status: project: default: informational: true patch: default: informational: true onnx-onnx-bca0315/community/000077500000000000000000000000001511334557700161135ustar00rootroot00000000000000onnx-onnx-bca0315/community/logo_request.md000066400000000000000000000020041511334557700211410ustar00rootroot00000000000000 ## Member Company logos Member Companies as defined [here](readme.md#community-roles) can request their logo be displayed on https://onnx.ai and other materials. To have your logo displayed, submit a PR to https://github.com/onnx/onnx.github.io with the following: 1. Text of the PR must include written permission indicating the logo can be used on the onnx.ai website as well as in presentations showing ONNX Member Companies 2. A high quality logo file with transparent background needs to be committed in the "assets" directory. The image file should be 300dpi (best if physical printing is ever required) or a vector file which can be saved in any resolution. 3. The URL of your company or product web page. Ideally the page mentions ONNX. Member Companies may ask for their logo to be removed at any time and their status as Member Company rescinded. The Steering Committee also can vote to remove a Member Company of their status. onnx-onnx-bca0315/community/readme.md000066400000000000000000000351211511334557700176740ustar00rootroot00000000000000 # ONNX Open Governance ## TL;DR ONNX is rolling out open governance to encourage broader participation beyond the founding companies. We hope this will make the decision making process more transparent, enable better technical decisions with consideration of more viewpoints, and share the work of maintenance. We want ONNX to be the standard the whole community rallies to without reservations. ONNX open governance creates 3 roles: Member, Contributor and Approver. 3 structures are also created: Steering Committee, Special Interest Groups (SIGs), Working Groups. Contributors and Approvers can vote for the Steering Committee members. The Steering Committee charters SIGs and appoints SIG chairs. Every piece of ONNX belongs to some SIG. Contributors and Approvers participate in one or more SIGs. Our governance structure is based on the successful model of Kubernetes. The effort is bootstrapped with an initial Steering Committee and set of SIGs with the first elections to occur after 1 year. ## Principles The ONNX community adheres to the following principles: * __Open__: ONNX is open source. See repository guidelines and DCO, below. * __Welcoming and respectful__: See Code of Conduct, below. * __Transparent and accessible__: Work and collaboration should be done in public. See SIG governance, below. * __Merit__: Ideas and contributions are accepted according to their technical merit and alignment with project objectives, scope and design principles. Engineering investment >> corporate sponsorship * __Speed__: Contributing the time and effort to ensure fast decision-making is key to ensuring that the specifications produced is aligned to the fast iteration of machine learning technologies. ## Community Roles ### Members Members are individuals who are interested in or participate in the ONNX community. Members are able to follow and participate in all public modes of communication used by the ONNX community including but not limited to GitHub, Slack, Stack Overflow, email announcements and discussion aliases. Members are expected to adhere to the Code of Conduct but do not have any specific responsibilities. ### Contributors Contributors are Members who are active contributors to the community. They can have issues and PRs assigned to them. They also have voting privileges. Contributors can be active in many ways including but not limited to: * Authoring or reviewing PRs on GitHub * Filing or commenting on issues on GitHub * Contributing to SIG, subproject, or community discussions (e.g. Slack, meetings, email discussion forums, Stack Overflow, etc) * Creator of content, promoting and advocating the ONNX specification A Member can become a Contributor by being sponsored by 2 existing Approvers from different companies. Contributors who are not active in the last 12 months will be removed. ### Approvers Approvers are Contributors who are experienced with some aspect of the project and with general software engineering principles. Approvers are responsible for reviewing contributions for acceptance by considering not just code quality but also holistic impact of the contribution including compatibility, performance, and interactions with other areas. Approvers need to be active Contributors for at least 3 months and be sponsored by a SIG chair with no objections from other SIG chairs. ### Member Companies Member Companies are organizations that support ONNX in one or more of the following ways: * Having employees participate in SIGs, Working Groups, or the Steering Committee * Hosting a workshop or meetup for ONNX * Providing resources for building or hosting ONNX assets * Doing media or PR activities to promote ONNX * Shipping a product that supports ONNX Member Companies do not have any voting rights, except via their employees who are Contributors. Affiliates and subsidiaries are considered part of the Member Company and not as separate organizations. Being a Member Company does not by itself confer any compliance or certification to the Member Company's products. Member Companies can request their logo be displayed on the website and other materials by following these [instructions](logo_request.md). ## Organizational Structure The ONNX community is organized in the following manner, with all governance and execution being planned and coordinated as follows: * **Steering Committee** is made up of a set number of people whose charter it is to define and iterate on the vision, goals, and governance process of the ONNX community. * **Special Interest Groups (SIGs)** are persistent groups that are responsible for specific parts of the project. SIGs must have open and transparent proceedings. Anyone is welcome to participate and contribute provided they follow the Code of Conduct. The purpose of a SIG is to develop a set of goals to be achieved over a set period of time, and then to gather input, drive consensus and closure, implement code contributions, and other related activities to achieve the goal. SIGs are also responsible for ongoing maintenance of the code in their areas. * **Working Groups** are temporary groups that are formed to address issues that cross SIG boundaries. Working groups do not own any code ownership or other long term artifacts. Working groups can report back and act through involved SIGs. ### Steering Committee #### Role The Steering Committee has a set of rights and responsibilities including the following: * Define, evolve, and defend the vision, values, mission, and scope of the project. * Define, evolve, and defend a Code of Conduct, which must include a neutral, unbiased process for resolving conflicts. * Define and evolve project governance structures and policies, including how members become contributors, approvers, SIG chairs, etc. * Charter and refine policy for defining new community groups (Special Interest Groups, Working Groups, and any future possible defined structure), and establish transparency and accountability policies for such groups. * Decide, for the purpose of elections, who is a member of standing of the ONNX project, and what privileges that entails. * Decide which functional areas and scope are part of the ONNX project, including accepting new or pruning old SIGs and Working Groups. * Decide how and when official releases of ONNX artifacts are made and what they include. * Declare releases when quality/feature/other requirements are met. * Control access to, establish processes regarding, and provide a final escalation path for any ONNX repository, which currently includes all repositories under the ONNX GitHub organizations * Control and delegate access to and establish processes regarding other project resources/assets, including artifact repositories, build and test infrastructure, web sites and their domains, blogs, social-media accounts, etc. * Define any certification process. * Manage the ONNX brand and any outbound marketing. * Make decisions by majority vote if consensus cannot be reached. #### Structure The Steering Committee consists of 5 individuals. No single Member Company may have more than 1 representative. Members serve 1 year terms. The starting composition will be individuals from Microsoft, Facebook, Amazon, and 2 other Member Companies, who have been picked by the three founding members based on contributions and experience. After the initial term of each Steering Committee representative is completed, their seat will be open for any contributor in the community to be elected into the seat via a community vote. Only contributors may vote, but would be restricted to one vote per Member Company. If a member of the Steering Committee changes companies, by default they retain and may continue on with the role. If the employment change results in a single Member Company having more than one representative, then one of them must resign. When there is a vacancy on the Steering Committee, the remaining members can appoint a new representative for the remainder of the term until the next election. The Steering Committee will decide on and publish an election process within 3 months of formalizing this organizational structure. This will cover voting eligibility, eligibility for candidacy, election process and schedule. During this time period, the Steering Committee will also establish SIGs and Working Groups. A Steering Committee member can be removed due to Code of Conduct violations. ### SIG - Special Interest Groups #### Role The ONNX project is organized primarily into Special Interest Groups, or SIGs. Each SIG is comprised of individuals from multiple companies and organizations, with a common purpose of advancing the project with respect to a specific topic. Our goal is to enable a distributed decision structure and code ownership, as well as providing focused forums for getting work done, making decisions, and on-boarding new contributors. Every identifiable part of the project (e.g., repository, subdirectory, API, test, issue, PR, Slack channel) is intended to be owned by some SIG. At the time of inception of this organizational structure, the following SIGs will be present: * Architecture & Infra * This SIG is responsible for defining and maintaining the core ONNX format, the build and CI/CD systems for ONNX repositories, publishing release packages for ONNX, and creating tools to help integrate with and test against the ONNX standard. This SIG is also the defacto owner of files in the main ONNX repository unless explicitly owned by another SIG. * Operator Standardization * This SIG is responsible for determining the operators that are part of the ONNX spec (ONNX and ONNX-ML domains), ensuring high quality operator definitions and documentation, establishing criteria for adding new operators, managing ops domains and compliance tiers, and enforcing versioning mechanisms. * Converters * This SIG is responsible for developing and maintaining the various converter repositories under ONNX. * Model zoo and tutorials * This SIG is responsible for the respective repositories with the charter of providing a comprehensive collection of state of the art ONNX models from a variety of sources and making it easy for users to get started with ONNX and the ecosystem around it. #### Structure SIGs must have at least one, and may have up to two SIG chairs at any given time. SIG chairs are intended to be organizers and facilitators, responsible for the operation of the SIG and for communication and coordination with the other SIGs, the Steering Committee, and the broader community. All SIG chairs are appointed by the Steering Committee. If there are more than two contributors being considered for a particular SIG, the Steering Committee will vote on and resolve who the chairs would be. Candidates need to be Approvers. Each SIG must have a charter that specifies its scope (topics, subsystems, code repos and directories), responsibilities, and areas of authority. Charters are submitted to the ONNX GitHub via PR for review and approval by the Steering Committee who will be looking to ensure the scope of the SIG as represented in the charter is reasonable. All SIGs are expected to follow the standards established by the Steering Committee for how Contributors are roles of authority/leadership are selected/granted, how decisions are made, and how conflicts are resolved. A primary reason that SIGs exist is as forums for collaboration. Much work in a SIG should stay local within that SIG. However, SIGs must communicate in the open, ensure other SIGs and community members can find meeting notes, discussions, designs, and decisions, and periodically communicate a high-level summary of the SIG's work to the community. SIGs are also responsible to: * Meet regularly, at least monthly * Keep up-to-date meeting notes, linked from the SIG's page in the community repo * Announce meeting agenda and minutes after each meeting, on their SIG mailing list and/or Slack channel * Ensure the SIG's mailing list is archived (i.e on GitHub) * Report activity in overall ONNX community meetings * Participate in release planning meetings, retrospectives, etc (if relevant) * Actively triage issues, PRs, test failures, etc. related to code and tests owned by the SIG * Use the above forums as the primary means of working, communicating, and collaborating, as opposed to private emails and meetings #### Decision making When it is time to formalize the work-product from a SIG, votes are taken from every contributor who participates in the SIG. The list of active contributors is determined by the one (or two) SIG leads to ensure that only those who have actively participated in the SIG can vote. At this time there are no restrictions on how many contributors from any one Member Company can participate (and hence vote). The Steering Committee will monitor how the community behaves and apply constraints if needed in the future. While most work shouldn’t require expensive coordination with other SIGs, there will be efforts (features, refactoring, etc.) that cross SIG boundaries. In this case, it is expected that the SIGs coordinate with each other and come to mutually agreed solutions. In some cases, it may make sense to form a Working Group for joint work. Cross-SIG coordination will naturally require more time and implies a certain amount of overhead. This is intentional to encourage changes to be well encapsulated whenever possible. ### WG - Working Groups Working Groups (WGs) are primarily used to facilitate topics of discussion that cross SIG lines, or are topics which are short-lived and require a limited set of decisions to be agreed upon. Working groups: * do not own code * have a clear goal measured through specific deliverables * will be disbanded after the goal is achieved Working Groups can create specifications, recommendations, or implementations for submission to the relevant SIGs for approval and acceptance. A list of all active, inactive, and completed working groups can be found in the [working-groups repository](https://github.com/onnx/working-groups) Working Groups are formed by submitting a proposal via PR to the Steering Committee. The proposal should cover: * what is the exact problem being worked on * what are the exit criteria * who are the chairs (up to 2) * what are the meeting and discussion mechanics Working Groups are disbanded when there is no activity for more than *3 months* or when the chair informs the Steering Committee. ## Repository Guidelines The current guidelines for all repos under ONNX github.org could be found [here](repo_guidelines.md). ## CLA / DCO As of October 2020, the CLA (https://cla-assistant.io/onnx/onnx) has been retired. All commits are subject to the DCO (https://www.developercertificate.com/) and need to be signed. onnx-onnx-bca0315/community/repo_guidelines.md000066400000000000000000000042311511334557700216120ustar00rootroot00000000000000 # Repositories under ONNX GitHub organization The ONNX GitHub organization contains a number of repositories. Every repository is owned by a SIG and the Steering Committee is responsible for managing these repos. Requests for creating, transferring, modifying, or archiving repositories can be made by filing an issue a request against https://github.com/onnx/steering-committee. ## Rules for all repos * Must be owned and managed by one of the ONNX SIGs or the Steering Committee * Must be actively maintained * Must adopt the ONNX Code of Conduct * Must adopt the standard ONNX license(s) [All code projects use the Apache 2.0 license. Documentation repositories must use the Creative Commons License version 4.0.] * Must adopt the ONNX DCO bot * Must adopt all ONNX automation (like static code analysis) * Must have CI or other automation in place for repos containing code to ensure quality * All OWNERS must be members of standing as defined by ability to vote in Steering Committee elections. ## Requirements for new, contributed repos We are happy to accept contributions as repos under the ONNX organization of new projects that meet the following requirements: * Project is closely related to ONNX * Adds value to the ONNX ecosystem * Determined to need a new repo rather than a folder in an existing repo * Applicable and usable by a wide set of ONNX users (for example, implemented support for multiple hardware backends at time of contribution or committment to do so soon after) * All contributors must have signed the ONNX DCO * Licenses of dependencies must be acceptable * Commitment to maintain the repo * Approval of the SIG that will own the repo * Approval of the Steering Committee If you want to contribute a repository, you should first work with the SIG that will own it. Then the SIG can work with the Steering Committee to finalize. ## Archiving repos Repositories that are inactive or unneeded will be archived. The SIG that owns the repo is responsible for deciding when it should be archived. SIGs should regularly validate the repos they own are still active and necessary. onnx-onnx-bca0315/community/sc-election-guidelines.md000066400000000000000000000122541511334557700227740ustar00rootroot00000000000000 # ONNX Steering Committee election guideline ## Introduction To encourage community participation and wider adoption in the industry, ONNX has introduced [open governance](https://github.com/onnx/onnx/wiki/Expanded-ONNX-Steering-Committee-Announced!) in March 2018. The governance has three defined structures to propel the development of ONNX project forward: [Steering Committee](/community/readme.md#steering-committee), [Special Interest Groups (SIGs)](/community/readme.md#sig---special-interest-groups), and [Working Groups (WGs)](/community/readme.md#wg---working-groups). While SIGs and WGs primarily focus on the technical roadmap of ONNX, the Steering Committee is responsible for setting the vision and governance process of the ONNX community. For the first year of its ONNX open governance, representatives from Facebook, Microsoft, AWS, Intel and Nvidia are chosen to serve as the ONNX Steering Committee to help guide the project. The Steering Committee will be elected by the [Contributors](/community/readme.md#community-roles) in its second year and will be re-elected every year. This document is created to provide guidelines for the election process to ensure maximum transparency and fairness. ## Timeline Candidate applications will be accepted in April, and the election will be held in May. The new term for Steering Committee begins on June 1st of the corresponding year. The following table outlines the schedule for the election process. | Schedule | Event | |:-------------|:--------------------| | 1st Monday of April| Application for Steering Committee candidates open. | | 3rd Monday of April| Candidates and their applications posted on github.| | 1st Monday of May| Election begins. | | 2nd Monday of May| Election closes, and votes counted. Election results announced in the same week.| | 3rd Monday of May| Previous Steering Committee to meet the newly elected Committee for official transition.| | June 1 | New term begins with elected Steering Committee. Steering Committee Emeritus members help with the transition for the month of June. | ## Eligibility ### Eligibility for Steering Committee candidacy Candidates will be self-nominated, and they do not necessarily need to be a [Contributor](/community/readme.md#community-roles) to the ONNX project. The duties of the Steering Committee extend beyond simply contributing code to the ONNX project. ### Eligibility for voting To participate in the Steering committee election, you must be a Contributor to the ONNX project. As defined in the community guideline, Contributor is sponsored by 2 approvers from different companies. Contributors are further required to submit their github handle, email address, and affiliated company name to be eligible for voting. Any Contributor who has not submitted their information by before April 31st will not be able to participate in the election. The Steering Committee is currently reviewing options for collecting contributor information, and the best option will be notified to the Contributors shortly. ## Candidacy process ## Voting process ### General election procedure In order to promote fairness, the Steering Committee has decided to limit 1 vote per Member Company. Contributors will be able to vote individually, but their votes will be rolled up to represent the vote of associated Member Company. This procedure will prevent large companies with lots of Contributors from dominating the election results. ### Voting mechanics and algorithm The election will use [Condorcet ranking](https://en.wikipedia.org/wiki/Condorcet_method) with [Schulze method](https://en.wikipedia.org/wiki/Schulze_method). Condorcet ranking allows voters to indicate ranked preference for candidates, and Schultz method provides an algorithm to tally the overall preference. For ONNX Steering Committee election, the Condorcet ranking with Schulze method will be performed twice. The individual Contributor votes gets tallied first to Member Companies, and the results of the Member Company votes are ranked again using the same method. ### Voting platform We will use Condorcet Internet Voting Service ([civs.cs.cornell.edu](https://civs.cs.cornell.edu/)) to collect votes from Contributors. After votes are casted, the results of individual votes will be uploaded to ONNX Github election directory to ensure transparency. ## Election officers and Steering Committee emeritus members ### Election officers Two election officers will be chosen from the current Steering committee to oversee the election process. They are responsible for overseeing the progress of the election and ensure the process is correctly implemented. Their duties include coordinating election as shown in the timeline above, tallying votes and announcing results for the ONNX community. ### Steering Committee emeritus members Two Steering Committee members will remain as emeritus members for the newly elected Committee to help with transition process for 1 month. If previous Steering Committee members are reelected, then they will guide the transition for the new members, and there will not be a separate Steering Committee emeritus members. onnx-onnx-bca0315/community/sigs.md000066400000000000000000000021161511334557700174020ustar00rootroot00000000000000 # SIGs - Special Interest Groups As described in the ONNX [governance](/community/readme.md#sig---special-interest-groups), Special Interest Groups (SIGs) are persistent groups responsible for specific parts of the project. SIGs have open and transparent proceedings to develop goals and implement code contributions. SIGs are also responsible for ongoing maintenance of the code in their areas. ## Joining a SIG If you are interested in participating, please [join the discussion](https://join.slack.com/t/lfaifoundation/shared_invite/zt-o65errpw-gMTbwNr7FnNbVXNVFkmyNA) in the respective Slack channels. Details about any upcoming meetings will also be shared in the Slack channels. SIG artifacts can be found in the [sigs repository](https://github.com/onnx/sigs). You can find the schedule of SIG meetings on the [LFX calendar](https://zoom-lfx.platform.linuxfoundation.org/meetings/lfai-onnx?view=month) ## Current SIGs The list of current sig is found [here](https://github.com/onnx/sigs#current-sigs). onnx-onnx-bca0315/community/working-groups.md000066400000000000000000000033621511334557700214360ustar00rootroot00000000000000 # Working Groups As described in the ONNX [governance](/community/readme.md#wg---working-groups), Working Groups (WGs) are temporary groups formed to address issues that cross SIG boundaries. Working Groups have a have a clear goal measured through specific deliverables and disband after the goal is achieved. Working groups do not own artifacts long term; they create specifications, recommendations, and/or code implementations for submission to the relevant SIGs for approval and acceptance. ## Proposing a new working group New Working Groups are created when there is sufficient interest in a topic area and someone volunteers to be the chair for the group and submits a proposal to the steering committee. The chair facilitates the discussion and helps synthesize proposals and decisions. ## Joining a working group Working Groups have most of their discussions on Slack. If you are interested in participating, please join the discussion in the respective Slack channels. Details about any upcoming meetings will also be shared in the Slack channel. Working Group artifacts can be found in the [working-groups repository](https://github.com/onnx/working-groups). You can find the schedule of meetings on the [LF AI wiki](https://onnx.ai/calendar) ## Active working groups The list of active working group is found [here](https://github.com/onnx/working-groups#active-working-groups). ## Completed working groups The list of completed working group is found [here](https://github.com/onnx/working-groups#completed-working-groups). ## Inactive working groups The list of inactive working group is found [here](https://github.com/onnx/working-groups#inactive-working-groups). onnx-onnx-bca0315/docs/000077500000000000000000000000001511334557700150175ustar00rootroot00000000000000onnx-onnx-bca0315/docs/AddNewOp.md000066400000000000000000000244201511334557700170040ustar00rootroot00000000000000 # Adding New Operator or Function to ONNX Or updating an existing operator to a new Opset version. ## Table of Contents - [Adding New Operator or Function to ONNX](#adding-new-operator-or-function-to-onnx) - [Table of Contents](#table-of-contents) - [Proposing and submitting a new operator or function to ONNX](#proposing-and-submitting-a-new-operator-or-function-to-onnx) - [4 steps to add an operator](#4-steps-to-add-an-operator) - [Step 1: Proposing a new operator/function](#step-1-proposing-a-new-operatorfunction) - [Step 2: Submit PR](#step-2-submit-pr) - [Example to Follow](#example-to-follow) - [Step 3: PR Review by Operators SIG](#step-3-pr-review-by-operators-sig) - [Sign-off](#sign-off) - [Step 4: ONNX release](#step-4-onnx-release) - [Updating an existing operator](#updating-an-existing-operator) - [Checklist](#checklist) - [Removing operator or function](#removing-operator-or-function) - [Removing operator](#removing-operator) - [Removing function](#removing-function) - [Document removing operator or function](#document-removing-operator-or-function) ## Proposing and submitting a new operator or function to ONNX Operators are the basic building blocks used to define ONNX models. With a rich set of operators, ONNX can describe most DNN and ML models from various frameworks. Functions enable expressing complex operators in terms of more primitive operators. The ONNX specification includes a core set of operators that enable many models. It is a non-goal to add all possible operators, however more operators are added as needed to cover evolving needs. In this document, we describe the process of accepting a new proposed operator and how to properly submit a new operator as part of ONNX standard. The goal is to improve on what we currently have based on our experience, learning and feedbacks we gathered from the community. ## 4 steps to add an operator 1. Decide what to propose 2. Submit PR for new operator/function 3. Review of PR by Operators SIG 4. Merging of PR and inclusion in next ONNX release ### Step 1: Proposing a new operator/function In order to propose a new operator/function, the following is needed: 1. If the operator can be expressed in terms of other ONNX operators, then it should be a function and not an operator (we have a function in ONNX : MeanVarianceNormalization). 2. If the operators can be split to new primitives, propose those primitives instead and make the operator a function. 3. Based on a model. This will help us understand the usage and that it solves an actual problem. For the case of the model being private or IP and can't be shared, the operator doesn't belong to the standard and should be implemented as custom OP. 4. The operator needs to be implemented by at-least one (well-known) framework. This help us to understand the actual behavior of the operator and its usage. 5. Operator signature and behavior: 1. If the operator is available in numpy, prefer numpy semantics. 2. If the operator is available in more than one frameworks, make sure that your design is general and cover those frameworks. 6. Prefer attributes over inputs. 7. The operator should not be made more complex than is required by the use-cases. However, the operator should be made as general as possible, as long as it does not make the implementation more complex. This requires carefully balancing generality and complexity. For example, generalizing from 3-D tensors to N-D tensors is straight-forward (implementation-wise) for some operators, but complex for other operators. The choice in such cases will be made based on the complexity of such a generalization. ### Step 2: Submit PR Once the criteria of proposing new operator/function has been satisfied, you will need to submit a PR for the new operator/function. Here the expectation of what the PR should include. The reviewer is expected to verify the completeness of the PR before signoff. 1. Description: 1. Write a detailed description about the operator, and its expected behavior. Pretty much, the description should be clear enough to avoid confusion between implementors. 2. Add an example in the description to illustrate the usage. 3. Add reference to the source of the operator in the corresponding framework in the description (if possible). 4. Write the mathematical formula or a pseudocode in the description. The core algorithm needs to be very clear. 2. Write a reference implementation in Python, this reference implementation should cover all the expected behavior of the operator. Only in extremely rare case, we will waive this requirement. 3. Operator version: check out our [versioning doc](/docs/Versioning.md#operator-versioning) 4. Write unit test, that cover main usage and corner cases. 1. The testing examples will be extracted to the doc. 2. We also generate binary data for it. 3. Example: [onnx/backend/test/case/node/abs.py](/onnx/backend/test/case/node/abs.py) 5. Write upgrade and downgrade tests: 1. Add at least one automatic upgrade test for your operator in [onnx/test/version_converter/automatic_upgrade_test.py](/onnx/test/version_converter/automatic_upgrade_test.py) using `_test_op_upgrade`. These tests create a given operator at a given opset version (usually the version the operator was introduced in) and test that the version converter is able to convert them to the highest available version. So for a new operator `_test_op_upgrade` will not test anything, but as soon as the operator gets updated in a future opset the test will automatically become nontrivial. 2. Similarly add at least one automatic downgrade test for your operator in [onnx/test/version_converter/automatic_downgrade_test.py](/onnx/test/version_converter/automatic_downgrade_test.py) using `_test_op_downgrade`. Specifying the current version so that once the op is updated at a higher opset version the test will ensure downward conversion is validated. 6. Update the documentation and generate the test data. 1. Running [the script](/tools/update_doc.sh). If you have files under `onnx/backend/test/data/node` which cannot be generated by the scripts from `onnx/backend/test/case/node`, please further use `python onnx/backend/test/cmd_tools.py generate-data --clean` to cleanup the directory and only preserve needed test data. to update the doc and generate the test data. 7. Shape Inference function 1. Please provide a shape inference function in cases where it is meaningful and applicable. 2. In cases where shape inference is not possible, it must have logic to perform rank inference at the very least (adding right amount of dimensions to the output shape) 3. Shape inference functions must be accompanied by unit tests ([onnx/test/shape_inference_test.py](/onnx/test/shape_inference_test.py)). 4. You can refer to the shape inference function for the `TopK` operator while implementing your own function ([onnx/defs/math/defs.cc](/onnx/defs/math/defs.cc)) #### Example to Follow [PR 1959](https://github.com/onnx/onnx/pull/1959) is a good example to follow. ### Step 3: PR Review by Operators SIG The [Operators SIG](https://github.com/onnx/sigs/tree/main/operators) is responsible for the operators/functions in the ONNX specification. The SIG regularly meets and reviews PRs. #### Sign-off At least two sign-off from the Operators SIG [contributors](https://github.com/onnx/onnx/tree/main/community#community-roles). ### Step 4: ONNX release Once the PR is reviewed and signed off by the Operators SIG, it will be merged. Your new operator/function will be part of the main branch and available to anyone building from source. These are not official releases. ONNX periodically releases official new versions that are a snapshot of the main branch. Your new operator/function will be part of that release. ## Updating an existing operator The definition of an existing operator may need to be updated when e.g. there are new scenarios or input types to support. The process is largely similar to that for creating a new operator. ### Checklist Use this checklist when updating an existing operator: https://github.com/onnx/onnx/wiki/Checklist-for-updating-an-existing-operator ## Removing operator or function There are a lot of reasons for removing existing ONNX operator or function, such us being replaced with different operator or can be decomposed by a set of other operators. This document describes the criteria of removing an existing ONNX operator from the standard. ### Removing operator Any operator in ONNX was added because it was required by a model and/or framework. In order to deprecate such an operator we need to do the following. - Operator can’t be deprecated unless there is a replacement. - Replacement can be a more general operator that supersedes the old one. - Or a set of primitive operators that together can implement the same functionality and behavior of the deprecated operator (Function). - If the deprecated operator can be decomposed by existing operators then it must be converted to a function. - If replacement isn’t in ONNX standard yet, then add the replacement operator or set of operators first. - Add a version adapter which turns the operator into its replacement for the version converter. Example: [onnx/version_converter/adapters/upsample_9_10.h](/onnx/version_converter/adapters/upsample_9_10.h) - No grace period is needed for deprecated operators. ### Removing function Function, by definition, is composed of ONNX primitives; however, function could have been accelerated by framework or runtime that support ONNX. So, removing function is not recommended, with the exception of adding another single function which supersedes its functionality. ### Document removing operator or function To make sure everyone is aware of the deprecation, the following need to happen: - Any removed operator or function from ONNX need to be mentioned in the release note. - Their old documentation needs to be updated to show the new replacement and the mapping between the old to the new. - Only `def.cc` need to be remove, `old.cc` will remain. - `old.cc` need to be updated with the mapping to the replacement. - ONNX checker need to be updated to error with a proper message. - All removed operators need to be appended at the end of the `operator.md` file. onnx-onnx-bca0315/docs/Broadcasting.md000066400000000000000000000062041511334557700177430ustar00rootroot00000000000000 # Broadcasting in ONNX In ONNX, element-wise operators can take inputs with different shape, as long as the input tensors are broadcastable to the same shape. ONNX supports two types of broadcasting: multidirectional broadcasting and unidirectional broadcasting. We will introduce these two types of broadcasting respectively in the following sections. ## Multidirectional Broadcasting In ONNX, a set of tensors are multidirectional broadcastable to the same shape if one of the following is true: - The tensors all have exactly the same shape. - The tensors all have the same number of dimensions and the length of each dimensions is either a common length or 1. - The tensors that have too few dimensions can have their shapes prepended with a dimension of length 1 to satisfy property 2. For example, the following tensor shapes are supported by multidirectional broadcasting: - shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar ==> shape(result) = (2, 3, 4, 5) - shape(A) = (2, 3, 4, 5), shape(B) = (5,), ==> shape(result) = (2, 3, 4, 5) - shape(A) = (4, 5), shape(B) = (2, 3, 4, 5), ==> shape(result) = (2, 3, 4, 5) - shape(A) = (1, 4, 5), shape(B) = (2, 3, 1, 1), ==> shape(result) = (2, 3, 4, 5) - shape(A) = (3, 4, 5), shape(B) = (2, 1, 1, 1), ==> shape(result) = (2, 3, 4, 5) Multidirectional broadcasting is the same as [Numpy's broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#general-broadcasting-rules). Multidirectional broadcasting is supported by the following operators in ONNX: - [Add](Operators.md#Add) - [And](Operators.md#And) - [Div](Operators.md#Div) - [Equal](Operators.md#Equal) - [Greater](Operators.md#Greater) - [Less](Operators.md#Less) - [Max](Operators.md#Max) - [Mean](Operators.md#Mean) - [Min](Operators.md#Min) - [Mul](Operators.md#Mul) - [Or](Operators.md#Or) - [Pow](Operators.md#Pow) - [Sub](Operators.md#Sub) - [Sum](Operators.md#Sum) - [Where](Operators.md#Where) - [Xor](Operators.md#Xor) ## Unidirectional Broadcasting In ONNX, tensor B is unidirectional broadcastable to tensor A if one of the following is true: - Tensor A and B both have exactly the same shape. - Tensor A and B all have the same number of dimensions and the length of each dimensions is either a common length or B's length is 1. - Tensor B has too few dimensions, and B can have its shapes prepended with a dimension of length 1 to satisfy property 2. When unidirectional broadcasting happens, the output's shape is the same as the shape of A (i.e., the larger shape of two input tensors). In the following examples, tensor B is unidirectional broadcastable to tensor A: - shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar ==> shape(result) = (2, 3, 4, 5) - shape(A) = (2, 3, 4, 5), shape(B) = (5,), ==> shape(result) = (2, 3, 4, 5) - shape(A) = (2, 3, 4, 5), shape(B) = (2, 1, 1, 5), ==> shape(result) = (2, 3, 4, 5) - shape(A) = (2, 3, 4, 5), shape(B) = (1, 3, 1, 5), ==> shape(result) = (2, 3, 4, 5) Unidirectional broadcasting is supported by the following operators in ONNX: - [Gemm](Operators.md#Gemm) - [PRelu](Operators.md#PRelu) onnx-onnx-bca0315/docs/CIPipelines.md000066400000000000000000000163061511334557700175130ustar00rootroot00000000000000 # ONNX CI Pipelines * CI pipelines matrix: | | When it runs | Test | |-------------------------------------------------------------------------------------------|--------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [CI / Test](/.github/workflows/main.yml) | Every PR | ONNX C++ tests Test doc generation Test proto generation Verify node test generation | | [Windows_No_Exception CI](/.github/workflows/win_no_exception_ci.yml) | Every PR | Only ONNX C++ tests Test selective schema loading | | [Lint / Optional Lint](/.github/workflows/lint.yml) | Every PR | Not required -- it shows lint warnings for suggestions in PR | | [Lint / Enforce style](/.github/workflows/lint.yml) | Every PR | Required linters Auto-generated files are up to date | | [WindowsRelease](/.github/workflows/release_win.yml) | Main branch Release branch Weekly(1) | Release Windows wheel Verify with different dependency versions - latest and min supported numpy version, latest and min supported protobuf version(2) Verify ONNX with the latest [ONNX Runtime PyPI package](https://pypi.org/project/onnxruntime/)(3). | | [LinuxRelease_aarch64](/.github/workflows/release_linux_aarch64.yml) | Main branch Release branch Weekly | Release Linux aarch64 wheel Verify with different dependency versions - latest numpy version, latest and min supported protobuf version Verify ONNX with the latest ONNX Runtime PyPI package | | [LinuxRelease_x86_64](/.github/workflows/release_linux_x86_64.yml) | Main branch Release branch Weekly | Release Linux x86_64 wheel Test TEST_HUB=1(4) Verify with different dependency versions - latest numpy version, latest and min supported protobuf version Verify ONNX with the latest ONNX Runtime PyPI package. | | [MacRelease](/.github/workflows/release_mac.yml) | Main branch Release branch Weekly | Release Mac wheel Verify with different dependency versions - latest numpy version, latest and min supported protobuf version Verify ONNX with the latest ONNX Runtime PyPI package. Test source distribution generation Test build with source distribution Release onnx-weekly source distribution | | [Weekly CI with the latest ONNX and ONNX Model Zoo](/.github/workflows/weekly_mac_ci.yml) | weekly(6) | Test latest ONNX checker Test latest ONNX shape inference With all models from [onnx/models](https://github.com/onnx/models)(7) | | [Reuse](/.github/workflows/reuse.yml) | Every PR | Checks for Copyright and License header More information could be found at: https://reuse.software/ If no license is to be added, or the checker does not recognize it, it must be configured under REUSE.toml. | | [Dependabot](/.github/dependabot.yml) | Main branch weekly | Create PRs for new dependency versions | Every PR * (1) When the release CIs will run: * After a PR has been merged into main/rel-* branch * Run weekly (Sunday midnight) and publish Python wheel to [onnx-weekly](https://pypi.org/project/onnx-weekly/) package on PyPI [2024.10.23: The current consideration is to delete the packages on pypi only due to running out of disk space. Starting with the oldest packages.] * Any PR targeting rel-* branch * To manually run them, add a PR label "run release CIs" (only maintainers have permission). * (2) Minimum supported versions are listed [here](/requirements.txt). * (3) [Test](/onnx/test/test_with_ort.py) ONNX Python wheel with `onnxruntime.InferenceSession` from latest ONNXRuntime. Please note that ONNX Runtime does not support Windows-x86 thus its verification is skipped. * (4) TEST_HUB=1 will test [onnx.hub](/onnx/test/hub_test.py) by using this API to download an ONNX model from onnx/models. This test is restricted to only 1 pipeline for saving quota usage. * (5) Although the build environment is macos-11, use MACOSX_DEPLOYMENT_TARGET=10.12 and -p [macosx_10_12_x86_64](https://github.com/onnx/onnx/blob/2e048660ffa8243596aaf3338e60c7c0575458f2/.github/workflows/release_mac.yml#L74) to force the wheel to support 10.12+. * (6): * The ONNX Model Zoo test will run weekly (Sunday midnight) * To manually trigger it, add a PR label "test ONNX Model Zoo" (only maintainers have permission). Please note that it will need a lot of bandwidth to download models through git-lfs API when loading models via [onnx.hub](/docs/Hub.md) so use it with caution. * (7) Some old deprecated models (opset-1) are [skipped](/workflow_scripts/config.py). onnx-onnx-bca0315/docs/Changelog-ml.md000066400000000000000000001565461511334557700176570ustar00rootroot00000000000000 ## Operator Changelog *This file is automatically generated from the [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py). Do not modify directly and instead edit operator definitions.* For an operator input/output's differentiability, it can be differentiable, non-differentiable, or undefined. If a variable's differentiability is not specified, that variable has undefined differentiability. # ai.onnx.ml ## Version 1 of the 'ai.onnx.ml' operator set ### **ai.onnx.ml.ArrayFeatureExtractor-1** Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Inputs
X : T
Data to be selected
Y : tensor(int64)
The indices, based on 0 as the first index of any dimension.
#### Outputs
Z : T
Selected output data as an array
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string)
The input must be a tensor of a numeric type or string. The output will be of the same tensor type.
### **ai.onnx.ml.Binarizer-1** Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
threshold : float (default is 0.0)
Values greater than this are mapped to 1, others to 0.
#### Inputs
X : T
Data to be binarized
#### Outputs
Y : T
Binarized output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type. The output will be of the same tensor type.
### **ai.onnx.ml.CastMap-1** Converts a map to a tensor.
The map key must be an int64 and the values will be ordered in ascending order based on this key.
The operator supports dense packing or sparse packing. If using sparse packing, the key cannot exceed the max_map-1 value. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cast_to : string (default is TO_FLOAT)
A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.
map_form : string (default is DENSE)
Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.
max_map : int (default is 1)
If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.
#### Inputs
X : T1
The input map that is to be cast to a tensor
#### Outputs
Y : T2
A tensor representing the same data as the input map, ordered by their keys
#### Type Constraints
T1 : map(int64, string), map(int64, float)
The input must be an integer map to either string or float.
T2 : tensor(string), tensor(float), tensor(int64)
The output is a 1-D tensor of string, float, or integer.
### **ai.onnx.ml.CategoryMapper-1** Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings, with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cats_int64s : list of ints
The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.
cats_strings : list of strings
The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence
default_int64 : int (default is -1)
An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
default_string : string (default is _Unused)
A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
#### Inputs
X : T1
Input data
#### Outputs
Y : T2
Output data. If strings are input, the output values are integers, and vice versa.
#### Type Constraints
T1 : tensor(string), tensor(int64)
The input must be a tensor of strings or integers, either [N,C] or [C].
T2 : tensor(string), tensor(int64)
The output is a tensor of strings or integers. Its shape will be the same as the input shape.
### **ai.onnx.ml.DictVectorizer-1** Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.
For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``, then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
int64_vocabulary : list of ints
An integer vocabulary array.
One and only one of the vocabularies must be defined.
string_vocabulary : list of strings
A string vocabulary array.
One and only one of the vocabularies must be defined.
#### Inputs
X : T1
A dictionary.
#### Outputs
Y : T2
A 1-D tensor holding values from the input dictionary.
#### Type Constraints
T1 : map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double)
The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.
T2 : tensor(int64), tensor(float), tensor(double), tensor(string)
The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.
### **ai.onnx.ml.FeatureVectorizer-1** Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C]. Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
inputdimensions : list of ints
The size of each input in the input list
#### Inputs (1 - ∞)
X (variadic) : T1
An ordered collection of tensors, all with the same element type.
#### Outputs
Y : tensor(float)
The output array, elements ordered as the inputs.
#### Type Constraints
T1 : tensor(int32), tensor(int64), tensor(float), tensor(double)
The input type must be a tensor of a numeric type.
### **ai.onnx.ml.Imputer-1** Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor holds floats, integers if the input tensor holds integers. The imputed values must all fit within the width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined, which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
imputed_value_floats : list of floats
Value(s) to change to
imputed_value_int64s : list of ints
Value(s) to change to.
replaced_value_float : float (default is 0.0)
A value that needs replacing.
replaced_value_int64 : int (default is 0)
A value that needs replacing.
#### Inputs
X : T
Data to be processed.
#### Outputs
Y : T
Imputed output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.
### **ai.onnx.ml.LabelEncoder-1** Converts strings to integers and vice versa.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
When converting from integers to strings, the string is fetched from the 'classes_strings' list, by simple indexing.
When converting from strings to integers, the string is looked up in the list and the index at which it is found is used as the converted value. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classes_strings : list of strings
A list of labels.
default_int64 : int (default is -1)
An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
default_string : string (default is _Unused)
A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
#### Inputs
X : T1
Input data.
#### Outputs
Y : T2
Output data. If strings are input, the output values are integers, and vice versa.
#### Type Constraints
T1 : tensor(string), tensor(int64)
The input type must be a tensor of integers or strings, of any shape.
T2 : tensor(string), tensor(int64)
The output type will be a tensor of strings or integers, and will have the same shape as the input.
### **ai.onnx.ml.LinearClassifier-1** Linear classifier #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_ints : list of ints
Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.
classlabels_strings : list of strings
Class labels when using string labels. One and only one 'classlabels' attribute must be defined.
coefficients : list of floats (required)
A collection of weights of the model(s).
intercepts : list of floats
A collection of intercepts.
multi_class : int (default is 0)
Indicates whether to do OvR or multinomial (0=OvR is the default).
post_transform : string (default is NONE)
Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
#### Inputs
X : T1
Data to be classified.
#### Outputs
Y : T2
Classification outputs (one class per example).
Z : tensor(float)
Classification scores ([N,E] - one score for each class and example
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]
T2 : tensor(string), tensor(int64)
The output will be a tensor of strings or integers.
### **ai.onnx.ml.LinearRegressor-1** Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
coefficients : list of floats
Weights of the model(s).
intercepts : list of floats
Weights of the intercepts, if used.
post_transform : string (default is NONE)
Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
targets : int (default is 1)
The total number of regression targets, 1 if not defined.
#### Inputs
X : T
Data to be regressed.
#### Outputs
Y : tensor(float)
Regression outputs (one per target, per example).
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.Normalizer-1** Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':

Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
norm : string (default is MAX)
One of 'MAX,' 'L1,' 'L2'
#### Inputs
X : T
Data to be encoded, a tensor of shape [N,C] or [C]
#### Outputs
Y : tensor(float)
Encoded output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.OneHotEncoder-1** Replace each input element with an array of ones and zeros, where a single one is placed at the index of the category that was passed in. The total category count will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8, the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast to integers and the cats_int64s category list will be used for the lookups. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cats_int64s : list of ints
List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.
cats_strings : list of strings
List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.
zeros : int (default is 1)
If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.
#### Inputs
X : T
Data to be encoded.
#### Outputs
Y : tensor(float)
Encoded output data, having one more dimension than X.
#### Type Constraints
T : tensor(string), tensor(int64), tensor(int32), tensor(float), tensor(double)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.SVMClassifier-1** Support Vector Machine classifier #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_ints : list of ints
Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.
coefficients : list of floats
kernel_params : list of floats
List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
kernel_type : string (default is LINEAR)
The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
prob_a : list of floats
First set of probability coefficients.
prob_b : list of floats
Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.
rho : list of floats
support_vectors : list of floats
vectors_per_class : list of ints
#### Inputs
X : T1
Data to be classified.
#### Outputs
Y : T2
Classification outputs (one class per example).
Z : tensor(float)
Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type, either [C] or [N,C].
T2 : tensor(string), tensor(int64)
The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the batch size of the input.
### **ai.onnx.ml.SVMRegressor-1** Support Vector Machine regression prediction and one-class SVM anomaly detection. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
coefficients : list of floats
Support vector coefficients.
kernel_params : list of floats
List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
kernel_type : string (default is LINEAR)
The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
n_supports : int (default is 0)
The number of support vectors.
one_class : int (default is 0)
Flag indicating whether the regression is a one-class SVM or not.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'
rho : list of floats
support_vectors : list of floats
Chosen support vectors
#### Inputs
X : T
Data to be regressed.
#### Outputs
Y : tensor(float)
Regression outputs (one score per target per example).
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type, either [C] or [N,C].
### **ai.onnx.ml.Scaler-1** Rescale input data, for example to standardize features by removing the mean and scaling to unit variance. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
offset : list of floats
First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
scale : list of floats
Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'
#### Inputs
X : T
Data to be scaled.
#### Outputs
Y : tensor(float)
Scaled output data.
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.TreeEnsembleClassifier-1** Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
base_values : list of floats
Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
class_ids : list of ints
The index of the class list that each weight is for.
class_nodeids : list of ints
node id that this weight is for.
class_treeids : list of ints
The id of the tree that this node is in.
class_weights : list of floats
The weight for the class in class_id.
classlabels_int64s : list of ints
Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.
nodes_falsenodeids : list of ints
Child node if expression is false.
nodes_featureids : list of ints
Feature id for each node.
nodes_hitrates : list of floats
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the default value is false (0) for all nodes.
nodes_modes : list of strings
The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'
nodes_nodeids : list of ints
Node id for each node. Ids may restart at zero for each tree, but it not required to.
nodes_treeids : list of ints
Tree id for each node.
nodes_truenodeids : list of ints
Child node if expression is true.
nodes_values : list of floats
Thresholds to do the splitting on for each node.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'
#### Inputs
X : T1
Input of shape [N,F]
#### Outputs
Y : T2
N, Top class for each point
Z : tensor(float)
The class score for each class, for each point, a tensor of shape [N,E].
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type.
T2 : tensor(string), tensor(int64)
The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.
### **ai.onnx.ml.TreeEnsembleRegressor-1** Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
aggregate_function : string (default is SUM)
Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'
base_values : list of floats
Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
n_targets : int
The total number of targets.
nodes_falsenodeids : list of ints
Child node if expression is false
nodes_featureids : list of ints
Feature id for each node.
nodes_hitrates : list of floats
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.
nodes_modes : list of strings
The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'
nodes_nodeids : list of ints
Node id for each node. Node ids must restart at zero for each tree and increase sequentially.
nodes_treeids : list of ints
Tree id for each node.
nodes_truenodeids : list of ints
Child node if expression is true
nodes_values : list of floats
Thresholds to do the splitting on for each node.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
target_ids : list of ints
The index of the target that each weight is for
target_nodeids : list of ints
The node id of each weight
target_treeids : list of ints
The id of the tree that each node is in.
target_weights : list of floats
The weight for each target
#### Inputs
X : T
Input of shape [N,F]
#### Outputs
Y : tensor(float)
N classes
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type.
### **ai.onnx.ml.ZipMap-1** Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes. Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
#### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_int64s : list of ints
The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.
#### Inputs
X : tensor(float)
The input values
#### Outputs
Z : T
The output map
#### Type Constraints
T : seq(map(string, float)), seq(map(int64, float))
The output will be a sequence of string or integer maps to float.
## Version 2 of the 'ai.onnx.ml' operator set ### **ai.onnx.ml.LabelEncoder-2** Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
For key look-up, bit-wise comparison is used so even a float NaN can be mapped to a value in 'values_*' attribute.
#### Version This version of the operator has been available since version 2 of the 'ai.onnx.ml' operator set. #### Attributes
default_float : float (default is -0.0)
A float.
default_int64 : int (default is -1)
An integer.
default_string : string (default is _Unused)
A string.
keys_floats : list of floats
A list of floats.
keys_int64s : list of ints
A list of ints.
keys_strings : list of strings
A list of strings. One and only one of 'keys_*'s should be set.
values_floats : list of floats
A list of floats.
values_int64s : list of ints
A list of ints.
values_strings : list of strings
A list of strings. One and only one of 'value_*'s should be set.
#### Inputs
X : T1
Input data. It can be either tensor or scalar.
#### Outputs
Y : T2
Output data.
#### Type Constraints
T1 : tensor(string), tensor(int64), tensor(float)
The input type is a tensor of any shape.
T2 : tensor(string), tensor(int64), tensor(float)
Output type is determined by the specified 'values_*' attribute.
## Version 3 of the 'ai.onnx.ml' operator set ### **ai.onnx.ml.TreeEnsembleClassifier-3** Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. #### Version This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set. #### Attributes
base_values : list of floats
Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
base_values_as_tensor : tensor
Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
class_ids : list of ints
The index of the class list that each weight is for.
class_nodeids : list of ints
node id that this weight is for.
class_treeids : list of ints
The id of the tree that this node is in.
class_weights : list of floats
The weight for the class in class_id.
class_weights_as_tensor : tensor
The weight for the class in class_id.
classlabels_int64s : list of ints
Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.
nodes_falsenodeids : list of ints
Child node if expression is false.
nodes_featureids : list of ints
Feature id for each node.
nodes_hitrates : list of floats
Popularity of each node, used for performance and may be omitted.
nodes_hitrates_as_tensor : tensor
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the default value is false (0) for all nodes.
nodes_modes : list of strings
The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'
nodes_nodeids : list of ints
Node id for each node. Ids may restart at zero for each tree, but it not required to.
nodes_treeids : list of ints
Tree id for each node.
nodes_truenodeids : list of ints
Child node if expression is true.
nodes_values : list of floats
Thresholds to do the splitting on for each node.
nodes_values_as_tensor : tensor
Thresholds to do the splitting on for each node.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'
#### Inputs
X : T1
Input of shape [N,F]
#### Outputs
Y : T2
N, Top class for each point
Z : tensor(float)
The class score for each class, for each point, a tensor of shape [N,E].
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type.
T2 : tensor(string), tensor(int64)
The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.
### **ai.onnx.ml.TreeEnsembleRegressor-3** Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF #### Version This version of the operator has been available since version 3 of the 'ai.onnx.ml' operator set. #### Attributes
aggregate_function : string (default is SUM)
Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'
base_values : list of floats
Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)
base_values_as_tensor : tensor
Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)
n_targets : int
The total number of targets.
nodes_falsenodeids : list of ints
Child node if expression is false
nodes_featureids : list of ints
Feature id for each node.
nodes_hitrates : list of floats
Popularity of each node, used for performance and may be omitted.
nodes_hitrates_as_tensor : tensor
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.
nodes_modes : list of strings
The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'
nodes_nodeids : list of ints
Node id for each node. Node ids must restart at zero for each tree and increase sequentially.
nodes_treeids : list of ints
Tree id for each node.
nodes_truenodeids : list of ints
Child node if expression is true
nodes_values : list of floats
Thresholds to do the splitting on for each node.
nodes_values_as_tensor : tensor
Thresholds to do the splitting on for each node.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
target_ids : list of ints
The index of the target that each weight is for
target_nodeids : list of ints
The node id of each weight
target_treeids : list of ints
The id of the tree that each node is in.
target_weights : list of floats
The weight for each target
target_weights_as_tensor : tensor
The weight for each target
#### Inputs
X : T
Input of shape [N,F]
#### Outputs
Y : tensor(float)
N classes
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type.
## Version 4 of the 'ai.onnx.ml' operator set ### **ai.onnx.ml.LabelEncoder-4** Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. Note that the 'keys_*' and 'values_*' attributes must have the same length. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value. The type of the 'default_*' attribute must match the 'values_*' attribute chosen.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
Float keys with value 'NaN' match any input 'NaN' value regardless of bit value. If a key is repeated, the last key takes precedence. #### Version This version of the operator has been available since version 4 of the 'ai.onnx.ml' operator set. #### Attributes
default_float : float (default is -0.0)
A float.
default_int64 : int (default is -1)
An integer.
default_string : string (default is _Unused)
A string.
default_tensor : tensor
A default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.
keys_floats : list of floats
A list of floats.
keys_int64s : list of ints
A list of ints.
keys_strings : list of strings
A list of strings.
keys_tensor : tensor
Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.
values_floats : list of floats
A list of floats.
values_int64s : list of ints
A list of ints.
values_strings : list of strings
A list of strings.
values_tensor : tensor
Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.
#### Inputs
X : T1
Input data. It must have the same element type as the keys_* attribute set.
#### Outputs
Y : T2
Output data. This tensor's element type is based on the values_* attribute set.
#### Type Constraints
T1 : tensor(string), tensor(int64), tensor(float), tensor(int32), tensor(int16), tensor(double)
The input type is a tensor of any shape.
T2 : tensor(string), tensor(int64), tensor(float), tensor(int32), tensor(int16), tensor(double)
Output type is determined by the specified 'values_*' attribute.
## Version 5 of the 'ai.onnx.ml' operator set ### **ai.onnx.ml.TreeEnsemble-5** Tree Ensemble operator. Returns the regressed values for each input in a batch. Inputs have dimensions `[N, F]` where `N` is the input batch size and `F` is the number of input features. Outputs have dimensions `[N, num_targets]` where `N` is the batch size and `num_targets` is the number of targets, which is a configurable attribute. The encoding of this attribute is split along interior nodes and the leaves of the trees. Notably, attributes with the prefix `nodes_*` are associated with interior nodes, and attributes with the prefix `leaf_*` are associated with leaves. The attributes `nodes_*` must all have the same length and encode a sequence of tuples, as defined by taking all the `nodes_*` fields at a given position. All fields prefixed with `leaf_*` represent tree leaves, and similarly define tuples of leaves and must have identical length. This operator can be used to implement both the previous `TreeEnsembleRegressor` and `TreeEnsembleClassifier` nodes. The `TreeEnsembleRegressor` node maps directly to this node and requires changing how the nodes are represented. The `TreeEnsembleClassifier` node can be implemented by adding a `ArgMax` node after this node to determine the top class. To encode class labels, a `LabelEncoder` or `GatherND` operator may be used. #### Version This version of the operator has been available since version 5 of the 'ai.onnx.ml' operator set. #### Attributes
aggregate_function : int (default is 1)
Defines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)
leaf_targetids : list of ints (required)
The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).
leaf_weights : tensor (required)
The weight for each leaf.
membership_values : tensor
Members to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.
n_targets : int
The total number of targets.
nodes_falseleafs : list of ints (required)
1 if false branch is leaf for each node and 0 if an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry
nodes_falsenodeids : list of ints (required)
If `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.
nodes_featureids : list of ints (required)
Feature id for each node.
nodes_hitrates : tensor
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.
nodes_modes : tensor (required)
The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.
nodes_splits : tensor (required)
Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.
nodes_trueleafs : list of ints (required)
1 if true branch is leaf for each node and 0 an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry
nodes_truenodeids : list of ints (required)
If `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.
post_transform : int (default is 0)
Indicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)
tree_roots : list of ints (required)
Index into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.
#### Inputs
X : T
Input of shape [Batch Size, Number of Features]
#### Outputs
Y : T
Output of shape [Batch Size, Number of targets]
#### Type Constraints
T : tensor(float), tensor(double), tensor(float16)
The input type must be a tensor of a numeric type.
### **ai.onnx.ml.TreeEnsembleClassifier-5** (deprecated) This operator is DEPRECATED. Please use TreeEnsemble with provides similar functionality. In order to determine the top class, the ArgMax node can be applied to the output of TreeEnsemble. To encode class labels, use a LabelEncoder operator. Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. #### Version This version of the operator has been deprecated since version 5 of the 'ai.onnx.ml' operator set. ### **ai.onnx.ml.TreeEnsembleRegressor-5** (deprecated) This operator is DEPRECATED. Please use TreeEnsemble instead which provides the same functionality.
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF #### Version This version of the operator has been deprecated since version 5 of the 'ai.onnx.ml' operator set. onnx-onnx-bca0315/docs/Changelog.md000066400000000000000000052443121511334557700172430ustar00rootroot00000000000000 ## Operator Changelog *This file is automatically generated from the [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py). Do not modify directly and instead edit operator definitions.* For an operator input/output's differentiability, it can be differentiable, non-differentiable, or undefined. If a variable's differentiability is not specified, that variable has undefined differentiability. # ai.onnx (default) ## Version 1 of the default ONNX operator set ### **Abs-1** Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Add-1** Performs element-wise binary addition (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **And-1** Returns the tensor resulted from performing the `and` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **ArgMax-1** Computes the indices of the max elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **ArgMin-1** Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **AveragePool-1** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements exclude pad. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **BatchNormalization-1** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints (required)
legacy optimization attribute.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero, default is 1e-5f.
is_test : int (default is 0)
If set to nonzero, run spatial batch normalization in test mode, default is 0.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.
spatial : int (default is 1)
If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.
#### Inputs
X : T
The input 4-dimensional tensor of shape NCHW.
scale : T
The scale as a 1-dimensional tensor of size C to be applied to the output.
B : T
The bias as a 1-dimensional tensor of size C to be applied to the output.
mean : T
The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.
var : T
The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.
#### Outputs (1 - 5)
Y : T
The output 4-dimensional tensor of the same shape as X.
mean (optional) : T
The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.
var (optional) : T
The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.
saved_mean (optional) : T
Saved mean used during training to speed up gradient computation. Should not be used for testing.
saved_var (optional) : T
Saved variance used during training to speed up gradient computation. Should not be used for testing.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cast-1** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. NOTE: Casting to and from strings is not supported yet. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
to : string (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input : T1
Input tensor to be cast.
#### Outputs
output : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain input types. Casting from strings and complex are not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain output types. Casting to strings and complex are not supported.
### **Ceil-1** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Clip-1** Clip operator limits the given input within an interval. The interval is specified with arguments 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max() respectively. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
max : float
Maximum value, above which element is replaced by max
min : float
Minimum value, under which element is replaced by min
#### Inputs
input : T
Input tensor whose elements to be clipped
#### Outputs
output : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Concat-1** Concatenate a list of tensors into a single tensor #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
Which axis to concat on. Default value is 1.
#### Inputs (1 - ∞)
inputs (variadic) : T
List of tensors for concatenation
#### Outputs
concat_result : T
Concatenated tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **Constant-1** A constant tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
value : tensor (required)
The value for the elements of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Conv-1** The convolution operator consumes an input tensor and a filter, and computes the output. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
dilations : list of ints
dilation value along each spatial axis of the filter.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs (2 - 3)
X : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
W : T
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
B (optional) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ConvTranspose-1** The convolution transpose operator consumes an input tensor and a filter, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
dilations : list of ints
dilation value along each spatial axis of the filter.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding : list of ints
The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.
output_shape : list of ints
The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs (2 - 3)
X : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
W : T
The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
B (optional) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **DepthToSpace-1** DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
#### Inputs
input : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output : T
Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Div-1** Performs element-wise binary division (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Dropout-1** Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
is_test : int (default is 0)
(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.
ratio : float (default is 0.5)
(float, default 0.5) the ratio of random dropout
#### Inputs
data : T
The input data as Tensor.
#### Outputs (1 - 2)
output : T
The output.
mask (optional) : T
The output mask. If is_test is nonzero, this output is not filled.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Elu-1** Elu takes one input data (Tensor) and produces one output data (Tensor) where the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Coefficient of ELU default to 1.0.
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Equal-1** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(int32), tensor(int64)
Constrain input to integral tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Exp-1** Calculates the exponential of the given input tensor, element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
input : T
Input tensor
#### Outputs
output : T
The exponential of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Flatten-1** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input : T
A tensor of rank >= axis.
#### Outputs
output : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Floor-1** Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GRU-1** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is foward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
output_sequence : int (default is 0)
The sequence output for the hidden is optional if 0. Default 0.
#### Inputs (3 - 6)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Gather-1** Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). Example 1: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` Example 2: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]
#### Inputs
data : T
Tensor of rank r >= 1.
indices : Tind
Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.
#### Outputs
output : T
Tensor of rank q + (r - 1).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **Gemm-1** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
beta : float (default is 1.0)
Scalar multiplier for input tensor C, the default value is 1.0.
broadcast : int (default is 0)
Whether C should be broadcasted
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs
A : T
Input tensor A
B : T
Input tensor B
C : T
Input tensor C, can be inplace.
#### Outputs
Y : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalAveragePool-1** GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalLpPool-1** GlobalLpPool consumes an input tensor X and applies lp pool pooling across the the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
p : float (default is 2.0)
p value of the Lp norm used to pool over the input data, default is 2.0.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y : T
Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalMaxPool-1** GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Greater-1** Returns the tensor resulted from performing the `greater` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input to float tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **HardSigmoid-1** HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.2)
Value of alpha default to 0.2
beta : float (default is 0.5)
Value of beta default to 0.5
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Hardmax-1** The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the hardmax values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Identity-1** Identity operator #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
Tensor to copy input into.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **If-1** If conditional #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
B : tensor(bool)
Only bool
### **InstanceNormalization-1** Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero, default is 1e-5f.
#### Inputs
input : T
The input 4-dimensional tensor of shape NCHW.
scale : T
The input 1-dimensional scale tensor of size C.
B : T
The input 1-dimensional bias tensor of size C.
#### Outputs
output : T
The output 4-dimensional tensor of the same shape as input.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LRN-1** Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor of shape (N x C x D1 x D2, ..., Dk), its region is {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}. square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)). Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.0001)
Scaling parameter.
beta : float (default is 0.75)
The exponent.
bias : float (default is 1.0)
size : int (required)
The number of channels to sum over
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y : T
Output tensor, which has the shape and type as input tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LSTM-1** Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
input_forget : int (default is 0)
Couple the input and forget gates if 1, default 0.
output_sequence : int (default is 0)
The sequence output for the hidden is optional if 0. Default 0.
#### Inputs (3 - 8)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional) : T
Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional) : T
The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
#### Outputs (0 - 3)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional) : T
The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **LeakyRelu-1** LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.01)
Coefficient of leakage default to 0.01.
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Less-1** Returns the tensor resulted from performing the `less` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input to float tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Log-1** Calculates the natural log of the given input tensor, element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
input : T
Input tensor
#### Outputs
output : T
The natural log of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LogSoftmax-1** The operator computes the logsoftmax (log of softmax) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the logsoftmax values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Loop-1** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] %keepgoing[BOOL, scalar] %b[INT32, scalar] ) { %my_local = Add(%a, %b) %b_out = Sub(%a, %b) %keepgoing_out = Greater(%my_local, %b_out) %user_defined_vals = Add(%b, %b) return %keepgoing_out, %b_out, %user_defined_vals } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ for (int i=0; i < max_trip_count && keepgoing; ++i) { /* User-defined code (loop body) */ int my_local = a + b; // Reading values in the enclosing scope is fine b = a - b; // writes fine if we specify b as a loop-carried dependency keepgoing = my_local > b; // keepgoing is a loop-carried dependency user_defined_vals[i] = b + b; /* End user-defined code */ } // my_local = 123; // Can't do this. my_local was defined in the body // These below values are live-out from the loop and therefore accessible b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable a here) are in scope and can be referenced in the inputs of the loop. 2) Any variables which you wish to make available in the enclosing scope (i.e. the variables b and keepgoing) must be declared as either loop-carried dependencies (both at the op inputs and output and at the body net input and output) or scan_outputs. 3) Values created in the body cannot be accessed in the enclosing scope. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (3 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **LpNormalization-1** Given a matrix, apply Lp-normalization along the provided axis. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The axis on which to apply normalization, -1 mean last axis.
p : int (default is 2)
The order of the normalization, only 1 or 2 are supported.
#### Inputs
input (differentiable) : T
Input matrix
#### Outputs
output (differentiable) : T
Matrix after normalization
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LpPool-1** LpPool consumes an input tensor X and applies Lp pooling across the the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.
kernel_shape : list of ints
The size of the kernel along each axis.
p : float (default is 2.0)
p value of the Lp norm used to pool over the input data, default is 2.0.
pads : list of ints
Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.
strides : list of ints
Stride along each axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MatMul-1** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
A : T
N-dimensional matrix A
B : T
N-dimensional matrix B
#### Outputs
Y : T
Matrix multiply results from A * B
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Max-1** Element-wise max of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Max.
#### Outputs
max : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxPool-1** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxRoiPool-1** ROI max pool consumes an input tensor X and region of interests (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
pooled_shape : list of ints (required)
ROI pool output shape (height, width).
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois (non-differentiable) : T
RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].
#### Outputs
Y (differentiable) : T
RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Mean-1** Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Mean.
#### Outputs
mean : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Min-1** Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Min
#### Outputs
min : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Mul-1** Performs element-wise binary multiplication (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Neg-1** Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Not-1** Returns the negation of the input tensor element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bool)
Constrain input/output to boolean tensors.
### **Or-1** Returns the tensor resulted from performing the `or` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **PRelu-1** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
slope : T
Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Pad-1** Given `data` tensor, paddings, mode, and value. Example: Insert 0 paddings to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] paddings = [0, 0, 2, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Three modes: constant(default), reflect, edge
paddings : list of ints (required)
List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.
value : float (default is 0.0)
One float, indicates the value to be filled, default is 0
#### Inputs
data : T
Input tensor.
#### Outputs
output : T
Tensor after padding.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Pow-1** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
#### Inputs
X : T
Input tensor of any shape, base of the exponent.
Y : T
Input tensor of any shape broadcastable to X shape, the exponent component.
#### Outputs
Z : T
Output tensor (same size as X)
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **RNN-1** Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi) #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh'])
One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
output_sequence : int (default is 0)
The sequence output for the hidden is optional if 0. Default 0.
#### Inputs (3 - 6)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **RandomNormal-1** Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. Default is TensorProto::FLOAT.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from normal distribution
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomNormalLike-1** Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by `mean` and `scale`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message, and be valid as an output type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from normal distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniform-1** Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the `shape` argument and the range by `low` and `high`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniformLike-1** Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by `low` and `high`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **Reciprocal-1** Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ReduceL1-1** Computes the L1 norm of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceL2-1** Computes the L2 norm of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceLogSum-1** Computes the log sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceLogSumExp-1** Computes the log sum exponent of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMax-1** Computes the max of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMean-1** Computes the mean of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields undefined. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMin-1** Computes the min of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceProd-1** Computes the product of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 1. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceSum-1** Computes the sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceSumSquare-1** Computes the sum square of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Relu-1** Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Reshape-1** Reshape the input tensor similar to numpy.reshape. It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
shape : list of ints
New shape
#### Inputs
data : T
An input tensor.
#### Outputs
reshaped : T
Reshaped data.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Selu-1** Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.6732)
Coefficient of SELU default to 1.6732.
consumed_inputs : list of ints
legacy optimization attribute.
gamma : float (default is 1.0507)
Coefficient of SELU default to 1.0507.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Shape-1** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
data : T
An input tensor.
#### Outputs
shape : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Sigmoid-1** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Size-1** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
data : T
An input tensor.
#### Outputs
size : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **Slice-1** Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `axes`, `starts` and `ends` attributes to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].
ends : list of ints (required)
Ending indices (exclusive) of corresponding axis in axes`
starts : list of ints (required)
Starting indices of corresponding axis in `axes`
#### Inputs
data : T
Tensor of data to extract slices from.
#### Outputs
output : T
Sliced data tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Softmax-1** The operator computes the softmax (normalized exponential) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the softmax values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Softplus-1** Softplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Softsign-1** Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The softsign (x/(1+|x|)) values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **SpaceToDepth-1** SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
#### Inputs
input : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output : T
Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Split-1** Split a tensor into a list of tensors, along the specified 'axis'. The lengths of the split can be specified using argument 'axis' or optional second input blob to the operator. Otherwise, the tensor is split to equal sized parts. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
Which axis to split on
split : list of ints
length of each output
#### Inputs (1 - 2)
input : T
The tensor to split
split (optional) : T
Optional list of output lengths (see also arg 'split')
#### Outputs (1 - ∞)
outputs... (variadic) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
### **Sqrt-1** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Squeeze-1** Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints
List of non-negative integers, indicate the dimensions to squeeze.
#### Inputs
data : T
Tensors with at least max(dims) dimensions.
#### Outputs
squeezed : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Sub-1** Performs element-wise binary subtraction (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sum-1** Element-wise sum of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Sum.
#### Outputs
sum : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tanh-1** Calculates the hyperbolic tangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
consumed_inputs : list of ints
legacy optimization attribute.
#### Inputs
input : T
1-D input tensor
#### Outputs
output : T
The hyperbolic tangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tile-1** Repeat the elements of a tensor along an axis. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
input : T
Input tensor of any shape.
tiles : T
Number of repeated copies to make of the input tensor.
axis : T
Axis along which to repeat.
#### Outputs
output : T
Output tensor of same shape and type as input.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T1 : tensor(int64)
Constrain tiles and axis's type to int64 tensors.
### **TopK-1** Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: -Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Dimension on which to do the sort.
k : int (required)
Number of top elements to retrieve
#### Inputs
X : T
Tensor of shape [a_0, a_1, ..., a_{n-1}]
#### Outputs
Values : T
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
Indices : I
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Transpose-1** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.
#### Inputs
data : T
An input tensor.
#### Outputs
transposed : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Unsqueeze-1** Insert single-dimensional entries to the shape of a tensor. Takes one required argument `axes`, a list of dimensions that will be inserted. Dimension indices in `axes` are as seen in the output tensor. For example: Given a tensor such that tensor with shape [3, 4, 5], then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1] #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axes : list of ints (required)
List of non-negative integers, indicate the dimensions to be inserted
#### Inputs
data : T
Original tensor
#### Outputs
expanded : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Upsample-1** Upsample the input tensor. The width and height of the output tensor are: output_width = floor(input_width * width_scale), output_height = floor(input_height * height_scale). Example: Given `data` tensor, width_scale, height_scale, mode, Upsample the input 4-D tensor in nearest mode: data = [[[ [1, 2], [3, 4] ]]] width_scale = 2 height_scale = 2 mode = "nearest" output = [[[ [1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4] ]]] #### Version No versioning maintained for experimental ops. #### Attributes
height_scale : float (required)
The scale along height dimension. It takes value greater than or equal to 1.
mode : string (default is nearest)
Two interpolation modes: nearest(default), bilinear
width_scale : float (required)
The scale along width dimension. It takes value greater than or equal to 1.
#### Inputs
X : T
4-D tensor, [N,C,H,W]
#### Outputs
Y : T
4-D tensor after resizing, [N,C,H,W]
#### Type Constraints
T : tensor(bool), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain output types to bool, int32, int64, float16, float, double tensors.
### **Xor-1** Returns the tensor resulted from performing the `xor` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions.
broadcast : int (default is 0)
Enable broadcasting
#### Inputs
A : T
Left input tensor for the logical operator.
B : T
Right input tensor for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
## Version 2 of the default ONNX operator set ### **GlobalLpPool-2** GlobalLpPool consumes an input tensor X and applies lp pool pooling across the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 2 of the default ONNX operator set. #### Attributes
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LpPool-2** LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. #### Version This version of the operator has been available since version 2 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Pad-2** Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] #### Version This version of the operator has been available since version 2 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Three modes: constant(default), reflect, edge
pads : list of ints (required)
List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.
value : float (default is 0.0)
One float, indicates the value to be filled.
#### Inputs
data : T
Input tensor.
#### Outputs
output : T
Tensor after padding.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Split-2** Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using argument 'split'. Otherwise, the tensor is split to equal sized parts. #### Version This version of the operator has been available since version 2 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on.
split : list of ints
length of each output
#### Inputs
input : T
The tensor to split
#### Outputs (1 - ∞)
outputs (variadic) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 3 of the default ONNX operator set ### **GRU-3** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 #### Version This version of the operator has been available since version 3 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
linear_before_reset : int (default is 0)
When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
output_sequence : int (default is 0)
The sequence output for the hidden is optional if 0. Default 0.
#### Inputs (3 - 6)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
## Version 4 of the default ONNX operator set ### **Concat-4** Concatenate a list of tensors into a single tensor #### Version This version of the operator has been available since version 4 of the default ONNX operator set. #### Attributes
axis : int (required)
Which axis to concat on
#### Inputs (1 - ∞)
inputs (variadic) : T
List of tensors for concatenation
#### Outputs
concat_result : T
Concatenated tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
## Version 5 of the default ONNX operator set ### **Reshape-5** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. #### Version This version of the operator has been available since version 5 of the default ONNX operator set. #### Inputs
data : T
An input tensor.
shape : tensor(int64)
Specified shape for output.
#### Outputs
reshaped : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 6 of the default ONNX operator set ### **Abs-6** Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **Add-6** Performs element-wise binary addition (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **BatchNormalization-6** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero, default is 1e-5f.
is_test : int (default is 0)
If set to nonzero, run spatial batch normalization in test mode, default is 0.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.
spatial : int (default is 1)
If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale : T
The scale as a 1-dimensional tensor of size C to be applied to the output.
B : T
The bias as a 1-dimensional tensor of size C to be applied to the output.
mean : T
The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.
var : T
The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.
#### Outputs (1 - 5)
Y : T
The output tensor of the same shape as X.
mean (optional) : T
The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.
var (optional) : T
The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.
saved_mean (optional) : T
Saved mean used during training to speed up gradient computation. Should not be used for testing.
saved_var (optional) : T
Saved variance used during training to speed up gradient computation. Should not be used for testing.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cast-6** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. NOTE: Casting to and from strings is not supported yet. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input : T1
Input tensor to be cast.
#### Outputs
output : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain input types. Casting from strings and complex are not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain output types. Casting to strings and complex are not supported.
### **Ceil-6** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Clip-6** Clip operator limits the given input within an interval. The interval is specified with arguments 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max() respectively. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
max : float (default is (3.402823e+38))
Maximum value, above which element is replaced by max
min : float (default is (-3.402823e+38))
Minimum value, under which element is replaced by min
#### Inputs
input : T
Input tensor whose elements to be clipped
#### Outputs
output : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Div-6** Performs element-wise binary division (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Dropout-6** Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
is_test : int (default is 0)
(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.
ratio : float (default is 0.5)
(float, default 0.5) the ratio of random dropout
#### Inputs
data : T
The input data as Tensor.
#### Outputs (1 - 2)
output : T
The output.
mask (optional) : T
The output mask. If is_test is nonzero, this output is not filled.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Elu-6** Elu takes one input data (Tensor) and produces one output data (Tensor) where the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Coefficient of ELU.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Exp-6** Calculates the exponential of the given input tensor, element-wise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
The exponential of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Floor-6** Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Gemm-6** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
beta : float (default is 1.0)
Scalar multiplier for input tensor C, the default value is 1.0.
broadcast : int (default is 0)
Whether C should be broadcasted
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs
A : T
Input tensor A
B : T
Input tensor B
C : T
Input tensor C
#### Outputs
Y : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **HardSigmoid-6** HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.2)
Value of alpha.
beta : float (default is 0.5)
Value of beta.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **InstanceNormalization-6** Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
#### Inputs
input (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale (differentiable) : T
The input 1-dimensional scale tensor of size C.
B (differentiable) : T
The input 1-dimensional bias tensor of size C.
#### Outputs
output (differentiable) : T
The output tensor of the same shape as input.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LeakyRelu-6** LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.01)
Coefficient of leakage.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Log-6** Calculates the natural log of the given input tensor, element-wise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
The natural log of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Max-6** Element-wise max of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Max.
#### Outputs
max : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Mean-6** Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Mean.
#### Outputs
mean : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Min-6** Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Min
#### Outputs
min : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Mul-6** Performs element-wise binary multiplication (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Neg-6** Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double)
Constrain input and output types to signed numeric tensors.
### **PRelu-6** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
slope : T
Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Reciprocal-6** Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Relu-6** Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Selu-6** Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.67326)
Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).
gamma : float (default is 1.0507)
Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sigmoid-6** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sqrt-6** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sub-6** Performs element-wise binary subtraction (with limited broadcast support). If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Attributes
axis : int
If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0)
Pass 1 to enable broadcasting
#### Inputs
A : T
First operand, should share the type with the second operand.
B : T
Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
#### Outputs
C : T
Result, has same dimensions and type as A
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Sum-6** Element-wise sum of each of the input tensors. All inputs and outputs must have the same shape and data type. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for Sum.
#### Outputs
sum : T
Output tensor. Same dimension as inputs.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tanh-6** Calculates the hyperbolic tangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
The hyperbolic tangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tile-6** Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] #### Version This version of the operator has been available since version 6 of the default ONNX operator set. #### Inputs
input : T
Input tensor of any shape.
repeats : T1
1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.
#### Outputs
output : T
Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(int64)
Constrain repeat's type to int64 tensors.
## Version 7 of the default ONNX operator set ### **Acos-7** Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arccosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Add-7** Performs element-wise binary addition (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First operand.
B : T
Second operand.
#### Outputs
C : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **And-7** Returns the tensor resulted from performing the `and` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Asin-7** Calculates the arcsine (inverse of sine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arcsine of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Atan-7** Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arctangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **AveragePool-7** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **BatchNormalization-7** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
spatial : int (default is 1)
If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale : T
If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)
B : T
If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)
mean : T
If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).
var : T
If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).
#### Outputs (1 - 5)
Y : T
The output tensor of the same shape as X
mean (optional) : T
The running mean after the BatchNormalization operator.
var (optional) : T
The running variance after the BatchNormalization operator.
saved_mean (optional) : T
Saved mean used during training to speed up gradient computation.
saved_var (optional) : T
Saved variance used during training to speed up gradient computation.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cos-7** Calculates the cosine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The cosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Div-7** Performs element-wise binary division (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First operand.
B : T
Second operand.
#### Outputs
C : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Dropout-7** Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
ratio : float (default is 0.5)
The ratio of random dropout
#### Inputs
data : T
The input data as Tensor.
#### Outputs (1 - 2)
output : T
The output.
mask (optional) : T
The output mask.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Equal-7** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(int32), tensor(int64)
Constrain input to integral tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **GRU-7** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
linear_before_reset : int (default is 0)
When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
#### Inputs (3 - 6)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Gemm-7** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B.
beta : float (default is 1.0)
Scalar multiplier for input tensor C.
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs
A : T
Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B : T
Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C : T
Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
#### Outputs
Y : T
Output tensor of shape (M, N).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Greater-7** Returns the tensor resulted from performing the `greater` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input to float tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **LSTM-7** Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
input_forget : int (default is 0)
Couple the input and forget gates if 1.
#### Inputs (3 - 8)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional) : T
Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional) : T
The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
#### Outputs (0 - 3)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional) : T
The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Less-7** Returns the tensor resulted from performing the `less` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input to float tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Mul-7** Performs element-wise binary multiplication (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First operand.
B : T
Second operand.
#### Outputs
C : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Multinomial-7** Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
dtype : int (default is 6)
(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.
sample_size : int (default is 1)
Number of times to sample.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.
#### Outputs
output : T2
Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain output types to integral tensors.
### **Or-7** Returns the tensor resulted from performing the `or` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **PRelu-7** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
X : T
Input tensor
slope : T
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
#### Outputs
Y : T
Output tensor (same size as X)
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Pow-7** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
X : T
First operand, base of the exponent.
Y : T
Second operand, power of the exponent.
#### Outputs
Z : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **RNN-7** Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh'])
One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
#### Inputs (3 - 6)
X : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T
The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R : T
The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional) : T
The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Sin-7** Calculates the sine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The sine of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sub-7** Performs element-wise binary subtraction (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A : T
First operand.
B : T
Second operand.
#### Outputs
C : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Tan-7** Calculates the tangent of the given input tensor, element-wise. #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The tangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Upsample-7** Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Attributes
mode : string (default is nearest)
Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
scales : list of floats (required)
The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.
#### Inputs
X : T
N-D tensor
#### Outputs
Y : T
N-D tensor after resizing
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Xor-7** Returns the tensor resulted from performing the `xor` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
## Version 8 of the default ONNX operator set ### **Expand-8** Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimensions must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Inputs
input : T
Input tensor
shape : tensor(int64)
A 1-D tensor indicates the shape you want to expand to, following the broadcast rule
#### Outputs
output : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensors.
### **Max-8** Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for max.
#### Outputs
max : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxPool-8** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Mean-8** Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for mean.
#### Outputs
mean : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Min-8** Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for min.
#### Outputs
min : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Scan-8** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). All these tensors are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). The scan operation returns the final values of the state_variables as well as the scan_outputs. The operation supports batching, and the batch-axis is required to be 0. When multiple scan_input tensors are used, they must all have the same batch-size, and they must all have the same maximum-sequence-length (the dimensionality of the sequence axis or scan axis). The sequence axis or scan axis is required to be 1. The operation has an optional sequence_lens input (of shape [BATCH_SIZE]) to allow variable length sequences of length <= the maximum-sequence-length. If this input is not specified, all sequences are assumed to be of length equal to maximum-sequence-length. For variable length input sequences, the scan_outputs will consist of a sequence of same length as the input, padded to the maximum-sequence-length. The optional attribute directions can be used to scan a sequence in the reverse direction. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body > (sequence_lengths, init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // T.shape[0] denotes the batch-size of T // The batch-size of scan_1, ..., scan_m are all required to be equal batch_size = scan_1.shape[0]; // scan_i.shape[1] denotes the (max) sequence-length of scan_i // scan_i.shape[1] is required to be equal to scan_j.shape[1] for all i,j. max_sequence_length = scan_1.shape[1]; for (int batch = 0; batch < batch_size; ++batch) { // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: N = (sequence_lengths specified) ? sequence_lengths[batch] : max_sequence_length; // execute loop for (int t = 0; t < N; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = (scan_1[batch])[t]; ... ; si_m = (scan_m[batch])[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } // accumulate the outputs for this batch: bst_1[batch] = st_1; ..., bst_n[batch] = st_n; // Note scan-outputs will have size max_sequence_length, but only first N values will be meaningful. // The remaining values have an undefined value. b_scan_out_1[batch] = scan_out_1; ...; b_scan_out_k[batch] = scan_out_k; } return bst_1, ..., bst_n, b_scan_out_1, ..., b_scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1]("", %H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
#### Inputs (2 - ∞)
sequence_lens (optional) : I
Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
I : tensor(int64)
Int64 tensor
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
### **Sum-8** Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 8 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for sum.
#### Outputs
sum : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
## Version 9 of the default ONNX operator set ### **Acosh-9** Calculates the hyperbolic arccosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arccosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Asinh-9** Calculates the hyperbolic arcsine of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arcsine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Atanh-9** Calculates the hyperbolic arctangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arctangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **BatchNormalization-9** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
scale (differentiable) : T
Scale tensor of shape (C).
B (differentiable) : T
Bias tensor of shape (C).
mean (differentiable) : T
running (training) or estimated (testing) mean tensor of shape (C).
var (differentiable) : T
running (training) or estimated (testing) variance tensor of shape (C).
#### Outputs (1 - 5)
Y (differentiable) : T
The output tensor of the same shape as X
mean (optional, non-differentiable) : T
The running mean after the BatchNormalization operator.
var (optional, non-differentiable) : T
The running variance after the BatchNormalization operator.
saved_mean (optional, non-differentiable) : T
Saved mean used during training to speed up gradient computation.
saved_var (optional, non-differentiable) : T
Saved variance used during training to speed up gradient computation.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cast-9** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input : T1
Input tensor to be cast.
#### Outputs
output : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string)
Constrain output types. Casting to complex is not supported.
### **Compress-9** Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
axis : int
(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.
#### Inputs
input : T
Tensor of rank r >= 1.
condition : T1
Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
#### Outputs
output : T
Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(bool)
Constrain to boolean tensors.
### **Constant-9** A constant tensor. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
value : tensor (required)
The value for the elements of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **ConstantOfShape-9** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain output types to be numerics.
### **Cosh-9** Calculates the hyperbolic cosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic cosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Erf-9** Computes the error function of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
The error function of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **EyeLike-9** Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the 'dtype' argument. If 'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.
k : int (default is 0)
(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.
#### Inputs
input : T1
2D input tensor to copy shape, and optionally, type information from.
#### Outputs
output : T2
Output tensor, same shape as input tensor T1.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain input types. Strings and complex are not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
Constrain output types. Strings and complex are not supported.
### **Flatten-9** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input : T
A tensor of rank >= axis.
#### Outputs
output : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output to all tensor types.
### **Gemm-9** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B.
beta : float (default is 1.0)
Scalar multiplier for input tensor C.
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs
A : T
Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B : T
Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C : T
Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
#### Outputs
Y : T
Output tensor of shape (M, N).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
### **Greater-9** Returns the tensor resulted from performing the `greater` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **IsNaN-9** Returns which elements of the input are NaN. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
X : T1
input
#### Outputs
Y : T2
output
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
### **Less-9** Returns the tensor resulted from performing the `less` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **MatMul-9** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
A : T
N-dimensional matrix A
B : T
N-dimensional matrix B
#### Outputs
Y : T
Matrix multiply results from A * B
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
### **MaxUnpool-9** MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corresponding pooling op that the unpooling op is trying to invert. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs (2 - 3)
X : T1
Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
I : T2
Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).
output_shape (optional) : T2
The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.
#### Outputs
output : T1
Output data tensor that contains the result of the unpooling.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int64)
Constrain index tensor to int64
### **MeanVarianceNormalization-9** A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula:
``` (X-EX)/sqrt(E(X-EX)^2) ``` #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
axes : list of ints (default is ['0', '2', '3'])
A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
#### Inputs
X : T
Input tensor
#### Outputs
Y : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **NonZero-9** Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
X : T
input
#### Outputs
Y : tensor(int64)
output
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to all tensor types.
### **OneHot-9** Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.
#### Inputs
indices : T1
Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.
depth : T2
Scalar or rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.
values : T3
Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor.
#### Outputs
output : T3
Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
### **PRelu-9** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
slope (differentiable) : T
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
#### Outputs
Y (differentiable) : T
Output tensor (same size as X)
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
### **Scan-9** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
### **Scatter-9** Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates` into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`. For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices` for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case, data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1, where i and j are loop counters from 0 up to the respective size in `updates` - 1. Example 1: data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] Example 2: data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]
#### Inputs
data : T
Tensor of rank r >= 1.
indices : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input).
updates : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **Shrink-9** Shrink takes one input data (Tensor) and produces one Tensor output, having same datatype and shape with input. It has two attributes, lambd and bias. The formula of this operator is: If x < -lambd, y = x + bias; If x > lambd, y = x - bias; Otherwise, y = 0. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
bias : float (default is 0.0)
The bias value added to output. Default is 0.
lambd : float (default is 0.5)
The lambd value for the Shrink formulation. Default is 0.5.
#### Inputs
input (differentiable) : T
The input data as Tensor.
#### Outputs
output (differentiable) : T
The output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
### **Sign-9** Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input : T
Input tensor
#### Outputs
output : T
The sign of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **Sinh-9** Calculates the hyperbolic sine of the given input tensor element-wise. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic sine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **TfIdfVectorizer-9** This transform extracts n-grams from the input sequence and save them as a vector. Input can be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input. For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row. More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor. In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips. If the number of skips is 2, we should skip two tokens when scanning through the original sequence. Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2. The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]. If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28] indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively. The output vector (denoted by Y) stores the count of each n-gram; Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0], ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17], respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output. Note that we may consider all skips up to S when generating the n-grams. The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF", this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute. Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor. If pool_strings is set, the input must be a string tensor. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
max_gram_length : int (required)
Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.
max_skip_count : int (required)
Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1
min_gram_length : int (required)
Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.
mode : string (required)
The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)
ngram_counts : list of ints (required)
The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.
ngram_indexes : list of ints (required)
list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.
pool_int64s : list of ints
List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
pool_strings : list of strings
List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
weights : list of floats
list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.
#### Inputs
X (non-differentiable) : T
Input for n-gram extraction
#### Outputs
Y (non-differentiable) : T1
Ngram results
#### Type Constraints
T : tensor(string), tensor(int32), tensor(int64)
Input is ether string UTF-8 or int32/int64
T1 : tensor(float)
1-D tensor of floats
### **Upsample-9** Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
mode : string (default is nearest)
Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
#### Inputs
X : T
N-D tensor
scales : tensor(float)
The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.
#### Outputs
Y : T
N-D tensor after resizing
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
### **Where-9** Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Inputs
condition (non-differentiable) : B
When True (nonzero), yield X, otherwise yield Y
X (differentiable) : T
values selected at indices where condition is True
Y (differentiable) : T
values selected at indices where condition is False
#### Outputs
output (differentiable) : T
Tensor of shape equal to the broadcasted shape of condition, X, and Y.
#### Type Constraints
B : tensor(bool)
Constrain to boolean tensors.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 10 of the default ONNX operator set ### **AveragePool-10** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ConvInteger-10** The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point, and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.
group : int (default is 1)
number of groups input channels and output channels are divided into. default is 1.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input 'w'.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.
#### Inputs (2 - 4)
x : T1
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
w : T2
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
x_zero_point (optional) : T1
Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.
w_zero_point (optional) : T2
Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)
#### Outputs
y : T3
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input x and its zero point data type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain input w and its zero point data type to 8-bit integer tensor.
T3 : tensor(int32)
Constrain output y data type to 32-bit integer tensor.
### **DequantizeLinear-10** The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' are both scalars. 'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs (2 - 3)
x : T
N-D quantized input tensor to be de-quantized.
x_scale : tensor(float)
Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
x_zero_point (optional) : T
Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.
#### Outputs
y : tensor(float)
N-D full precision output tensor. It has same shape as input 'x'.
#### Type Constraints
T : tensor(int8), tensor(uint8), tensor(int32)
Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.
### **Dropout-10** Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
ratio : float (default is 0.5)
The ratio of random dropout
#### Inputs
data : T
The input data as Tensor.
#### Outputs (1 - 2)
output : T
The output.
mask (optional) : T1
The output mask.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(bool)
Constrain output mask types to boolean tensors.
### **IsInf-10** Map infinity to true and other values to false. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
detect_negative : int (default is 1)
(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.
detect_positive : int (default is 1)
(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.
#### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
### **MatMulInteger-10** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs (2 - 4)
A (non-differentiable) : T1
N-dimensional matrix A
B (non-differentiable) : T2
N-dimensional matrix B
a_zero_point (optional, non-differentiable) : T1
Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1].
b_zero_point (optional, non-differentiable) : T2
Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N].
#### Outputs
Y (non-differentiable) : T3
Matrix multiply results from A * B
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input A data type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain input B data type to 8-bit integer tensor.
T3 : tensor(int32)
Constrain output Y data type as 32-bit integer tensor.
### **MaxPool-10** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
Dilation value along each spatial axis of filter.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major.
strides : list of ints
Stride along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Mod-10** Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor. Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod(). If the input type is floating point, then `fmod` attribute must be set to 1. In case of dividend being zero, the results will be platform dependent. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
fmod : int (default is 0)
Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment
#### Inputs
A : T
Dividend tensor
B : T
Divisor tensor
#### Outputs
C : T
Remainder tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **NonMaxSuppression-10** Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
center_point_box : int (default is 0)
Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.
#### Inputs (2 - 5)
boxes : tensor(float)
An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.
scores : tensor(float)
An input tensor with shape [num_batches, num_classes, spatial_dimension]
max_output_boxes_per_class (optional) : tensor(int64)
Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.
iou_threshold (optional) : tensor(float)
Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.
score_threshold (optional) : tensor(float)
Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.
#### Outputs
selected_indices : tensor(int64)
selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].
#### Type Constraints ### **QLinearConv-10** The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized filter, its scale and zero point, and output's scale and zero point, and computes the quantized output. Each scale and zero-point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per output channel). Each input or output and its related zero point must have same type. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into. default is 1.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input 'w'.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (8 - 9)
x : T1
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
x_scale : tensor(float)
Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
x_zero_point : T1
Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
w : T2
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
w_scale : tensor(float)
Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
w_zero_point : T2
Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
y_scale : tensor(float)
Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
y_zero_point : T3
Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
B (optional) : T4
Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0
#### Outputs
y : T3
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain filter type to 8-bit integer tensor.
T3 : tensor(int8), tensor(uint8)
Constrain output type to 8-bit integer tensor.
T4 : tensor(int32)
Constrain bias type to 32-bit integer tensor.
### **QLinearMatMul-10** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs
a (non-differentiable) : T1
N-dimensional quantized matrix a
a_scale (non-differentiable) : tensor(float)
scale of quantized input a
a_zero_point (non-differentiable) : T1
zero point of quantized input a
b (non-differentiable) : T2
N-dimensional quantized matrix b
b_scale (non-differentiable) : tensor(float)
scale of quantized input b
b_zero_point (non-differentiable) : T2
zero point of quantized input b
y_scale (non-differentiable) : tensor(float)
scale of quantized output y
y_zero_point (non-differentiable) : T3
zero point of quantized output y
#### Outputs
y (non-differentiable) : T3
Quantized matrix multiply results from a * b
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input a and its zero point data type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain input b and its zero point data type to 8-bit integer tensor.
T3 : tensor(int8), tensor(uint8)
Constrain output y and its zero point data type to 8-bit integer tensor.
### **QuantizeLinear-10** The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : tensor(float)
Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.
y_zero_point (optional) : T2
Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.
#### Outputs
y : T2
N-D quantized output tensor. It has same shape as input 'x'.
#### Type Constraints
T1 : tensor(float), tensor(int32)
Constrain 'x' to float or int32 tensor.
T2 : tensor(int8), tensor(uint8)
Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.
### **Resize-10** Resize the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
mode : string (default is nearest)
Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
#### Inputs
X : T
N-D tensor
scales : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.
#### Outputs
Y : T
N-D tensor after resizing
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
### **ReverseSequence-10** Reverse batch of sequences having different lengths specified by `sequence_lens`. For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements. Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1 output = [[3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0]] Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0 output = [[0.0, 1.0, 2.0, 3.0 ], [5.0, 4.0, 6.0, 7.0 ], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0]] #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
batch_axis : int (default is 1)
(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.
time_axis : int (default is 0)
(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.
#### Inputs
input : T
Tensor of rank r >= 2.
sequence_lens : tensor(int64)
Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.
#### Outputs
Y : T
Tensor with same shape of input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
### **RoiAlign-10** Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
mode : string (default is avg)
The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.
output_height : int (default is 1)
default 1; Pooled output Y's height.
output_width : int (default is 1)
default 1; Pooled output Y's width.
sampling_ratio : int (default is 0)
Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f.
#### Inputs
X : T1
Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois : T1
RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
batch_indices : T2
1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
#### Outputs
Y : T1
RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain types to float tensors.
T2 : tensor(int64)
Constrain types to int tensors.
### **Slice-10** Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If a negative value is passed for step, it represents slicing backward. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs (3 - 5)
data : T
Tensor of data to extract slices from.
starts : Tind
1-D tensor of starting indices of corresponding axis in `axes`
ends : Tind
1-D tensor of ending indices (exclusive) of corresponding axis in `axes`
axes (optional) : Tind
1-D tensor of axes that `starts` and `ends` apply to.
steps (optional) : Tind
1-D tensor of slice step of corresponding axis in `axes`. Default to 1.
#### Outputs
output : T
Sliced data tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **StringNormalizer-10** StringNormalization performs string operations for basic cleaning. This operator has only one input (denoted by X) and only one output (denoted by Y). This operator first examines the elements in the X, and removes elements specified in "stopwords" attribute. After removing stop words, the intermediate result can be further lowercased, uppercased, or just returned depending the "case_change_action" attribute. This operator only accepts [C]- and [1, C]-tensor. If all elements in X are dropped, the output will be the empty value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C]. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
case_change_action : string (default is NONE)
string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"
is_case_sensitive : int (default is 0)
Boolean. Whether the identification of stop words in X is case-sensitive. Default is false
locale : string
Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.
stopwords : list of strings
List of stop words. If not set, no word would be removed from X.
#### Inputs
X : tensor(string)
UTF-8 strings to normalize
#### Outputs
Y : tensor(string)
UTF-8 Normalized strings
#### Type Constraints ### **ThresholdedRelu-10** ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Threshold value
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **TopK-10** Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: -Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Dimension on which to do the sort.
#### Inputs
X : T
Tensor of shape [a_0, a_1, ..., a_{n-1}]
K : tensor(int64)
A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
#### Outputs
Values : T
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
Indices : I
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Upsample-10** (deprecated) Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). #### Version This version of the operator has been deprecated since version 10 of the default ONNX operator set. ## Version 11 of the default ONNX operator set ### **ArgMax-11** Computes the indices of the max elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **ArgMin-11** Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **AveragePool-11** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled: ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **BitShift-11** Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
direction : string (required)
Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).
#### Inputs
X (non-differentiable) : T
First operand, input to be shifted.
Y (non-differentiable) : T
Second operand, amounts of shift.
#### Outputs
Z (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)
Constrain input and output types to integer tensors.
### **Clip-11** Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (1 - 3)
input : T
Input tensor whose elements to be clipped
min (optional) : T
Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).
max (optional) : T
Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
#### Outputs
output : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Compress-11** Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int
(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
Tensor of rank r >= 1.
condition (non-differentiable) : T1
Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
#### Outputs
output (differentiable) : T
Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(bool)
Constrain to boolean tensors.
### **Concat-11** Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (required)
Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..
#### Inputs (1 - ∞)
inputs (variadic) : T
List of tensors for concatenation
#### Outputs
concat_result : T
Concatenated tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
### **ConcatFromSequence-11** Concatenate a sequence of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. By default 'new_axis' is 0, the behavior is similar to numpy.concatenate. When 'new_axis' is 1, the behavior is similar to numpy.stack. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (required)
Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`.
new_axis : int (default is 0)
Insert and concatenate on a new axis or not, default 0 means do not insert new axis.
#### Inputs
input_sequence : S
Sequence of tensors for concatenation
#### Outputs
concat_result : T
Concatenated tensor
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input types to any tensor type.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
### **Constant-11** A constant tensor. Exactly one of the two attributes, either value or sparse_value, must be specified. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Conv-11** The convolution operator consumes an input tensor and a filter, and computes the output. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T : tensor(float16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ConvTranspose-11** The convolution transpose operator consumes an input tensor and a filter, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding : list of ints
Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.
output_shape : list of ints
The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **CumSum-11** Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
exclusive : int (default is 0)
If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.
reverse : int (default is 0)
If set to 1 will perform the sums in reverse direction.
#### Inputs
x (differentiable) : T
An input tensor that is to be processed.
axis (non-differentiable) : T2
A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.
#### Outputs
y (differentiable) : T
Output tensor of the same type as 'x' with cumulative sums of the x's elements
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float), tensor(double)
Input can be of any tensor type.
T2 : tensor(int32), tensor(int64)
axis tensor can be int32 or int64 only
### **DepthToSpace-11** DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
mode : string (default is DCR)
DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.
#### Inputs
input : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output : T
Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Det-11** Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions, and the inner-most 2 dimensions form square matrices. The output is a tensor of shape `[*]`, containing the determinants of all input submatrices. e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to floating-point tensors.
### **DynamicQuantizeLinear-11** A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as: ``` y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin) ``` * where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8 * data range is adjusted to include 0. Zero point is calculated as: ``` intermediate_zero_point = qmin - min(x)/y_scale y_zero_point = cast(round(saturate(intermediate_zero_point))) ``` * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. Data quantization formula is: ``` y = saturate (round (x / y_scale) + y_zero_point) ``` * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
x : T1
Input tensor
#### Outputs
y : T2
Quantized output tensor
y_scale : tensor(float)
Output scale. It's a scalar, which means a per-tensor/layer quantization.
y_zero_point : T2
Output zero point. It's a scalar, which means a per-tensor/layer quantization.
#### Type Constraints
T1 : tensor(float)
Constrain 'x' to float tensor.
T2 : tensor(uint8)
Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.
### **Equal-11** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
A : T
First input operand for the logical operator.
B : T
Second input operand for the logical operator.
#### Outputs
C : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Flatten-11** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input : T
A tensor of rank >= axis.
#### Outputs
output : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output to all tensor types.
### **Gather-11** Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). axis = 0 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}] ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` axis = 1 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}] ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data : T
Tensor of rank r >= 1.
indices : Tind
Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output : T
Tensor of rank q + (r - 1).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **GatherElements-11** GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [ [1, 1], [4, 3], ], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [ [4, 8, 3], [7, 2, 3], ], ] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data : T
Tensor of rank r >= 1.
indices : Tind
Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output : T
Tensor of the same shape as indices.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **GatherND-11** Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive) 3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r` => error condition 2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, and 4 below) This operator is the inverse of `ScatterND`. `Example 1` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
data : T
Tensor of rank r >= 1.
indices : tensor(int64)
Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **Gemm-11** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md). This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B.
beta : float (default is 1.0)
Scalar multiplier for input tensor C.
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs (2 - 3)
A : T
Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B : T
Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (optional) : T
Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
#### Outputs
Y : T
Output tensor of shape (M, N).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
### **Hardmax-11** The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the hardmax values of the corresponding input. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **If-11** If conditional #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
B : tensor(bool)
Only bool
### **LogSoftmax-11** The operator computes the logsoftmax (log of softmax) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the logsoftmax values of the corresponding input. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Loop-11** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **LpPool-11** LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxPool-11** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
I : tensor(int64)
Constrain index tensor to int64
### **MaxUnpool-11** MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corresponding pooling op that the unpooling op is trying to invert. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T1
Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
I (non-differentiable) : T2
Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).
output_shape (optional, non-differentiable) : T2
The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.
#### Outputs
output (differentiable) : T1
Output data tensor that contains the result of the unpooling.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int64)
Constrain index tensor to int64
### **NonMaxSuppression-11** Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
center_point_box : int (default is 0)
Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.
#### Inputs (2 - 5)
boxes : tensor(float)
An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.
scores : tensor(float)
An input tensor with shape [num_batches, num_classes, spatial_dimension]
max_output_boxes_per_class (optional) : tensor(int64)
Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.
iou_threshold (optional) : tensor(float)
Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.
score_threshold (optional) : tensor(float)
Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.
#### Outputs
selected_indices : tensor(int64)
selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].
#### Type Constraints ### **OneHot-11** Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor. when axis = 0: output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise. when axis = -1: output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).
#### Inputs
indices (non-differentiable) : T1
Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.
depth (non-differentiable) : T2
Scalar or Rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.
values (non-differentiable) : T3
Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor.
#### Outputs
output (non-differentiable) : T3
Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
### **Pad-11** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`
#### Inputs (2 - 3)
data : T
Input tensor.
pads : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.
constant_value (optional) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).
#### Outputs
output : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output to only numeric types.
### **Range-11** Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta` up to `limit` (exclusive). The number of elements in the output of range is computed as below: ``` number_of_elements = max( ceil( (limit - start) / delta ) , 0 ) ``` The pseudocode determining the contents of the output is shown below: ``` for(int i=0; i
start : T
Scalar. First entry for the range of output values.
limit : T
Scalar. Exclusive upper limit for the range of output values.
delta : T
Scalar. Value to step by.
#### Outputs
output : T
A 1-D tensor with same type as the inputs containing generated range of values.
#### Type Constraints
T : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)
Constrain input types to common numeric type tensors.
### **ReduceL1-11** Computes the L1 norm of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceL2-11** Computes the L2 norm of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceLogSum-11** Computes the log sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceLogSumExp-11** Computes the log sum exponent of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMax-11** Computes the max of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMean-11** Computes the mean of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceMin-11** Computes the min of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceProd-11** Computes the product of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceSum-11** Computes the sum of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **ReduceSumSquare-11** Computes the sum square of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to high-precision numeric tensors.
### **Resize-11** Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
coordinate_transformation_mode : string (default is half_pixel)
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).
cubic_coeff_a : float (default is -0.75)
The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".
exclude_outside : int (default is 0)
If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
extrapolation_value : float (default is 0.0)
When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
mode : string (default is nearest)
Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
nearest_mode : string (default is round_prefer_floor)
Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
#### Inputs (3 - 4)
X : T1
N-D tensor
roi : T2
1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
scales : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.
sizes (optional) : tensor(int64)
The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. May only be set if 'scales' is set to an empty tensor.
#### Outputs
Y : T1
N-D tensor after resizing
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain roi type to float or double.
### **Round-11** Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halves, the rule is to round them to the nearest even integer. If input x is integral, +0, -0, NaN, or infinite, x itself is returned. The output tensor has the same shape and type as the input. Examples: ``` round([0.9]) = [1.0] round([2.5]) = [2.0] round([2.3]) = [2.0] round([1.5]) = [2.0] round([-4.5]) = [-4.0] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Scan-11** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types
### **Scatter-11** (deprecated) This operator is deprecated. Please use ScatterElements, which provides the same functionality. Scatter takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been deprecated since version 11 of the default ONNX operator set. ### **ScatterElements-11** ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data : T
Tensor of rank r >= 1.
indices : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **ScatterND-11** ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more `updates` for the same index-location is not supported. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
data : T
Tensor of rank r >= 1.
indices : tensor(int64)
Tensor of rank q >= 1.
updates : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Outputs
output : T
Tensor of rank r >= 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **SequenceAt-11** Outputs a tensor copy from the tensor at 'position' in 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
input_sequence : S
Input sequence.
position : I
Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
tensor : T
Output tensor at the specified position in the input sequence.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceConstruct-11** Construct a tensor sequence containing 'inputs' tensors. All tensors in 'inputs' must have the same data type. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (1 - ∞)
inputs (variadic) : T
Tensors.
#### Outputs
output_sequence : S
Sequence enclosing the input tensors.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input types to any tensor type.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to any tensor type.
### **SequenceEmpty-11** Construct an empty tensor sequence, with given data type. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type of the tensors in the output sequence. The default type is 'float'.
#### Inputs #### Outputs
output : S
Empty sequence.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to any tensor type.
### **SequenceErase-11** Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it erases the last tensor from 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (1 - 2)
input_sequence : S
Input sequence.
position (optional) : I
Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
output_sequence : S
Output sequence that has the tensor at the specified position removed.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceInsert-11** Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'. 'tensor' must have the same data type as 'input_sequence'. Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (2 - 3)
input_sequence : S
Input sequence.
tensor : T
Input tensor to be inserted into the input sequence.
position (optional) : I
Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
output_sequence : S
Output sequence that contains the inserted tensor at given position.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceLength-11** Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
input_sequence : S
Input sequence.
#### Outputs
length : I
Length of input sequence. It must be a scalar(tensor of empty shape).
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int64)
Constrain output to integral tensor. It must be a scalar(tensor of empty shape).
### **Slice-11** Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represents number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward. If a negative value is passed for step, it represents slicing backward. However step value cannot be 0. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (3 - 5)
data : T
Tensor of data to extract slices from.
starts : Tind
1-D tensor of starting indices of corresponding axis in `axes`
ends : Tind
1-D tensor of ending indices (exclusive) of corresponding axis in `axes`
axes (optional) : Tind
1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
steps (optional) : Tind
1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.
#### Outputs
output : T
Sliced data tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **Softmax-11** The operator computes the softmax (normalized exponential) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the softmax values of the corresponding input. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input : T
The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
#### Outputs
output : T
The output values with the same shape as input tensor (the original size without coercion).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Split-11** Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using argument 'split'. Otherwise, the tensor is split to equal sized parts. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
split : list of ints
length of each output. Values should be >= 0.
#### Inputs
input : T
The tensor to split
#### Outputs (1 - ∞)
outputs (variadic) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **SplitToSequence-11** Split a tensor into a sequence of tensors, along the specified 'axis'. Lengths of the parts can be specified using the optional argument 'split'. If the argument `split' is not specified, a default scalar value of 1 is used as the value of `split'. 'split' must contain only positive numbers. 'split' is either a scalar (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input' will be split into chunks all of size 'split' if possible. The last chunk alone may be smaller than 'split' if the 'input' size along the given axis 'axis' is not divisible by 'split'. If 'split' is a 1-dimensional tensor, the input tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis' specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the dimension size of input tensor on 'axis'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].
keepdims : int (default is 1)
Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.
#### Inputs (1 - 2)
input : T
The tensor to split
split (optional) : I
Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0.
#### Outputs
output_sequence : S
One or more outputs forming a sequence of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input types to all tensor types.
I : tensor(int32), tensor(int64)
Constrain split size to integral tensor.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to all tensor types.
### **Squeeze-11** Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints
List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data : T
Tensors with at least max(dims) dimensions.
#### Outputs
squeezed : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **TopK-11** Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: * Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis * Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). * If "largest" is 1 (the default value) then the k largest elements are returned. * If "sorted" is 1 (the default value) then the resulting k elements will be sorted. * If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined. Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
largest : int (default is 1)
Whether to return the top-K largest or smallest elements.
sorted : int (default is 1)
Whether to return the elements in sorted order.
#### Inputs
X (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{n-1}]
K (non-differentiable) : tensor(int64)
A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
#### Outputs
Values (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
Indices (non-differentiable) : I
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to numeric tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Unique-11** Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor 'Y' contains all unique values or subtensors of the input. The second optional output tensor 'indices' contains indices of 'Y' elements' first occurrence in 'X'. The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html Example 1: ``` input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] ``` Example 2: ``` input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2] ``` Example 3: ``` input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1] ``` Example 4: ``` input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis = 1 ``` intermediate data are presented below for better understanding: there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)): ``` A: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1], [0, 1]]. ``` there are 3 unique subtensors: ``` [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]]. ``` sorted unique subtensors: ``` B: [[0, 1], [0, 1]], [[1, 1], [1, 1]], [[2, 1], [2, 1]]. ``` output_Y is constructed from B: ``` [[[0. 1.], [1. 1.], [2. 1.]], [[0. 1.], [1. 1.], [2. 1.]]] ``` output_indices is to map from B to A: ``` [1, 0, 2] ``` output_inverse_indices is to map from A to B: ``` [1, 0, 2, 0] ``` output_counts: ``` [2, 1, 1] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int
(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
sorted : int (default is 1)
(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).
#### Inputs
X (non-differentiable) : T
A N-D input tensor that is to be processed.
#### Outputs (1 - 4)
Y (non-differentiable) : T
A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'
indices (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing indices of 'Y' elements' first occurrence in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor.
inverse_indices (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'.
counts (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input can be of any tensor type.
### **Unsqueeze-11** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example: Given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axes : list of ints (required)
List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Inputs
data : T
Original tensor
#### Outputs
expanded : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 12 of the default ONNX operator set ### **ArgMax-12** Computes the indices of the max elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the max is selected if the max appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **ArgMin-12** Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **Celu-12** Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula: ``` max(0,x) + min(0,alpha*(exp(x/alpha)-1)) ``` #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float)
Constrain input and output types to float32 tensors.
### **Clip-12** Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs (1 - 3)
input : T
Input tensor whose elements to be clipped
min (optional) : T
Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).
max (optional) : T
Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
#### Outputs
output : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numeric tensors.
### **Constant-12** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Dropout-12** Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
seed : int
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs (1 - 3)
data : T
The input data as Tensor.
ratio (optional) : T1
The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
training_mode (optional) : T2
If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.
#### Outputs (1 - 2)
output : T
The output.
mask (optional) : T2
The output mask.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input 'ratio' types to float tensors.
T2 : tensor(bool)
Constrain output 'mask' types to boolean tensors.
### **Einsum-12** An einsum of the form `term1, term2 -> output-term` produces an output tensor using the following equation ``` output[output-term] = reduce-sum( input1[term1] * input2[term2] ) ``` where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term. The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions. This sequence may be followed by "->" to separate the left and right hand side of the equation. If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation. When a dimension character is repeated in the left-hand side, it represents summation along the dimension. The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
equation : string (required)
Einsum expression string.
#### Inputs (1 - ∞)
Inputs (variadic, differentiable) : T
Operands
#### Outputs
Output (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numerical tensor types.
### **GatherND-12** Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. `Example 1` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] `Example 5` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
batch_dims : int (default is 0)
The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]
#### Inputs
data : T
Tensor of rank r >= 1.
indices : tensor(int64)
Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **GreaterOrEqual-12** Returns the tensor resulted from performing the `greater_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **LessOrEqual-12** Returns the tensor resulted from performing the `less_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Max-12** Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for max.
#### Outputs
max : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to numeric tensors.
### **MaxPool-12** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional, non-differentiable) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)
Constrain input and output types to float and 8 bit tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Min-12** Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic) : T
List of tensors for min.
#### Outputs
min : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to numeric tensors.
### **NegativeLogLikelihoodLoss-12** A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. When an optional "weight" is provided, the sample loss is calculated as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. loss is zero for the case when target-value equals ignore_index. loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: mean(loss), if "weight" is not provided, or if weight is provided, sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. If "reduction" attribute is set to "sum", the output is a scalar: sum(loss). See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] Example 2: // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 Example 3: // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.
#### Inputs (2 - 3)
input : T
Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).
target : Tind
Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.
weight (optional) : T
Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.
#### Outputs
loss : T
The negative log likelihood loss
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input, weight, and output types to floating-point tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
### **Pow-12** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Inputs
X : T
First operand, base of the exponent.
Y : T1
Second operand, power of the exponent.
#### Outputs
Z : T
Output tensor.
#### Type Constraints
T : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input X and output types to float/int tensors.
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input Y types to float/int tensors.
### **ReduceMax-12** Computes the max of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(uint8), tensor(int8)
Constrain input and output types to high-precision and 8 bit numeric tensors.
### **ReduceMin-12** Computes the min of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data : T
An input tensor.
#### Outputs
reduced : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(uint8), tensor(int8)
Constrain input and output types to high-precision and 8 bit numeric tensors.
### **SoftmaxCrossEntropyLoss-12** Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can calculated as follows: l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. or l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. loss is zero for the case when label-value equals ignore_index. l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] Finally, L is optionally reduced: If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). If reduction = 'sum', the output is scalar: Sum(L). If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.
#### Inputs (2 - 3)
scores : T
The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.
labels : Tind
The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.
weights (optional) : T
A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.
#### Outputs (1 - 2)
output : T
Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.
log_prob (optional) : T
Log probability tensor. If the output of softmax is prob, its value is log(prob).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
## Version 13 of the default ONNX operator set ### **Abs-13** Absolute takes one input data (Tensor) and produces one output data (Tensor) where absolute value, y = abs(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Add-13** Performs element-wise binary addition (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
### **ArgMax-13** Computes the indices of the max elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the max is selected if the max appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
reduced (non-differentiable) : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **ArgMin-13** Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
reduced (non-differentiable) : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Cast-13** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules: * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
Constrain output types. Casting to complex is not supported.
### **Ceil-13** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Clip-13** Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. When 'min' is greater than 'max', the clip operator sets all the 'input' values to the value of 'max'. Thus, this is equivalent to 'Min(max, Max(input, min))'. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - 3)
input (differentiable) : T
Input tensor whose elements to be clipped
min (optional, non-differentiable) : T
Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).
max (optional, non-differentiable) : T
Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
#### Outputs
output (differentiable) : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Concat-13** Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (required)
Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..
#### Inputs (1 - ∞)
inputs (variadic, differentiable) : T
List of tensors for concatenation
#### Outputs
concat_result (differentiable) : T
Concatenated tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
### **Constant-13** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **DepthToSpace-13** DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) ``` In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
mode : string (default is DCR)
DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.
#### Inputs
input (differentiable) : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output (differentiable) : T
Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **DequantizeLinear-13** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. `x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs (2 - 3)
x : T
N-D quantized input tensor to be de-quantized.
x_scale : tensor(float)
Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional) : T
Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : tensor(float)
N-D full precision output tensor. It has same shape as input 'x'.
#### Type Constraints
T : tensor(int8), tensor(uint8), tensor(int32)
Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.
### **Div-13** Performs element-wise binary division (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
### **Dropout-13** Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
seed : int
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs (1 - 3)
data (differentiable) : T
The input data as Tensor.
ratio (optional, non-differentiable) : T1
The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
training_mode (optional, non-differentiable) : T2
If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.
#### Outputs (1 - 2)
output (differentiable) : T
The output.
mask (optional, non-differentiable) : T2
The output mask.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input 'ratio' types to float tensors.
T2 : tensor(bool)
Constrain output 'mask' types to boolean tensors.
### **Equal-13** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Erf-13** Computes the error function of the given input tensor element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The error function of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Exp-13** Calculates the exponential of the given input tensor, element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The exponential of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Expand-13** Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimensions must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
shape (non-differentiable) : tensor(int64)
A 1-D tensor indicates the shape you want to expand to, following the broadcast rule
#### Outputs
output (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensors.
### **Flatten-13** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output to all tensor types.
### **Floor-13** Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Gather-13** Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). It is an indexing operation that indexes into the input `data` along a single (specified) axis. Each entry in `indices` produces a `r-1` dimensional slice of the input tensor. The entire operation produces, conceptually, a `q`-dimensional tensor of `r-1` dimensional slices, which is arranged into a `q + (r-1)`-dimensional tensor, with the `q` dimensions taking the place of the original `axis` that is being indexed into. The following few examples illustrate how `Gather` works for specific shapes of `data`, `indices`, and given value of `axis`: | data shape | indices shape | axis | output shape | output equation | | --- | --- | --- | --- | --- | | (P, Q) | ( ) (a scalar) | 0 | (Q) | output[q] = data[indices, q] | | (P, Q, R) | ( ) (a scalar) | 1 | (P, R) | output[p, r] = data[p, indices, r] | | (P, Q) | (R, S) | 0 | (R, S, Q) | output[r, s, q] = data[ [indices[r, s], q] | | (P, Q) | (R, S) | 1 | (P, R, S) | output[p, r, s] = data[ p, indices[r, s]] | More generally, if `axis = 0`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` If `axis = 1`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of rank q + (r - 1).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **GatherElements-13** GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [1, 1], [4, 3], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [4, 8, 3], [7, 2, 3], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of the same shape as indices.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **GatherND-13** Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. **Example 1** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] ``` **Example 2** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] ``` **Example 3** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` **Example 4** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] ``` **Example 5** ``` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
batch_dims : int (default is 0)
The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **Gemm-13** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 * A' = transpose(A) if transA else A * B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md). This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B.
beta : float (default is 1.0)
Scalar multiplier for input tensor C.
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs (2 - 3)
A (differentiable) : T
Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (differentiable) : T
Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (optional, differentiable) : T
Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
#### Outputs
Y (differentiable) : T
Output tensor of shape (M, N).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
Constrain input and output types to float/int tensors.
### **Greater-13** Returns the tensor resulted from performing the `greater` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Hardmax-13** The operator computes the hardmax values for the given input: Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise The "axis" attribute indicates the dimension along which Hardmax will be performed. The output tensor has the same shape and contains the Hardmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Describes the dimension Hardmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Identity-13** Identity operator #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
Tensor to copy input into.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **If-13** If conditional #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
All Tensor and Sequence types
B : tensor(bool)
Only bool
### **IsNaN-13** Returns which elements of the input are NaN. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
### **LRN-13** Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element `X[n, c, d1, ..., dk]` in a tensor of shape `(N x C x D1 x D2, ..., Dk)`, its region is `{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}`. `square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)`, where `max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))`. `Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.0001)
Scaling parameter.
beta : float (default is 0.75)
The exponent.
bias : float (default is 1.0)
size : int (required)
The number of channels to sum over
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output tensor, which has the shape and type as input tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Less-13** Returns the tensor resulted from performing the `less` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Log-13** Calculates the natural log of the given input tensor, element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The natural log of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **LogSoftmax-13** The operator computes the log of softmax values for the given input: LogSoftmax(input, axis) = Log(Softmax(input, axis=axis)) The "axis" attribute indicates the dimension along which LogSoftmax will be performed. The output tensor has the same shape and contains the LogSoftmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Describes the dimension LogSoftmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Loop-13** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
All Tensor and Sequence types
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **MatMul-13** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (differentiable) : T
N-dimensional matrix A
B (differentiable) : T
N-dimensional matrix B
#### Outputs
Y (differentiable) : T
Matrix multiply results from A * B
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
Constrain input and output types to float/int tensors.
### **Max-13** Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for max.
#### Outputs
max (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **Mean-13** Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for mean.
#### Outputs
mean (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **MeanVarianceNormalization-13** A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: `(X-EX)/sqrt(E(X-EX)^2)` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints (default is ['0', '2', '3'])
A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Min-13** Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for min.
#### Outputs
min (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **Mod-13** Performs an element-wise binary modulo operation. The semantics and supported data types depend on the value of the `fmod` attribute which must be `0` (default), or `1`. If the `fmod` attribute is set to `0`, `T` is constrained to integer data types and the semantics follow that of the Python `%`-operator. The sign of the result is that of the divisor. If `fmod` is set to `1`, the behavior of this operator follows that of the `fmod` function in C and `T` is constrained to floating point data types. The result of this operator is the remainder of the division operation `x / y` where `x` and `y` are respective elements of `A` and `B`. The result is exactly the value `x - n * y`, where `n` is `x / y` with its fractional part truncated. The returned value has the same sign as `x` (except if `x` is `-0`) and is less or equal to `|y|` in magnitude. The following special cases apply when `fmod` is set to `1`: - If `x` is `-0` and `y` is greater than zero, either `+0` or `-0` may be returned. - If `x` is `±∞` and `y` is not `NaN`, `NaN` is returned. - If `y` is `±0` and `x` is not `NaN`, `NaN` should be returned. - If `y` is `±∞` and `x` is finite, `x` is returned. - If either argument is `NaN`, `NaN` is returned. This operator supports **multidirectional (i.e., NumPy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
fmod : int (default is 0)
Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment
#### Inputs
A (differentiable) : T
Dividend tensor
B (non-differentiable) : T
Divisor tensor
#### Outputs
C (differentiable) : T
Remainder tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
### **Mul-13** Performs element-wise binary multiplication (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
### **Neg-13** Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
Constrain input and output types to signed numeric tensors.
### **NegativeLogLikelihoodLoss-13** A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. ``` When an optional "weight" is provided, the sample loss is calculated as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. ``` loss is zero for the case when target-value equals ignore_index. ``` loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index ``` If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: ``` mean(loss), if "weight" is not provided, ``` or if weight is provided, ``` sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. ``` If "reduction" attribute is set to "sum", the output is a scalar: `sum(loss)`. See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: ``` // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] ``` Example 2: ``` // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 ``` Example 3: ``` // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.
#### Inputs (2 - 3)
input (differentiable) : T
Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).
target (non-differentiable) : Tind
Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.
weight (optional, non-differentiable) : T
Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.
#### Outputs
loss (differentiable) : T
The negative log likelihood loss
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input, weight, and output types to floating-point tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
### **NonZero-13** Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
input
#### Outputs
Y (non-differentiable) : tensor(int64)
output
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to all tensor types.
### **Pad-13** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`
#### Inputs (2 - 3)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Pow-13** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
First operand, base of the exponent.
Y (differentiable) : T1
Second operand, power of the exponent.
#### Outputs
Z (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input X and output types to float/int tensors.
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input Y types to float/int tensors.
### **QuantizeLinear-13** The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : tensor(float)
Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.
y_zero_point (optional) : T2
Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T2
N-D quantized output tensor. It has same shape as input 'x'.
#### Type Constraints
T1 : tensor(float), tensor(int32)
Constrain 'x' to float or int32 tensor.
T2 : tensor(int8), tensor(uint8)
Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.
### **Reciprocal-13** Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **ReduceL1-13** Computes the L1 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceL2-13** Computes the L2 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceLogSum-13** Computes the log sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceLogSumExp-13** Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceMax-13** Computes the max of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
Constrain input and output types to numeric tensors.
### **ReduceMean-13** Computes the mean of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields undefined. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceMin-13** Computes the min of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
Constrain input and output types to numeric tensors.
### **ReduceProd-13** Computes the product of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 1. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceSum-13** Computes the sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceSumSquare-13** Computes the sum square of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axes : list of ints
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **Relu-13** Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Reshape-13** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Resize-13** Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
coordinate_transformation_mode : string (default is half_pixel)
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).
cubic_coeff_a : float (default is -0.75)
The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".
exclude_outside : int (default is 0)
If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
extrapolation_value : float (default is 0.0)
When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
mode : string (default is nearest)
Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
nearest_mode : string (default is round_prefer_floor)
Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
#### Inputs (1 - 4)
X (differentiable) : T1
N-D tensor
roi (optional, non-differentiable) : T2
1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
scales (optional, non-differentiable) : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.
sizes (optional, non-differentiable) : tensor(int64)
The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.
#### Outputs
Y (differentiable) : T1
N-D tensor after resizing
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain roi type to float or double.
### **ScatterElements-13** ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates (differentiable) : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **ScatterND-13** ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more `updates` for the same index-location is not supported. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1.
updates (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **Shape-13** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Sigmoid-13** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Sign-13** Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (non-differentiable) : T
Input tensor
#### Outputs
output (non-differentiable) : T
The sign of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Size-13** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **Slice-13** Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor of its input `data` tensor. An effective `starts[i]`, `ends[i]`, and `steps[i]` must be computed for each `i` in `[0, ... r-1]` where `r = rank(input)` as follows: If `axes` are omitted, they are set to `[0, ..., r-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` The effective values are initialized as `start[i] = 0`, `ends[i] = dims[i]` where `dims` are the dimensions of `input` and `steps[i] = 1`. All negative elements of `axes` are made non-negative by adding `r` to them, where `r =rank(input)`. All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them, where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted `starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping and `[0, dims[axes[i]]-1]` for negative stepping. The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping `ends[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it is clamped to `[-1, dims[axes[i]]-1]`. Finally, `steps[axes[i]] = steps[i]`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward. Example 1: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] ``` Example 2: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (3 - 5)
data (differentiable) : T
Tensor of data to extract slices from.
starts (non-differentiable) : Tind
1-D tensor of starting indices of corresponding axis in `axes`
ends (non-differentiable) : Tind
1-D tensor of ending indices (exclusive) of corresponding axis in `axes`
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.
steps (optional, non-differentiable) : Tind
1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.
#### Outputs
output (differentiable) : T
Sliced data tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **Softmax-13** The operator computes the normalized exponential values for the given input: Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) The "axis" attribute indicates the dimension along which Softmax will be performed. The output tensor has the same shape and contains the Softmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Describes the dimension Softmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **SoftmaxCrossEntropyLoss-13** Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. * shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. * shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can calculated as follows: ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. ``` or ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. ``` loss is zero for the case when label-value equals ignore_index. ``` l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index ``` where: ``` p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] ``` Finally, L is optionally reduced: * If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). * If reduction = 'sum', the output is scalar: Sum(L). * If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: `ReduceSum(L) / ReduceSum(W)`, where tensor W is of shape `(N, D1, D2, ..., Dk)` and `W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.
#### Inputs (2 - 3)
scores (differentiable) : T
The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.
labels (non-differentiable) : Tind
The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.
weights (optional, non-differentiable) : T
A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.
#### Outputs (1 - 2)
output (differentiable) : T
Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.
log_prob (optional, differentiable) : T
Log probability tensor. If the output of softmax is prob, its value is log(prob).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
### **SpaceToDepth-13** SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
#### Inputs
input (differentiable) : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output (differentiable) : T
Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Split-13** Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using input 'split'. Otherwise, the tensor is split to equal sized parts. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
#### Inputs (1 - 2)
input (differentiable) : T
The tensor to split
split (optional, non-differentiable) : tensor(int64)
Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.
#### Outputs (1 - ∞)
outputs (variadic, differentiable) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Sqrt-13** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Squeeze-13** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Sub-13** Performs element-wise binary subtraction (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
### **Sum-13** Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for sum.
#### Outputs
sum (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **Tanh-13** Calculates the hyperbolic tangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic tangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tile-13** Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor of any shape.
repeats (non-differentiable) : T1
1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.
#### Outputs
output (differentiable) : T
Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(int64)
Constrain repeat's type to int64 tensors.
### **Transpose-13** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Unsqueeze-13** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 14 of the default ONNX operator set ### **Add-14** Performs element-wise binary addition (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **BatchNormalization-14** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
training_mode : int (default is 0)
If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
scale (differentiable) : T
Scale tensor of shape (C).
B (differentiable) : T
Bias tensor of shape (C).
input_mean (differentiable) : U
running (training) or estimated (testing) mean tensor of shape (C).
input_var (differentiable) : U
running (training) or estimated (testing) variance tensor of shape (C).
#### Outputs (1 - 3)
Y (differentiable) : T
The output tensor of the same shape as X
running_mean (optional, non-differentiable) : U
The running mean after the BatchNormalization operator.
running_var (optional, non-differentiable) : U
The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
U : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain mean and variance types to float tensors. It allows all float type for U.
### **CumSum-14** Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
exclusive : int (default is 0)
If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.
reverse : int (default is 0)
If set to 1 will perform the sums in reverse direction.
#### Inputs
x (differentiable) : T
An input tensor that is to be processed.
axis (non-differentiable) : T2
A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.
#### Outputs
y (differentiable) : T
Output tensor of the same type as 'x' with cumulative sums of the x's elements
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
T2 : tensor(int32), tensor(int64)
axis tensor can be int32 or int64 only
### **Div-14** Performs element-wise binary division (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **GRU-14** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `z` - update gate * `r` - reset gate * `h` - hidden gate * `t` - time step (t-1 means previous time step) * `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates * `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates * `Wb[zrh]` - W bias vectors for update, reset, and hidden gates * `Rb[zrh]` - R bias vectors for update, reset, and hidden gates * `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates * `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates * `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates * `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha * x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha * Tanh(beta * x) * HardSigmoid(x) - min(max(alpha * x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha * (e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): * zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) * rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) * ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 * ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 * Ht = (1 - zt) (.) ht + zt (.) Ht-1 This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
linear_before_reset : int (default is 0)
When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **HardSwish-14** HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Identity-14** Identity operator #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input and output types to all tensor and sequence types.
### **LSTM-14** Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `o` - output gate * `f` - forget gate * `c` - cell gate * `t` - time step (t-1 means previous time step) * `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates * `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates * `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates * `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates * `P[iof]` - P peephole weight vector for input, output, and forget gates * `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates * `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates * `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates * `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates * `PB[iof]` - P peephole weight vector for backward input, output, and forget gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): * it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) * ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) * ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) * Ct = ft (.) Ct-1 + it (.) ct * ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) * Ht = ot (.) h(Ct) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
input_forget : int (default is 0)
Couple the input and forget gates if 1.
layout : int (default is 0)
The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 8)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional, non-differentiable) : T
Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional, differentiable) : T
The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
#### Outputs (0 - 3)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional, differentiable) : T
The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Mul-14** Performs element-wise binary multiplication (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **RNN-14** Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `t` - time step (t-1 means previous time step) * `Wi` - W parameter weight matrix for input gate * `Ri` - R recurrence weight matrix for input gate * `Wbi` - W parameter bias vector for input gate * `Rbi` - R parameter bias vector for input gate * `WBi` - W parameter weight matrix for backward input gate * `RBi` - R recurrence weight matrix for backward input gate * `WBbi` - WR bias vectors for backward input gate * `RBbi` - RR bias vectors for backward input gate * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): * Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh'])
One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **Relu-14** Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
Constrain input and output types to signed numeric tensors.
### **Reshape-14** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
### **Sub-14** Performs element-wise binary subtraction (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
### **Trilu-14** Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute "upper" determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the "upper" attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
upper : int (default is 1)
Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.
#### Inputs (1 - 2)
input (differentiable) : T
Input tensor of rank 2 or higher.
k (optional, non-differentiable) : tensor(int64)
A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.
#### Outputs
output (differentiable) : T
Output tensor of the same type and shape as the input tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 15 of the default ONNX operator set ### **BatchNormalization-15** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
training_mode : int (default is 0)
If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
scale (differentiable) : T1
Scale tensor of shape (C).
B (differentiable) : T1
Bias tensor of shape (C).
input_mean (differentiable) : T2
running (training) or estimated (testing) mean tensor of shape (C).
input_var (differentiable) : T2
running (training) or estimated (testing) variance tensor of shape (C).
#### Outputs (1 - 3)
Y (differentiable) : T
The output tensor of the same shape as X
running_mean (optional, non-differentiable) : T2
The running mean after the BatchNormalization operator.
running_var (optional, non-differentiable) : T2
The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain scale and bias types to float tensors.
T2 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain mean and variance types to float tensors.
### **Bernoulli-15** Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p). This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified). #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Attributes
dtype : int
The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
All values in input have to be in the range:[0, 1].
#### Outputs
output : T2
The returned output tensor only has values 0 or 1, same shape as input tensor.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bool)
Constrain output types to all numeric tensors and bool tensors.
### **CastLike-15** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
Constrain output types. Casting to complex is not supported.
### **Optional-15** Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Attributes
type : type_proto
Type of the element in the optional output
#### Inputs (0 - 1)
input (optional) : V
The input element.
#### Outputs
output : O
The optional output enclosing the input element.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to all tensor and sequence types.
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain output type to all optional tensor or optional sequence types.
### **OptionalGetElement-15** Outputs the element in the optional-type input. It is an error if the input value does not have an element and the behavior is undefined in this case. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Inputs
input : O
The optional input.
#### Outputs
output : V
Output element in the optional input.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output type to all tensor or sequence types.
### **OptionalHasElement-15** Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Inputs
input : O
The optional input.
#### Outputs
output : B
A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
B : tensor(bool)
Constrain output to a boolean tensor.
### **Pow-15** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Inputs
X (differentiable) : T
First operand, base of the exponent.
Y (differentiable) : T1
Second operand, power of the exponent.
#### Outputs
Z (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input X and output types to float/int tensors.
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input Y types to float/int tensors.
### **Shape-15** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
## Version 16 of the default ONNX operator set ### **GreaterOrEqual-16** Returns the tensor resulted from performing the `greater_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **GridSample-16** Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from `grid`. Currently, only spatial (4-D) inputs are supported. For input `X` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). The tensor `X` contains values at centers of square pixels in a H by W 2-dimensional image. The tensor `grid` describes normalized positions where the output `Y` is to be computed using a specified interpolation method (the mode) and a padding mode (for grid positions falling outside the 2-dimensional image). Elements in `grid[N, H_out, W_out]` are size-2 vectors specifying positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[N, C, H_out, W_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
align_corners : int (default is 0)
If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.
mode : string (default is bilinear)
Three interpolation modes: bilinear (default), nearest and bicubic.
padding_mode : string (default is zeros)
Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.
#### Inputs
X (differentiable) : T1
4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.
grid (non-differentiable) : T2
Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.
#### Outputs
Y (differentiable) : T1
4-D tensor of shape (N, C, H_out, W_out) of sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input `X` and output `Y` types to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
### **Identity-16** Identity operator #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-16** If conditional #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.
B : tensor(bool)
Only bool
### **LeakyRelu-16** LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.01)
Coefficient of leakage.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LessOrEqual-16** Returns the tensor resulted from performing the `less_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Loop-16** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **PRelu-16** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
slope (differentiable) : T
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
#### Outputs
Y (differentiable) : T
Output tensor (same size as X)
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
### **RoiAlign-16** Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
coordinate_transformation_mode : string (default is half_pixel)
Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).
mode : string (default is avg)
The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.
output_height : int (default is 1)
default 1; Pooled output Y's height.
output_width : int (default is 1)
default 1; Pooled output Y's width.
sampling_ratio : int (default is 0)
Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f.
#### Inputs
X : T1
Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois : T1
RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
batch_indices : T2
1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
#### Outputs
Y : T1
RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double)
Constrain types to float tensors.
T2 : tensor(int64)
Constrain types to int tensors.
### **Scan-16** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
All Tensor types up to IRv4.
### **ScatterElements-16** ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] += updates[i][j] if axis = 0, output[i][indices[i][j]] += updates[i][j] if axis = 1, ``` When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] *= updates[i][j] if axis = 0, output[i][indices[i][j]] *= updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates (differentiable) : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **ScatterND-16** ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to "add", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] += updates[idx] When `reduction` is set to "mul", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] *= updates[idx] This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Attributes
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1.
updates (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **Where-16** Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. #### Inputs
condition (non-differentiable) : B
When True (nonzero), yield X, otherwise yield Y
X (differentiable) : T
values selected at indices where condition is True
Y (differentiable) : T
values selected at indices where condition is False
#### Outputs
output (differentiable) : T
Tensor of shape equal to the broadcasted shape of condition, X, and Y.
#### Type Constraints
B : tensor(bool)
Constrain to boolean tensors.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types (including bfloat).
## Version 17 of the default ONNX operator set ### **BlackmanWindow-17** Generates a Blackman window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Blackman window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
### **DFT-17** Computes the discrete Fourier transform of input. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.
inverse : int (default is 0)
Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.
onesided : int (default is 0)
If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.
#### Inputs (1 - 2)
input (non-differentiable) : T1
For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.
dft_length (optional, non-differentiable) : T2
The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value.
#### Outputs
output : T1
The Fourier Transform of the input vector. If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain scalar length types to int64_t.
### **HammingWindow-17** Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Hamming window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
### **HannWindow-17** Generates a Hann window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Hann window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
### **LayerNormalization-17** This is layer normalization defined in ONNX as function. The overall computation can be split into two stages. The first stage is standardization, which makes the normalized elements have zero mean and unit variances. The computation required by standardization can be described by the following equations. ``` Mean = ReduceMean(X) D = Sub(X, Mean) DD = Mul(D, D) Var = ReduceMean(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(D, InvStdDev) ``` where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `Var` and `StdDev` stand for variance and standard deviation, respectively. The second output is `Mean` and the last one is `InvStdDev`. Depending on `stash_type` attribute, the actual computation must happen in different floating-point precision. For example, if `stash_type` is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast `Normalized` back to the original type of `X`. The second stage then scales and shifts the outcome of the first stage using ``` NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B) ``` The second stage doesn't depends on `stash_type`. All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md). The same variable (i.e., input, output, and attribute) uses the same name in the equations above and this operator's definition. Let `d[i]` indicate the i-th dimension of `X`. If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`, the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`. `Y` and `X` have the same shape. This operator supports unidirectional broadcasting (tensors `Scale` and `B` should be unidirectional broadcastable to tensor `X`); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
stash_type : int (default is 1)
Type of Mean and InvStdDev. This also specifies stage one's computation precision.
#### Inputs (2 - 3)
X : T
Tensor to be normalized.
Scale : T
Scale tensor.
B (optional) : T
Bias tensor.
#### Outputs (1 - 3)
Y : T
Normalized tensor.
Mean (optional) : U
Saved mean used during training to speed up gradient computation
InvStdDev (optional) : U
Saved inverse standard deviation used during training to speed up gradient computation.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types and output Y type to float tensors.
U : tensor(float), tensor(bfloat16)
Type of Mean and InvStdDev tensors.
### **MelWeightMatrix-17** Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale. This function defines the mel scale in terms of a frequency in hertz according to the following formula: mel(f) = 2595 * log10(1 + f/700) In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0. The returned MelWeightMatrix can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram" M of shape [frames, num_mel_bins]. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT.
#### Inputs
num_mel_bins (non-differentiable) : T1
The number of bands in the mel spectrum.
dft_length (non-differentiable) : T1
The size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.
sample_rate (non-differentiable) : T1
Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.
lower_edge_hertz (non-differentiable) : T2
Lower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.
upper_edge_hertz (non-differentiable) : T2
The desired top edge of the highest frequency band.
#### Outputs
output (non-differentiable) : T3
The Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain to integer tensors.
T2 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
Constrain to float tensors
T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain to any numerical types.
### **STFT-17** Computes the Short-time Fourier Transform of the signal. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
onesided : int (default is 1)
If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.
#### Inputs (2 - 4)
signal (non-differentiable) : T1
Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.
frame_step (non-differentiable) : T2
The number of samples to step between successive DFTs.
window (optional, non-differentiable) : T1
A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value.
frame_length (optional, non-differentiable) : T2
A scalar representing the size of the DFT. It's an optional value.
#### Outputs
output (non-differentiable) : T1
The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
Constrain signal and output to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain scalar length types to int64_t.
### **SequenceMap-17** Applies a sub-graph to each sample in the input sequence(s). Inputs can be either tensors or sequences, with the exception of the first input which must be a sequence. The length of the first input sequence will determine the number of samples in the outputs. Any other sequence inputs should have the same number of samples. The number of inputs and outputs, should match the one of the subgraph. For each i-th element in the output, a sample will be extracted from the input sequence(s) at the i-th position and the sub-graph will be applied to it. The outputs will contain the outputs of the sub-graph for each sample, in the same order as in the input. This operator assumes that processing each sample is independent and could executed in parallel or in any order. Users cannot expect any specific ordering in which each subgraph is computed. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.
#### Inputs (1 - ∞)
input_sequence : S
Input sequence.
additional_inputs (variadic, heterogeneous) : V
Additional inputs to the graph
#### Outputs (1 - ∞)
out_sequence (variadic, heterogeneous) : S
Output sequence(s)
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input types to any sequence type.
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor or sequence type.
## Version 18 of the default ONNX operator set ### **BitwiseAnd-18** Returns the tensor resulting from performing the bitwise `and` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
### **BitwiseNot-18** Returns the bitwise not of the input tensor element-wise. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input/output to integer tensors.
### **BitwiseOr-18** Returns the tensor resulting from performing the bitwise `or` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
### **BitwiseXor-18** Returns the tensor resulting from performing the bitwise `xor` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
### **CenterCropPad-18** Center crop or pad an input to given dimensions. The crop/pad dimensions can be specified for a subset of the `axes`; unspecified dimensions will remain unchanged. If the input dimensions are larger than the target crop dimensions, a centered cropping window will be extracted from the input. The starting value for the cropping window is rounded down, which means that if the difference between the input shape and the crop shape is odd, the cropping window will be shifted half a pixel to the left of the input center. If the input dimensions are smaller than the target crop dimensions, the input will be padded equally on both sides to center it in the output. In cases where the total number of padding pixels is odd, an additional pixel will be added to the right side. The padding value used is zero. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
axes : list of ints
If provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
#### Inputs
input_data (differentiable) : T
Input to extract the centered crop from.
shape (non-differentiable) : Tind
1-D tensor representing the cropping window dimensions.
#### Outputs
output_data (differentiable) : T
Output data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **Col2Im-18** The operator rearranges column blocks back into a multidimensional image Col2Im behaves similarly to PyTorch's fold https://pytorch.org/docs/stable/generated/torch.nn.Fold.html, but it only supports *batched* multi-dimensional image tensors. Another implementation in Python with N-dimension support can be found at https://github.com/f-dangel/unfoldNd/. NOTE: Although specifying image_shape looks redundant because it could be calculated from convolution formulas, it is required as input for more advanced scenarios as explained at PyTorch's implementation (https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Col2Im.cpp#L10) #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
dilations : list of ints
1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.
pads : list of ints
1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
input (differentiable) : T
Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).
image_shape (non-differentiable) : tensor(int64)
The shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.
block_shape (non-differentiable) : tensor(int64)
The shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.
#### Outputs
output (differentiable) : T
Output tensor produced by rearranging blocks into an image.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all numeric tensor types.
### **GroupNormalization-18** (deprecated) A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494 This operator transforms input according to ``` y = scale * (x - mean) / sqrt(variance + epsilon) + bias, ``` where the mean and variance are computed per instance per group of channels, and `scale` and `bias` should be specified for each group of channels. The number of groups `num_groups` should be divisible by the number of channels so that there are an equal number of channels per group. When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization. #### Version This version of the operator has been deprecated since version 18 of the default ONNX operator set. ### **LpPool-18** LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Mish-18** Mish: A Self Regularized Non-Monotonic Neural Activation Function. Perform the linear unit element-wise on the input tensor X using formula: ``` mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x})) ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input X and output types to float tensors.
### **OptionalGetElement-18** If the input is a tensor or sequence type, it returns the input. If the input is an optional type, it outputs the element in the input. It is an error if the input is an empty optional-type (i.e. does not have an element) and the behavior is undefined in this case. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
input : O
The optional input.
#### Outputs
output : V
Output element in the optional input.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output type to all tensor or sequence types.
### **OptionalHasElement-18** Returns true if (1) the input is an optional-type and contains an element, or, (2) the input is a tensor or sequence type. If the input is not provided or is an empty optional-type, this op returns false. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs (0 - 1)
input (optional) : O
The optional input.
#### Outputs
output : B
A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
B : tensor(bool)
Constrain output to a boolean tensor.
### **Pad-18** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **ReduceL1-18** Computes the L1 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceL2-18** Computes the L2 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceLogSum-18** Computes the log sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceLogSumExp-18** Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceMax-18** Computes the max of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
Constrain input and output types to numeric tensors.
### **ReduceMean-18** Computes the mean of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields undefined. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceMin-18** Computes the min of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
Constrain input and output types to numeric tensors.
### **ReduceProd-18** Computes the product of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 1. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **ReduceSumSquare-18** Computes the sum square of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
### **Resize-18** Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is:
`output_dimension = floor(input_dimension * (roi_end - roi_start) * scale)`
if input \"sizes\" is not specified. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
antialias : int (default is 0)
If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.
axes : list of ints
If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
coordinate_transformation_mode : string (default is half_pixel)
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` .
cubic_coeff_a : float (default is -0.75)
The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".
exclude_outside : int (default is 0)
If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
extrapolation_value : float (default is 0.0)
When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
keep_aspect_ratio_policy : string (default is stretch)
This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[d])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[d])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.
mode : string (default is nearest)
Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
nearest_mode : string (default is round_prefer_floor)
Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
#### Inputs (1 - 4)
X (differentiable) : T1
N-D tensor
roi (optional, non-differentiable) : T2
1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
scales (optional, non-differentiable) : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.
sizes (optional, non-differentiable) : tensor(int64)
Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified.
#### Outputs
Y (differentiable) : T1
N-D tensor after resizing
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain roi type to float or double.
### **ScatterElements-18** ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to some reduction function `f`, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0, output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1, ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates (differentiable) : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **ScatterND-18** ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = updates[idx] ``` The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to some reduction function `f`, `output` is calculated as follows: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = f(output[tuple(indices[idx])], updates[idx]) ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherND. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1.
updates (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
### **Split-18** Split a tensor into a list of tensors, along the specified 'axis'. Either input 'split' or the attribute 'num_outputs' should be specified, but not both. If the attribute 'num_outputs' is specified, then the tensor is split into equal sized parts. If the tensor is not evenly splittable into `num_outputs`, the last chunk will be smaller. If the input 'split' is specified, it indicates the sizes of each output in the split. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
num_outputs : int
Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.
#### Inputs (1 - 2)
input (differentiable) : T
The tensor to split
split (optional, non-differentiable) : tensor(int64)
Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.
#### Outputs (1 - ∞)
outputs (variadic, differentiable) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
## Version 19 of the default ONNX operator set ### **AveragePool-19** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cast-19** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | NaN | FLT_MAX | NaN | | -Inf | -FLT_MAX | NaN | -FLT_MAX | NaN | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain output types. Casting to complex is not supported.
### **CastLike-19** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain output types. Casting to complex is not supported.
### **Constant-19** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input and output types to all tensor types.
### **DeformConv-19** Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
dilations : list of ints
Dilation value along each spatial axis of the kernel. Default is 1 along each axis.
group : int (default is 1)
Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.
kernel_shape : list of ints
Shape of the convolution kernel. If not present, it is inferred from the shape of input W.
offset_group : int (default is 1)
Number of groups of offset. C must be divisible by offset_group. Default is 1.
pads : list of ints
Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.
strides : list of ints
Stride along each spatial axis. Default is 1 along each axis.
#### Inputs (3 - 5)
X : T
Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.
W : T
Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).
offset : T
Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.
B (optional) : T
Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.
mask (optional) : T
The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.
#### Outputs
Y : T
Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD data
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **DequantizeLinear-19** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. `x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization, but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional) : T1
Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T2
N-D full precision output tensor. It has same shape as input 'x'.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.
T2 : tensor(float), tensor(float16), tensor(bfloat16)
'x_scale' determines the output type.
### **Equal-19** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(string)
Constrain input types to all (non-complex) tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Identity-19** Identity operator #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-19** If conditional #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.
B : tensor(bool)
Only bool
### **Loop-19** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **Pad-19** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **QuantizeLinear-19** The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is `y = saturate ((x / y_scale) + y_zero_point)`. For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. 'y_zero_point' is usually not used for quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, but the quantization formula remains the same for consistency and the type of the attribute 'y_zero_point' still determines the quantization type. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T1
Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.
y_zero_point (optional) : T2
Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T2
N-D quantized output tensor. It has same shape as input 'x'.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
Constrain 'x' to float, float16, bfloat16 or int32 tensor.
T2 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.
### **Reshape-19** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input and output types to all tensor types.
### **Resize-19** Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: ``` output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) ``` if input \"sizes\" is not specified. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
antialias : int (default is 0)
If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.
axes : list of ints
If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
coordinate_transformation_mode : string (default is half_pixel)
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .
cubic_coeff_a : float (default is -0.75)
The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".
exclude_outside : int (default is 0)
If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
extrapolation_value : float (default is 0.0)
When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
keep_aspect_ratio_policy : string (default is stretch)
This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.
mode : string (default is nearest)
Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
nearest_mode : string (default is round_prefer_floor)
Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
#### Inputs (1 - 4)
X (differentiable) : T1
N-D tensor
roi (optional, non-differentiable) : T2
1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
scales (optional, non-differentiable) : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.
sizes (optional, non-differentiable) : tensor(int64)
Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified.
#### Outputs
Y (differentiable) : T1
N-D tensor after resizing
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain roi type to float or double.
### **Scan-19** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
All Tensor types up to IRv9.
### **Shape-19** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Size-19** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
## Version 20 of the default ONNX operator set ### **AffineGrid-20** Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta (https://pytorch.org/docs/stable/generated/torch.nn.functional.affine_grid.html). An affine matrix `theta` is applied to a position tensor represented in its homogeneous expression. Here is an example in 3D: ``` [r00, r01, r02, t0] [x] [x'] [r10, r11, r12, t1] * [y] = [y'] [r20, r21, r22, t2] [z] [z'] [0, 0, 0, 1 ] [1] [1 ] ``` where `(x, y, z)` is the position in the original space, `(x', y', z')` is the position in the output space. The last row is always `[0, 0, 0, 1]` and is not stored in the affine matrix. Therefore we have `theta` of shape `(N, 2, 3)` for 2D or `(N, 3, 4)` for 3D. Input `size` is used to define grid of positions evenly spaced in the original 2D or 3D space, with dimensions ranging from `-1` to `1`. The output `grid` contains positions in the output space. When `align_corners=1`, consider `-1` and `1` to refer to the centers of the corner pixels (mark `v` in illustration). ``` v v v v |-------------------|------------------| -1 0 1 ``` When `align_corners=0`, consider `-1` and `1` to refer to the outer edge of the corner pixels. ``` v v v v |------------------|-------------------| -1 0 1 ``` #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
align_corners : int (default is 0)
if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.
#### Inputs
theta (non-differentiable) : T1
input batch of affine matrices with shape (N, 2, 3) for 2D or (N, 3, 4) for 3D
size (non-differentiable) : T2
the target output image size (N, C, H, W) for 2D or (N, C, D, H, W) for 3D
#### Outputs
grid (differentiable) : T1
output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
T2 : tensor(int64)
Constrain size's type to int64 tensors.
### **ConstantOfShape-20** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain output types to be numerics.
### **DFT-20** Computes the discrete Fourier Transform (DFT) of the input. Assuming the input has shape `[M, N]`, where `N` is the dimension over which the DFT is computed and `M` denotes the conceptual "all other dimensions," the DFT `y[m, k]` of shape `[M, N]` is defined as $$y[m, k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[m, n] ,$$ and the inverse transform is defined as $$x[m, n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[m, k] ,$$ where $j$ is the imaginary unit. The actual shape of the output is specified in the "output" section. Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
inverse : int (default is 0)
Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.
onesided : int (default is 0)
If `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.
#### Inputs (1 - 3)
input (non-differentiable) : T1
For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.
dft_length (optional, non-differentiable) : T2
The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal.
axis (optional, non-differentiable) : tensor(int64)
The axis as a scalar on which to perform the DFT. Default is `-2` (last signal axis). Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.
#### Outputs
output : T1
The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain scalar length types to integers.
### **Gelu-20** Gelu takes one input data (Tensor) and produces one output data (Tensor) where the gaussian error linear units function, $y = 0.5 * x * (1 + erf(x/sqrt(2)))$ is applied to the tensor elementwise. If the attribute "approximate" is set to "tanh", the function estimation, $y = 0.5 * x * (1 + Tanh(sqrt(2/\pi) * (x + 0.044715 * x^3)))$ is used and applied to the tensor elementwise. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
approximate : string (default is none)
Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
### **GridSample-20** Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from the `grid`. For spatial input `X` with shape (N, C, H, W), the `grid` will have shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). For volumetric input `X` with shape (N, C, D, H, W), the `grid` will have shape (N, D_out, H_out, W_out, 3), the output `Y` will have shape (N, C, D_out, H_out, W_out). More generally, for an input `X` of rank r+2 with shape (N, C, d1, d2, ..., dr), the `grid` will have shape (N, D1_out, D2_out, ..., Dr_out, r), the output `Y` will have shape (N, C, D1_out, D2_out, ..., Dr_out). The tensor `X` contains values at centers of square pixels (voxels, etc) locations such as (n, c, d1_in, d2_in, ..., dr_in). The (n, d1_out, d2_out, ..., dr_out, :) values from the tensor `grid` are the normalized positions for interpolating the values at the (n, c, d1_out, d2_out, ..., dr_out) locations from the output tensor `Y` using a specified interpolation method (the mode) and a padding mode (for `grid` positions falling outside the 2-dimensional image). For example, the values in `grid[n, h_out, w_out, :]` are size-2 vectors specifying normalized positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[n, c, h_out, w_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html). #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
align_corners : int (default is 0)
If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.
mode : string (default is linear)
Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.
padding_mode : string (default is zeros)
Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.
#### Inputs
X (differentiable) : T1
Input tensor of rank r+2 that has shape (N, C, D1, D2, ..., Dr), where N is the batch size, C is the number of channels, D1, D2, ..., Dr are the spatial dimensions.
grid (non-differentiable) : T2
Input offset of shape (N, D1_out, D2_out, ..., Dr_out, r), where D1_out, D2_out, ..., Dr_out are the spatial dimensions of the grid and output, and r is the number of spatial dimensions. Grid specifies the sampling locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If the grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode. Following computer vision convention, the coordinates in the length-r location vector are listed from the innermost tensor dimension to the outermost, the opposite of regular tensor indexing.
#### Outputs
Y (differentiable) : T1
Output tensor of rank r+2 that has shape (N, C, D1_out, D2_out, ..., Dr_out) of the sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input `X` and output `Y` types to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
### **ImageDecoder-20** Loads and decodes and image from a file. If it can't decode for any reason (e.g. corrupted encoded stream, invalid format, it will return an empty matrix). The following image formats are supported: * BMP * JPEG (note: Lossless JPEG support is optional) * JPEG2000 * TIFF * PNG * WebP * Portable image format (PBM, PGM, PPM, PXM, PNM) Decoded images follow a channel-last layout: (Height, Width, Channels). **JPEG chroma upsampling method:** When upsampling the chroma components by a factor of 2, the pixels are linearly interpolated so that the centers of the output pixels are 1/4 and 3/4 of the way between input pixel centers. When rounding, 0.5 is rounded down and up at alternative pixels locations to prevent bias towards larger values (ordered dither pattern). Considering adjacent input pixels A, B, and C, B is upsampled to pixels B0 and B1 so that ``` B0 = round_half_down((1/4) * A + (3/4) * B) B1 = round_half_up((3/4) * B + (1/4) * C) ``` This method, is the default chroma upsampling method in the well-established libjpeg-turbo library, also referred as "smooth" or "fancy" upsampling. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
pixel_format : string (default is RGB)
Pixel format. Can be one of "RGB", "BGR", or "Grayscale".
#### Inputs
encoded_stream (non-differentiable) : T1
Encoded stream
#### Outputs
image (non-differentiable) : T2
Decoded image
#### Type Constraints
T1 : tensor(uint8)
Constrain input types to 8-bit unsigned integer tensor.
T2 : tensor(uint8)
Constrain output types to 8-bit unsigned integer tensor.
### **IsInf-20** Map infinity to true and other values to false. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
detect_negative : int (default is 1)
(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.
detect_positive : int (default is 1)
(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.
#### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
### **IsNaN-20** Returns which elements of the input are NaN. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
### **ReduceMax-20** Computes the max of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8), tensor(bool)
Constrain input and output types to numeric and Boolean tensors.
### **ReduceMin-20** Computes the min of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8), tensor(bool)
Constrain input and output types to numeric and Boolean tensors.
### **RegexFullMatch-20** RegexFullMatch performs a full regex match on each element of the input tensor. If an element fully matches the regex pattern specified as an attribute, the corresponding element in the output is True and it is False otherwise. [RE2](https://github.com/google/re2/wiki/Syntax) regex syntax is used. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
pattern : string
Regex pattern to match on. This must be valid RE2 syntax.
#### Inputs
X (non-differentiable) : T1
Tensor with strings to match on.
#### Outputs
Y (non-differentiable) : T2
Tensor of bools indicating if each input string fully matches the regex pattern specified.
#### Type Constraints
T1 : tensor(string)
Inputs must be UTF-8 strings
T2 : tensor(bool)
Outputs are bools and are True where there is a full regex match and False otherwise.
### **StringConcat-20** StringConcat concatenates string tensors elementwise (with NumPy-style broadcasting support) #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Tensor to prepend in concatenation
Y (non-differentiable) : T
Tensor to append in concatenation
#### Outputs
Z (non-differentiable) : T
Concatenated string tensor
#### Type Constraints
T : tensor(string)
Inputs and outputs must be UTF-8 strings
### **StringSplit-20** StringSplit splits a string tensor's elements into substrings based on a delimiter attribute and a maxsplit attribute. The first output of this operator is a tensor of strings representing the substrings from splitting each input string on the `delimiter` substring. This tensor has one additional rank compared to the input tensor in order to store the substrings for each input element (where the input tensor is not empty). Note that, in order to ensure the same number of elements are present in the final dimension, this tensor will pad empty strings as illustrated in the examples below. Consecutive delimiters are not grouped together and are deemed to delimit empty strings, except if the `delimiter` is unspecified or is the empty string (""). In the case where the `delimiter` is unspecified or the empty string, consecutive whitespace characters are regarded as a single separator and leading or trailing whitespace is removed in the output. The second output tensor represents the number of substrings generated. `maxsplit` can be used to limit the number of splits performed - after the `maxsplit`th split if the string is not fully split, the trailing suffix of input string after the final split point is also added. For elements where fewer splits are possible than specified in `maxsplit`, it has no effect. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
delimiter : string
Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.
maxsplit : int
Maximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.
#### Inputs
X (non-differentiable) : T1
Tensor of strings to split.
#### Outputs
Y (non-differentiable) : T2
Tensor of substrings representing the outcome of splitting the strings in the input on the delimiter. Note that to ensure the same number of elements are present in the final rank, this tensor will pad any necessary empty strings.
Z (non-differentiable) : T3
The number of substrings generated for each input element.
#### Type Constraints
T1 : tensor(string)
The input must be a UTF-8 string tensor
T2 : tensor(string)
Tensor of substrings.
T3 : tensor(int64)
The number of substrings generated.
## Version 21 of the default ONNX operator set ### **Cast-21** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | NaN | FLT_MAX | NaN | | -Inf | -FLT_MAX | NaN | -FLT_MAX | NaN | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input types. Casting from complex is not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain output types. Casting to complex is not supported.
### **CastLike-21** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain output types. Casting to complex is not supported.
### **Constant-21** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types.
### **ConstantOfShape-21** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint4), tensor(int4), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain output types to be numerics or boolean.
### **DequantizeLinear-21** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 types quantization, but the dequantization formula remains the same for consistency, and `x_scale` still determines the output type. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
x_zero_point (optional) : T1
Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T2
N-D full precision output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
The type of the inputs 'x_zero_point' and 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16)
'x_scale' determines the output type.
### **Flatten-21** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output to all tensor types up to IRv10.
### **GroupNormalization-21** A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494 This operator transforms input according to ``` y = scale * (x - mean) / sqrt(variance + epsilon) + bias, ``` where the mean and variance are computed per instance per group of channels, and `scale` and `bias` should be specified for each channel. The number of groups `num_groups` should be divisible by the number of channels so that there are an equal number of channels per group. The overall computation has two stages: the first stage normalizes the elements to have zero mean and unit variance for each instance in each group, and the second stage scales and shifts the results of the first stage. The floating-point precision used in the first stage is determined by the `stash_type` attribute. For example, if `stash_type` is 1, the operator casts all input variables to 32-bit float, performs the computation, and finally casts the normalized results back to the original type of `X`. The second stage does not depend on `stash_type`. When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
num_groups : int (required)
The number of groups of channels. It should be a divisor of the number of channels `C`.
stash_type : int (default is 1)
The floating-point precision used in stage one of the computation.
#### Inputs
X (differentiable) : T
Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.
scale (differentiable) : T
Scale tensor of shape `(C)`.
bias (differentiable) : T
Bias tensor of shape `(C)`.
#### Outputs
Y (differentiable) : T
The output tensor of the same shape as `X`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Identity-21** Identity operator #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-21** If conditional #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.
B : tensor(bool)
Only bool
### **Loop-21** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **Pad-21** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types up to IRv10.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **QLinearMatMul-21** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Inputs
a (non-differentiable) : T1
N-dimensional quantized matrix a
a_scale (non-differentiable) : TS
scale of quantized input a
a_zero_point (non-differentiable) : T1
zero point of quantized input a
b (non-differentiable) : T2
N-dimensional quantized matrix b
b_scale (non-differentiable) : TS
scale of quantized input b
b_zero_point (non-differentiable) : T2
zero point of quantized input b
y_scale (non-differentiable) : TS
scale of quantized output y
y_zero_point (non-differentiable) : T3
zero point of quantized output y
#### Outputs
y (non-differentiable) : T3
Quantized matrix multiply results from a * b
#### Type Constraints
TS : tensor(float), tensor(float16), tensor(bfloat16)
Constrain scales.
T1 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of input a and its zeropoint.
T2 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of input b and its zeropoint.
T3 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of the output and its zeropoint.
### **QuantizeLinear-21** The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T1
Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.
y_zero_point (optional) : T2
Zero point for doing quantization to get `y`. Shape must match `y_scale`.Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T2
N-D quantized output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'x'.
T2 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
The type of the input `y_zero_point` and the output `y`.
### **Reshape-21** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types.
### **Scan-21** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
All Tensor types up to IRv10.
### **Shape-21** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Size-21** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **Squeeze-21** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types up to IRv10.
### **Transpose-21** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types.
### **Unsqueeze-21** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4)
Constrain input and output types to all tensor types up to IRv10.
## Version 22 of the default ONNX operator set ### **Acos-22** Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arccosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Acosh-22** Calculates the hyperbolic arccosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arccosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Asin-22** Calculates the arcsine (inverse of sine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arcsine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Asinh-22** Calculates the hyperbolic arcsine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arcsine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Atan-22** Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arctangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Atanh-22** Calculates the hyperbolic arctangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arctangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **AveragePool-22** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Bernoulli-22** Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p). This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int
The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
All values in input have to be in the range:[0, 1].
#### Outputs
output : T2
The returned output tensor only has values 0 or 1, same shape as input tensor.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output types to all numeric tensors and bool tensors.
### **Conv-22** The convolution operator consumes an input tensor and a filter, and computes the output. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ConvTranspose-22** The convolution transpose operator consumes an input tensor and a filter, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding : list of ints
Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.
output_shape : list of ints
The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cos-22** Calculates the cosine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The cosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Cosh-22** Calculates the hyperbolic cosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic cosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **DeformConv-22** Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dilations : list of ints
Dilation value along each spatial axis of the kernel. Default is 1 along each axis.
group : int (default is 1)
Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.
kernel_shape : list of ints
Shape of the convolution kernel. If not present, it is inferred from the shape of input W.
offset_group : int (default is 1)
Number of groups of offset. C must be divisible by offset_group. Default is 1.
pads : list of ints
Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.
strides : list of ints
Stride along each spatial axis. Default is 1 along each axis.
#### Inputs (3 - 5)
X : T
Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.
W : T
Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).
offset : T
Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.
B (optional) : T
Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.
mask (optional) : T
The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.
#### Outputs
Y : T
Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD data
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Det-22** Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions, and the inner-most 2 dimensions form square matrices. The output is a tensor of shape `[*]`, containing the determinants of all input submatrices. e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to floating-point tensors.
### **Dropout-22** Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
seed : int
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs (1 - 3)
data (differentiable) : T
The input data as Tensor.
ratio (optional, non-differentiable) : T1
The ratio of random dropout, with value in [0, 1). If set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
training_mode (optional, non-differentiable) : T2
If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.
#### Outputs (1 - 2)
output (differentiable) : T
The output.
mask (optional, non-differentiable) : T2
The output mask.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input and output types to float tensors.
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input 'ratio' types to float tensors.
T2 : tensor(bool)
Constrain output 'mask' types to boolean tensors.
### **Elu-22** Elu takes one input data (Tensor) and produces one output data (Tensor) where the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Coefficient of ELU.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **EyeLike-22** Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the 'dtype' argument. If 'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor. If not specified, the data type of the input tensor T1 is used.
k : int (default is 0)
(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.
#### Inputs
input : T1
2D input tensor to copy shape, and optionally, type information from.
#### Outputs
output : T2
Output tensor, same shape as input tensor T1.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain input types. Strings and complex are not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output types. Strings and complex are not supported.
### **GRU-22** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `z` - update gate * `r` - reset gate * `h` - hidden gate * `t` - time step (t-1 means previous time step) * `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates * `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates * `Wb[zrh]` - W bias vectors for update, reset, and hidden gates * `Rb[zrh]` - R bias vectors for update, reset, and hidden gates * `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates * `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates * `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates * `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha * x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha * Tanh(beta * x) * HardSigmoid(x) - min(max(alpha * x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha * (e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): * zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) * rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) * ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 * ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 * Ht = (1 - zt) (.) ht + zt (.) Ht-1 This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
linear_before_reset : int (default is 0)
When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **GlobalAveragePool-22** GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalLpPool-22** GlobalLpPool consumes an input tensor X and applies lp pool pooling across the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalMaxPool-22** GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GridSample-22** Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from the `grid`. For spatial input `X` with shape (N, C, H, W), the `grid` will have shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). For volumetric input `X` with shape (N, C, D, H, W), the `grid` will have shape (N, D_out, H_out, W_out, 3), the output `Y` will have shape (N, C, D_out, H_out, W_out). More generally, for an input `X` of rank r+2 with shape (N, C, d1, d2, ..., dr), the `grid` will have shape (N, D1_out, D2_out, ..., Dr_out, r), the output `Y` will have shape (N, C, D1_out, D2_out, ..., Dr_out). The tensor `X` contains values at centers of square pixels (voxels, etc) locations such as (n, c, d1_in, d2_in, ..., dr_in). The (n, d1_out, d2_out, ..., dr_out, :) values from the tensor `grid` are the normalized positions for interpolating the values at the (n, c, d1_out, d2_out, ..., dr_out) locations from the output tensor `Y` using a specified interpolation method (the mode) and a padding mode (for `grid` positions falling outside the 2-dimensional image). For example, the values in `grid[n, h_out, w_out, :]` are size-2 vectors specifying normalized positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[n, c, h_out, w_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
align_corners : int (default is 0)
If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.
mode : string (default is linear)
Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.
padding_mode : string (default is zeros)
Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.
#### Inputs
X (differentiable) : T1
Input tensor of rank r+2 that has shape (N, C, D1, D2, ..., Dr), where N is the batch size, C is the number of channels, D1, D2, ..., Dr are the spatial dimensions.
grid (non-differentiable) : T2
Input offset of shape (N, D1_out, D2_out, ..., Dr_out, r), where D1_out, D2_out, ..., Dr_out are the spatial dimensions of the grid and output, and r is the number of spatial dimensions. Grid specifies the sampling locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If the grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode. Following computer vision convention, the coordinates in the length-r location vector are listed from the innermost tensor dimension to the outermost, the opposite of regular tensor indexing.
#### Outputs
Y (differentiable) : T1
Output tensor of rank r+2 that has shape (N, C, D1_out, D2_out, ..., Dr_out) of the sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input `X` and output `Y` types to all tensor types.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
### **HardSigmoid-22** HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
alpha : float (default is 0.2)
Value of alpha.
beta : float (default is 0.5)
Value of beta.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **HardSwish-22** HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **InstanceNormalization-22** Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
#### Inputs
input (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale (differentiable) : T
The input 1-dimensional scale tensor of size C.
B (differentiable) : T
The input 1-dimensional bias tensor of size C.
#### Outputs
output (differentiable) : T
The output tensor of the same shape as input.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LSTM-22** Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `o` - output gate * `f` - forget gate * `c` - cell gate * `t` - time step (t-1 means previous time step) * `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates * `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates * `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates * `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates * `P[iof]` - P peephole weight vector for input, output, and forget gates * `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates * `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates * `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates * `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates * `PB[iof]` - P peephole weight vector for backward input, output, and forget gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): * it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) * ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) * ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) * Ct = ft (.) Ct-1 + it (.) ct * ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) * Ht = ot (.) h(Ct) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
input_forget : int (default is 0)
Couple the input and forget gates if 1.
layout : int (default is 0)
The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 8)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional, non-differentiable) : T
Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional, differentiable) : T
The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
#### Outputs (0 - 3)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional, differentiable) : T
The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **LpNormalization-22** Given a matrix, apply Lp-normalization along the provided axis. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The axis on which to apply normalization, -1 mean last axis.
p : int (default is 2)
The order of the normalization, only 1 or 2 are supported.
#### Inputs
input (differentiable) : T
Input matrix
#### Outputs
output (differentiable) : T
Matrix after normalization
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **LpPool-22** LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxPool-22** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional, non-differentiable) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)
Constrain input and output types to float and 8 bit tensors.
I : tensor(int64)
Constrain index tensor to int64
### **MaxRoiPool-22** ROI max pool consumes an input tensor X and region of interests (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
pooled_shape : list of ints (required)
ROI pool output shape (height, width).
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois (non-differentiable) : T
RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].
#### Outputs
Y (differentiable) : T
RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxUnpool-22** MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corresponding pooling op that the unpooling op is trying to invert. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T1
Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
I (non-differentiable) : T2
Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).
output_shape (optional, non-differentiable) : T2
The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.
#### Outputs
output (differentiable) : T1
Output data tensor that contains the result of the unpooling.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int64)
Constrain index tensor to int64
### **Mish-22** Mish: A Self Regularized Non-Monotonic Neural Activation Function. Perform the linear unit element-wise on the input tensor X using formula: ``` mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x})) ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input X and output types to float tensors.
### **Multinomial-22** Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int (default is 6)
(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.
sample_size : int (default is 1)
Number of times to sample.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.
#### Outputs
output : T2
Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain output types to integral tensors.
### **NegativeLogLikelihoodLoss-22** A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. ``` When an optional "weight" is provided, the sample loss is calculated as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. ``` loss is zero for the case when target-value equals ignore_index. ``` loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index ``` If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: ``` mean(loss), if "weight" is not provided, ``` or if weight is provided, ``` sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. ``` If "reduction" attribute is set to "sum", the output is a scalar: `sum(loss)`. See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: ``` // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] ``` Example 2: ``` // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 ``` Example 3: ``` // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.
#### Inputs (2 - 3)
input (differentiable) : T
Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).
target (non-differentiable) : Tind
Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.
weight (optional, non-differentiable) : T
Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.
#### Outputs
loss (differentiable) : T
The negative log likelihood loss
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input, weight, and output types to floating-point tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
### **RNN-22** Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `t` - time step (t-1 means previous time step) * `Wi` - W parameter weight matrix for input gate * `Ri` - R recurrence weight matrix for input gate * `Wbi` - W parameter bias vector for input gate * `Rbi` - R parameter bias vector for input gate * `WBi` - W parameter weight matrix for backward input gate * `RBi` - R recurrence weight matrix for backward input gate * `WBbi` - WR bias vectors for backward input gate * `RBbi` - RR bias vectors for backward input gate * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): * Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh'])
One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
### **RandomNormal-22** Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. Default is TensorProto::FLOAT.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from normal distribution
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomNormalLike-22** Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by `mean` and `scale`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message, and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from normal distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniform-22** Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the `shape` argument and the range by `low` and `high`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniformLike-22** Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by `low` and `high`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RoiAlign-22** Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
coordinate_transformation_mode : string (default is half_pixel)
Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).
mode : string (default is avg)
The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.
output_height : int (default is 1)
default 1; Pooled output Y's height.
output_width : int (default is 1)
default 1; Pooled output Y's width.
sampling_ratio : int (default is 0)
Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f.
#### Inputs
X : T1
Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois : T1
RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
batch_indices : T2
1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
#### Outputs
Y : T1
RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain types to float tensors.
T2 : tensor(int64)
Constrain types to int tensors.
### **Round-22** Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halves, the rule is to round them to the nearest even integer. If input x is integral, +0, -0, NaN, or infinite, x itself is returned. The output tensor has the same shape and type as the input. Examples: ``` round([0.9]) = [1.0] round([2.5]) = [2.0] round([2.3]) = [2.0] round([1.5]) = [2.0] round([-4.5]) = [-4.0] ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Selu-22** Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.67326)
Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).
gamma : float (default is 1.0507)
Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sin-22** Calculates the sine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The sine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Sinh-22** Calculates the hyperbolic sine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic sine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Softplus-22** Softplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Softsign-22** Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The softsign (x/(1+|x|)) values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **Tan-22** Calculates the tangent of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The tangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ThresholdedRelu-22** ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Threshold value
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
## Version 23 of the default ONNX operator set ### **Attention-23** Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed. This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V. For self attention, `kv_sequence_length` equals to `q_sequence_length`. For cross attention, query and key might have different lengths. This operator also covers the 3 following variants based on the number of heads: 1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, `q_num_heads = kv_num_heads`. 2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, `q_num_heads > kv_num_heads`, `q_num_heads % kv_num_heads == 0`. 3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, `q_num_heads > kv_num_heads`, `kv_num_heads=1`. Attention bias to be added is calculated based on `attn_mask` input and `is_causal attribute`, only one of which can be provided. 1) If `is_causal` is set to `1`, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment. 2) `attn_mask`: A boolean mask where a value of `True` indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: ``` The following pattern is applied by this operator: Q K V | | | Q*sqrt(scale) K*sqrt(scale) | | | | | Transpose | | | | ---MatMul--- | | | at_mask---Add | | | softcap (if provided) | | | Softmax | | | -----MatMul------ | Y ``` #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
is_causal : int (default is 0)
If set to `1`, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment.
kv_num_heads : int
Number of heads of key and value. Must be used with 3D inputs of Q, K and V.
q_num_heads : int
Number of heads of query. Must be used with 3D inputs of Q, K and V.
qk_matmul_output_mode : int (default is 0)
If set to `0`, qk_matmul_output is the output of qk matmul. If set to `1`, qk_matmul_output includes the addition of the attention mask to the output of qk matmul. If set to `2`, qk_matmul_output is the output after the softcap operation. If set to `3`, qk_matmul_output is the output after the softmax operation. Default value is 0.
scale : float
Scaling factor applied to $Q*K^T$. Default value is `1/sqrt(head_size)`. To prevent [numerical overflow](https://tinyurl.com/sudb9s96), scale `Q`, `K` by `sqrt(scale)` before matmul.
softcap : float (default is 0.0)
Softcap value for attention weights. Default value is 0.
softmax_precision : int
The floating-point precision used in softmax computation. If softmax precision is not provided, the same precision as the input of softmax (Q and K) is used.
#### Inputs (3 - 6)
Q : T1
Query tensor. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, q_hidden_size)`. For cases with a 3D input tensor, `q_hidden_size = q_num_heads * head_size`
K : T1
Key tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, k_hidden_size)`. For cases with a 3D input tensor, `k_hidden_size = kv_num_heads * head_size`
V : T2
Value tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, v_hidden_size)`. For cases with a 3D input tensor, `v_hidden_size = kv_num_heads * v_head_size`
attn_mask (optional) : U
Attention mask. Shape must be broadcastable to 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length.` Two types of masks are supported. A boolean mask where a value of `True` indicates that the element should take part in attention. Also supports a float mask of the same type as query, key, value that is added to the attention score.
past_key (optional) : T1
past state cache for key with shape `(batch_size, kv_num_heads, past_sequence_length, head_size)`
past_value (optional) : T2
past state cache for value with shape `(batch_size, kv_num_heads, past_sequence_length, v_head_size)`
#### Outputs (1 - 4)
Y : T1
The output tensor . 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, hidden_size)`. For cases with a 3D input tensor, `hidden_size = q_num_heads * v_head_size`
present_key (optional) : T1
Updated key cache with shape `(batch_size, kv_num_heads, total_sequence_length, head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
present_value (optional) : T2
Updated value cache with shape `(batch_size, kv_num_heads, total_sequence_length, v_head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
qk_matmul_output (optional) : T1
The output of QK matmul. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain Q and K inputs types to float tensors.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain V input types to float tensors.
U : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output 'mask' types to boolean tensors and input types.
### **Cast-23** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | NaN | FLT_MAX | NaN | | -Inf | -FLT_MAX | NaN | -FLT_MAX | NaN | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain output types. Casting to complex is not supported.
### **CastLike-23** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain output types. Casting to complex is not supported.
### **Constant-23** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types.
### **ConstantOfShape-23** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint4), tensor(int4), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(float4e2m1)
Constrain output types to be numerics or boolean.
### **DequantizeLinear-23** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type is the same as `x_scale`. The output type also determines the precision of the multiplication operation. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `x_scale` data type (`T2`)
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
x_zero_point (optional) : T1
Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T3
N-D full precision output tensor. It has the same shape as input `x`. The data type is specified by the `output_dtype` attribute or, in its absence, the type of `x_scale`.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
The type of the inputs 'x_zero_point' and 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16)
The type of the input 'x_scale'.
T3 : tensor(float), tensor(float16), tensor(bfloat16)
The type of the output 'y'.
### **Flatten-23** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output to all tensor types up to IRv10.
### **Identity-23** Identity operator #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-23** If conditional #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv11.
B : tensor(bool)
Only bool
### **Loop-23** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv11.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **Pad-23** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types up to IRv11.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **QuantizeLinear-23** The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and `y_scale`, unless the `precision` attribute is specified. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T3`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T3`.
precision : int (default is 0)
(Optional) The precision of the division operation between `x` and `y_scale`. If not provided, it will be the same as the type of `y_scale`.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T2
Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.
y_zero_point (optional) : T3
Zero point for doing quantization to get `y`. Shape must match `y_scale`.Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T3
N-D quantized output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'y_scale'.
T3 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
The type of the input `y_zero_point` and the output `y`.
### **RMSNormalization-23** This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467. The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions, where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape), the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be described by the following equations. ``` XSquared = Mul(X, X) XSquaredMean = ReduceMean(XSquared) MeanSquareEpsilon = Add(XSquaredMean, epsilon) RMS = Sqrt(MeanSquareEpsilon) Normalized = Div(X, RMS) ``` where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square, Depending on `stash_type` attribute, the actual computation must happen in different floating-point precision. For example, if `stash_type` is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast `Normalized` back to the original type of `X`. The second stage then scales the outcome of the first stage using: ``` Y= Mul(Normalized, Scale) ``` Let `d[i]` indicate the i-th dimension of `X`. If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`, the shape of `RMS` is `[d[0], ..., d[axis-1], 1, ..., 1]`. `Y` and `X` have the same shape. This operator supports unidirectional broadcasting (`Scale` should be unidirectional broadcastable to tensor `X`); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
stash_type : int (default is 1)
The floating-point precision used in stage one of the computation.
#### Inputs
X : T
The input tensor to be normalized. In general, the shape is (D1, D2, ... , Dn) for n-dimensional data, where the root mean squared norm is taken over the last D dimensions, D is determined by the axis attribute.
scale : V
Scale tensor. Scale tensor shape should be broadcastable to the normalized shape.
#### Outputs
Y : V
Output data tensor. Same shape as X
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input X type to float tensors.
V : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output Y and scale type to float tensors.
### **Reshape-23** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types.
### **RotaryEmbedding-23** RotaryEmbedding is the implementation of rotary positional embeddings (RoPE) based on the paper https://arxiv.org/pdf/2104.09864. The key advantage of RoPE is that it allows the model to understand both the absolute position of a token and the relative distances between tokens. This is achieved through a rotational mechanism where the extent of rotation is computed based on the token's absolute position (position_ids). The rotational mechanism is defined by sine and cosine functions that are used to represent the rotation angles. For each token in the sequence, its positional embedding is computed by rotating its embedding vector. This is done by splitting the embedding vector either into two halves or interleaving every alternate token and applying the rotation matrix to each half of the embedding vector. The rotation matrix is parameterized by the token's position in the sequence. The rotated halves of the embedding vector are concatenated to form the final positional embedding for each token. The rotated positional embeddings are used in the self-attention mechanism. The rotation ensures that the model captures both absolute and relative positional information. Rotary embeddings are defined using the following algorithm: ```python def rotary_embedding( input: np.ndarray, cos_cache: np.ndarray, sin_cache: np.ndarray, position_ids: np.ndarray | None = None, interleaved=None, rotary_embedding_dim=None, num_heads=None, ) -> np.ndarray: original_input_shape = input.shape # First ensure input to be processed has shape [batch_size, seq_len, num_heads, head_size] if len(input.shape) == 4: input = np.transpose(input, (0, 2, 1, 3)) batch_size = input.shape[0] sequence_length = input.shape[1] if len(input.shape) == 3: hidden_size = input.shape[2] assert num_heads != 0 head_size = int(hidden_size / num_heads) new_shape = [batch_size, sequence_length, num_heads, head_size] input = np.reshape(input, new_shape) assert len(input.shape) == 4 head_size = input.shape[3] # Fully or partially perform rotation on input based on rotary_embedding_dim attribute if rotary_embedding_dim is None or rotary_embedding_dim == 0: # If rotary_embedding_dim not provided, perform full rotation by using head_size rotary_embedding_dim = head_size x_rotate = input[:, :, :, :rotary_embedding_dim] x_not_rotate = input[:, :, :, rotary_embedding_dim:] rotary_embedding_dim_half = int(rotary_embedding_dim / 2) # Retrieve sin and cos caches using position ids if position_ids is not None: cos_cache = cos_cache[ position_ids ] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] sin_cache = sin_cache[ position_ids ] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] if cos_cache.shape[-1] != rotary_embedding_dim_half: raise ValueError( f"Last dimension of cos cache ({cos_cache.shape[-1]}) does not match rotary_embedding_dim/2 ({rotary_embedding_dim_half})." ) if sin_cache.shape[-1] != rotary_embedding_dim_half: raise ValueError( f"Last dimension of sin cache ({sin_cache.shape[-1]}) does not match rotary_embedding_dim/2 ({rotary_embedding_dim_half})." ) cos_cache = np.expand_dims( cos_cache, axis=2 ) # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2] sin_cache = np.expand_dims( sin_cache, axis=2 ) # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2] # Either divide the input in halves or interleave (based on interleaved attribute) if interleaved: x1 = x_rotate[:, :, :, 0::2] x2 = x_rotate[:, :, :, 1::2] else: x1, x2 = np.split(x_rotate, 2, axis=-1) # Calculate real and imaginary values real = (cos_cache * x1) - (sin_cache * x2) imag = (sin_cache * x1) + (cos_cache * x2) # Inserted rotated embeddings back to the original input if interleaved: # x_rotate[:, :, :, 0::2] = real # x_rotate[:, :, :, 1::2] = imag real = np.expand_dims(real, axis=-1) imag = np.expand_dims(imag, axis=-1) x_rotate_concat = np.concatenate((real, imag), axis=-1) x_rotate = np.reshape(x_rotate_concat, x_rotate.shape) else: x_rotate = np.concatenate((real, imag), axis=-1) output = np.concatenate((x_rotate, x_not_rotate), axis=-1) if len(original_input_shape) == 3: output = np.reshape(output, original_input_shape) else: output = np.transpose(output, (0, 2, 1, 3)) return output ``` #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
interleaved : int (default is 0)
Rotate using interleaved pattern. Default value is 0 (False).
num_heads : int
Number of attention heads. Must be provided when input is a 3D tensor.
rotary_embedding_dim : int (default is 0)
Rotary embedding dimension used to apply partial rotary embeddings.
#### Inputs (3 - 4)
X : T
The input tensor representing the token embeddings. 4D tensor with shape `(batch_size, num_heads, sequence_length, head_size)` or 3D tensor with shape `(batch_size, sequence_length, hidden_size)`. For cases with a 4D input tensor, `head_size` has to be even. For cases with a 3D input tensor, `num_heads` attribute must be provided and `hidden_size` must be an even multiple of `num_heads` where `hidden_size = num_heads * head_size`
cos_cache : T
The cosine values for the rotation. 2D tensor with shape `(max_position_id_plus_1, head_size / 2)` for full rotation or `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are provided. 3D tensor with shape `(batch_size, sequence_length, head_size / 2)` for full rotation or `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are not provided. `max_position_id_plus_1` is a parameter to the model.
sin_cache : T
The sine values for the rotation. 2D tensor with shape `(max_position_id_plus_1, head_size / 2)` for full rotation or `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are provided. 3D tensor with shape `(batch_size, sequence_length, head_size / 2)` for full rotation or `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are not provided. `max_position_id_plus_1` is a parameter to the model.
position_ids (optional) : M
The position indices for the tokens. 2D tensor with shape `(batch_size, sequence_length)`
#### Outputs
Y : T
Tensor with same shape as input.
#### Type Constraints
T : tensor(float), tensor(float16), tensor(bfloat16)
Constrain input and output types to float tensors.
M : tensor(int64)
Constrain input and output types to integer tensors.
### **Scan-23** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
All Tensor types up to IRv11.
### **Shape-23** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Size-23** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **Squeeze-23** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types up to IRv11.
### **Transpose-23** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types.
### **Unsqueeze-23** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
Constrain input and output types to all tensor types up to IRv11.
## Version 24 of the default ONNX operator set ### **Attention-24** Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed. This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V. For self attention, `kv_sequence_length` equals to `q_sequence_length`. For cross attention, query and key might have different lengths. This operator also covers the 3 following variants based on the number of heads: 1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, `q_num_heads = kv_num_heads`. 2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, `q_num_heads > kv_num_heads`, `q_num_heads % kv_num_heads == 0`. 3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, `q_num_heads > kv_num_heads`, `kv_num_heads=1`. Attention bias to be added is calculated based on `attn_mask` input and `is_causal` attribute: 1) `attn_mask`: A boolean mask where a value of `True` indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. 2) If `is_causal` is set to `1`, attention scores above the diagonal are masked out, regardless of the `attn_mask` input. With respect to KV cache update, this operator allows the following two use cases: 1) Cache update happens inside the Attention operator. In this case, the `K` and `V` inputs contain only the incoming tokens for the current autoregressive step, and the four optional inputs/outputs past and present key and value are all needed. The Attention op performs a Concat operation on the past and incoming key and value to form the present key and value, respectively. Note that this only works correctly for the special case where the past key and value do not contain padded tokens. 2) Cache update happens outside the Attention operator (for example, through the `TensorScatter` operator). In this case, the `K` and `V` inputs correspond to the entire cache tensor, so the four optional inputs/outputs past and present key and value should not be used. An additional input `nonpad_kv_seqlen` of shape (batch_size,) may be provided to indicate the number of non-padding tokens in each sample of the batch to save unnecessary computation. Here, the kv_sequence dimension of `attn_mask` can be shorter than `K` and `V`, but still needs to be at least as long as the maximum value of `nonpad_kv_seqlen`. Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: ``` The following pattern is applied by this operator: Q K V | | | Q*sqrt(scale) K*sqrt(scale) | | | | | Transpose | | | | ---MatMul--- | | | at_mask---Add | | | softcap (if provided) | | | Softmax | | | -----MatMul------ | Y ``` #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
is_causal : int (default is 0)
If set to `1`, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment.
kv_num_heads : int
Number of heads of key and value. Must be used with 3D inputs of Q, K and V.
q_num_heads : int
Number of heads of query. Must be used with 3D inputs of Q, K and V.
qk_matmul_output_mode : int (default is 0)
If set to `0`, qk_matmul_output is the output of qk matmul. If set to `1`, qk_matmul_output includes the addition of the attention mask to the output of qk matmul. If set to `2`, qk_matmul_output is the output after the softcap operation. If set to `3`, qk_matmul_output is the output after the softmax operation. Default value is 0.
scale : float
Scaling factor applied to $Q*K^T$. Default value is `1/sqrt(head_size)`. To prevent [numerical overflow](https://tinyurl.com/sudb9s96), scale `Q`, `K` by `sqrt(scale)` before matmul.
softcap : float (default is 0.0)
Softcap value for attention weights. Default value is 0.
softmax_precision : int
The floating-point precision used in softmax computation. If softmax precision is not provided, the same precision as the input of softmax (Q and K) is used.
#### Inputs (3 - 7)
Q : T1
Query tensor. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, q_hidden_size)`. For cases with a 3D input tensor, `q_hidden_size = q_num_heads * head_size`
K : T1
Key tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, k_hidden_size)`. For cases with a 3D input tensor, `k_hidden_size = kv_num_heads * head_size`
V : T2
Value tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, v_hidden_size)`. For cases with a 3D input tensor, `v_hidden_size = kv_num_heads * v_head_size`
attn_mask (optional) : U
Attention mask. Shape must be broadcastable to `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length.` The last dimension can also be shorter than `total_sequence_length` and will be padded to `total_sequence_length` with negative infinity. Two types of masks are supported: a boolean mask where a value of `True` indicates that the element should take part in attention, or a float mask of the same type as query, key, value that is added to the attention score.
past_key (optional) : T1
past state cache for key with shape `(batch_size, kv_num_heads, past_sequence_length, head_size)`
past_value (optional) : T2
past state cache for value with shape `(batch_size, kv_num_heads, past_sequence_length, v_head_size)`
nonpad_kv_seqlen (optional) : tensor(int64)
A vector of integers of shape `(batch_size,)` that indicates the number of valid (ie, non-padding) tokens in each sample. A padding mask can be derived from this. This should not be used together with `past_key` and `past_value` inputs or `present_key` and `present_value` outputs (See the KV cache use cases in the operator description).
#### Outputs (1 - 4)
Y : T1
The output tensor . 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, hidden_size)`. For cases with a 3D input tensor, `hidden_size = q_num_heads * v_head_size`
present_key (optional) : T1
Updated key cache with shape `(batch_size, kv_num_heads, total_sequence_length, head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
present_value (optional) : T2
Updated value cache with shape `(batch_size, kv_num_heads, total_sequence_length, v_head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
qk_matmul_output (optional) : T1
The output of QK matmul. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain Q and K inputs types to float tensors.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain V input types to float tensors.
U : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output 'mask' types to boolean tensors and input types.
### **Cast-24** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 types (E4M3FN, E4M3FNUZ, E5M2, E5M2FNUZ) were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | -Inf | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | FLOAT8E8M0 type was introduced to enable [Microscaling (MX) formats](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf). When casting to FLOAT8E8M0, the rounding behavior can be specified using the `round_mode` and `saturate` attributes. The current CUDA behavior is to round up and saturate. Casting negative values to FLOAT8E8M0 gives undefined behavior. The following table describes the casting behavior of special values to FLOAT8E8M0 in the two most common cases. | x | saturate + up | non-saturate + nearest | | ----------------- | ------------- | --------------------- | | 0 | 0 | NaN | | -0 | Unspecified | Unspecified | | NaN | NaN | NaN | | Inf | E8M0_MAX | NaN | | x > E8M0_MAX | E8M0_MAX | NaN | | x \< E8M0_MIN | E8M0_MIN | NaN | | x \< 0 | Unspecified | Unspecified | #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. All cases are fully described in the tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain output types. Casting to complex is not supported.
### **CastLike-24** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up. Please refer to operator Cast description for further details.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain output types. Casting to complex is not supported.
### **Constant-24** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types.
### **ConstantOfShape-24** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint4), tensor(int4), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(float4e2m1), tensor(float8e8m0)
Constrain output types to be numerics or boolean.
### **DequantizeLinear-24** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type is the same as `x_scale`. The output type also determines the precision of the multiplication operation. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `x_scale` data type (`T2`)
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
x_zero_point (optional) : T1
Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T3
N-D full precision output tensor. It has the same shape as input `x`. The data type is specified by the `output_dtype` attribute or, in its absence, the type of `x_scale`.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
The type of the inputs 'x_zero_point' and 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(float8e8m0)
The type of the input 'x_scale'.
T3 : tensor(float), tensor(float16), tensor(bfloat16)
The type of the output 'y'.
### **Flatten-24** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output to all tensor types up to IRv12.
### **Identity-24** Identity operator #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-24** If conditional #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv11.
B : tensor(bool)
Only bool
### **Loop-24** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv11.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **Pad-24** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types up to IRv12.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **QuantizeLinear-24** The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and `y_scale`, unless the `precision` attribute is specified. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T3`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T3`.
precision : int (default is 0)
(Optional) The precision of the division operation between `x` and `y_scale`. If not provided, it will be the same as the type of `y_scale`.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T2
Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.
y_zero_point (optional) : T3
Zero point for doing quantization to get `y`. Shape must match `y_scale`. Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T3
N-D quantized output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32), tensor(float8e8m0)
The type of the input 'y_scale'.
T3 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1)
The type of the input `y_zero_point` and the output `y`.
### **Reshape-24** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types.
### **Scan-24** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
All Tensor types up to IRv12.
### **Shape-24** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Size-24** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **SplitToSequence-24** Split a tensor into a sequence of tensors, along the specified 'axis'. Lengths of the parts can be specified using the optional argument 'split'. If the argument `split' is not specified, a default scalar value of 1 is used as the value of `split'. 'split' must contain only positive numbers. 'split' is either a scalar (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input' will be split into chunks all of size 'split' if possible. The last chunk alone may be smaller than 'split' if the 'input' size along the given axis 'axis' is not divisible by 'split'. If 'split' is a 1-dimensional tensor, the input tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis' specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the dimension size of input tensor on 'axis'. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].
keepdims : int (default is 1)
Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.
#### Inputs (1 - 2)
input : T
The tensor to split
split (optional) : I
Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0.
#### Outputs
output_sequence : S
One or more outputs forming a sequence of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input types to all tensor types.
I : tensor(int32), tensor(int64)
Constrain split size to integral tensor.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to all tensor types.
### **Squeeze-24** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types up to IRv12.
### **Swish-24** Swish function takes one input data (Tensor) and produces one output data (Tensor) of the same shape, where $Swish(x) = x * sigmoid(alpha * x)$. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Coefficient to multiply with input before sigmoid.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(bfloat16), tensor(double)
Constrain input and output types to float tensors.
### **TensorScatter-24** TensorScatter is a generic tensor update operation, motivated by the requirements for KV cache updates for Attention ops commonly found in LLMs. It is a functional operation that models an in-place update to a KV cache buffer. The past and present cache tensors have the same shape (batch_size, D1, D2, ..., max_sequence_length, ..., Dn), with the sequence dimension (indicated by the `axis` attribute) being max_sequence_length, so the sizes of these tensors do not need to grow between iterations. The `update` tensor's shape only differs from the cache tensors in the sequence dimension: (batch_size, D1, D2, ..., sequence_length, ..., Dn), where sequence_length <= max_sequence_length. The optional `write_indices` input indicates the write index for each sample in the batch, assumed to be zero if not provided. When the `mode` attribute is set to "circular", the write index is modulo max_sequence_length. The operation can be described using the following pseudocode: ``` for prefix_idx in np.ndindex(past_cache.shape[:axis]): batch_idx = prefix_idx[0] for sequence_idx in range(sequence_length): cache_idx = (*prefix_idx, write_indices[batch_idx] + sequence_idx) if mode == "circular": cache_idx = tuple(np.mod(np.asarray(cache_idx), max_sequence_length)) update_idx = (*prefix_idx, sequence_idx) present_cache[cache_idx] = update[update_idx] ``` During the prefill phase of attention, only the first two inputs are needed. During the decode phase, `write_indices` is also needed so that the incoming key or value update can be appended after the last valid token for each sample in the batch. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is -2)
Sequence dimension of the `past_cache` and `update` tensors. It cannot be 0 (the batch dimension). Default is -2.
mode : string (default is linear)
Write mode of cache update. Supported modes include `linear` and `circular`. `linear` mode requires write_indices+sequence_length<=max_sequence_length. For `circular` mode, the updates happen in wrap-around fashion, ie, the update index is modulo `max_sequence_length`
#### Inputs (2 - 3)
past_cache (differentiable) : T
Past state cache for key or value with shape `(batch_size, D1, D2, ..., max_sequence_length, ..., Dn)`.
update (differentiable) : T
New update tensor with shape `(batch_size, D1, D2, ..., sequence_length, ..., Dn)`.
write_indices (optional, non-differentiable) : tensor(int64)
Write indices for the incoming update tensor in the cache. Shape is `(batch_size,)`. Assumed to be all zeros if not provided.
#### Outputs
present_cache (differentiable) : T
Updated cache. Same shape as `past_cache`.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to any tensor type.
### **TopK-24** Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: * Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis * Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). * If "largest" is 1 (the default value) then the k largest elements are returned. * If "sorted" is 1 (the default value) then the resulting k elements will be sorted. * If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined. Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
largest : int (default is 1)
Whether to return the top-K largest or smallest elements.
sorted : int (default is 1)
Whether to return the elements in sorted order.
#### Inputs
X (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{n-1}]
K (non-differentiable) : tensor(int64)
A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
#### Outputs
Values (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
Indices (non-differentiable) : I
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
I : tensor(int64)
Constrain index tensor to int64
### **Transpose-24** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types.
### **Unsqueeze-24** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to all tensor types up to IRv12.
## Version 25 of the default ONNX operator set ### **Cast-25** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 types (E4M3FN, E4M3FNUZ, E5M2, E5M2FNUZ) were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | -Inf | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | FLOAT8E8M0 type was introduced to enable [Microscaling (MX) formats](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf). When casting to FLOAT8E8M0, the rounding behavior can be specified using the `round_mode` and `saturate` attributes. The current CUDA behavior is to round up and saturate. Casting negative values to FLOAT8E8M0 gives undefined behavior. The following table describes the casting behavior of special values to FLOAT8E8M0 in the two most common cases. | x | saturate + up | non-saturate + nearest | | ----------------- | ------------- | --------------------- | | 0 | 0 | NaN | | -0 | Unspecified | Unspecified | | NaN | NaN | NaN | | Inf | E8M0_MAX | NaN | | x > E8M0_MAX | E8M0_MAX | NaN | | x \< E8M0_MIN | E8M0_MIN | NaN | | x \< 0 | Unspecified | Unspecified | #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. All cases are fully described in the tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types. Casting to complex is not supported.
### **CastLike-25** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up. Please refer to operator Cast description for further details.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types. Casting to complex is not supported.
### **Constant-25** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
### **ConstantOfShape-25** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint4), tensor(int4), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types to be numerics or boolean.
### **DequantizeLinear-25** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type is the same as `x_scale`. The output type also determines the precision of the multiplication operation. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `x_scale` data type (`T2`)
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
x_zero_point (optional) : T1
Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T3
N-D full precision output tensor. It has the same shape as input `x`. The data type is specified by the `output_dtype` attribute or, in its absence, the type of `x_scale`.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(uint2), tensor(int2)
The type of the inputs 'x_zero_point' and 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(float8e8m0)
The type of the input 'x_scale'.
T3 : tensor(float), tensor(float16), tensor(bfloat16)
The type of the output 'y'.
### **Flatten-25** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output to all tensor types up to IRv13.
### **Identity-25** Identity operator #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
### **If-25** If conditional #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), seq(tensor(uint2)), seq(tensor(int2)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0)), optional(tensor(uint2)), optional(tensor(int2))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv13.
B : tensor(bool)
Only bool
### **Loop-25** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), seq(tensor(uint2)), seq(tensor(int2)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0)), optional(tensor(uint2)), optional(tensor(int2))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv13.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
### **Pad-25** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
### **QuantizeLinear-25** The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] - uint2: [0, 3] - int2: [-2, 1] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and `y_scale`, unless the `precision` attribute is specified. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T3`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T3`.
precision : int (default is 0)
(Optional) The precision of the division operation between `x` and `y_scale`. If not provided, it will be the same as the type of `y_scale`.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T2
Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.
y_zero_point (optional) : T3
Zero point for doing quantization to get `y`. Shape must match `y_scale`. Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T3
N-D quantized output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32), tensor(float8e8m0)
The type of the input 'y_scale'.
T3 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(uint2), tensor(int2)
The type of the input `y_zero_point` and the output `y`.
### **Reshape-25** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
### **Scan-25** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
All Tensor types up to IRv13.
### **Shape-25** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
### **Size-25** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
### **Squeeze-25** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
### **Transpose-25** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
### **Unsqueeze-25** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
# ai.onnx.preview.training ## Version 1 of the 'ai.onnx.preview.training' operator set ### **ai.onnx.preview.training.Adagrad-1** Compute one iteration of ADAGRAD, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, ADAGRAD requires some parameters: - The initial learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A learning-rate decay factor "decay_factor". - A small constant "epsilon" to avoid dividing-by-zero. At each ADAGRAD iteration, the optimized tensors are moved along a direction computed based on their estimated gradient and accumulated squared gradient. Assume that only a single tensor "X" is updated by this operator. We need the value of "X", its gradient "G", and its accumulated squared gradient "H". Therefore, variables in this operator's input list are sequentially "R", "T", "X", "G", and "H". Other parameters are given as attributes because they are usually constants. Also, the corresponding output tensors are the new value of "X" (called "X_new"), and then the new accumulated squared gradient (called "H_new"). Those outputs are computed from the given inputs following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Compute a scalar learning-rate factor. At the first update of X, T is generally // 0 (0-based update index) or 1 (1-based update index). r = R / (1 + T * decay_factor); // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G; // Compute new accumulated squared gradient. H_new = H + G_regularized * G_regularized; // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...) // computes element-wise square-root. H_adaptive = Sqrt(H_new) + epsilon // Compute the new value of "X". X_new = X - r * G_regularized / H_adaptive; If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then just reuse the entire pseudo code. Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf. In that reference paper, this operator is a special case of the Figure 1's composite mirror descent update. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
decay_factor : float (default is 0.0)
The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.
epsilon : float (default is 0.0)
Small scalar to avoid dividing by zero.
norm_coefficient : float (default is 0.0)
Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
#### Inputs (3 - ∞)
R : T1
The initial learning rate.
T : T2
The update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ai.onnx.preview.training.Adam-1** Compute one iteration of Adam, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. First of all, Adam requires some parameters: - The learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A small constant "epsilon" to avoid dividing-by-zero. - Two coefficients, "alpha" and "beta". At each Adam iteration, the optimized tensors are moved along a direction computed based on their exponentially-averaged historical gradient and exponentially-averaged historical squared gradient. Assume that only a tensor "X" is being optimized. The rest of required information is - the value of "X", - "X"'s gradient (denoted by "G"), - "X"'s exponentially-averaged historical gradient (denoted by "V"), and - "X"'s exponentially-averaged historical squared gradient (denoted by "H"). Some of those parameters are passed into this operator as input tensors and others are stored as this operator's attributes. Specifically, this operator's input tensor list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is the second input, and so on. Other parameters are given as attributes because they are constants. Moreover, the corresponding output tensors are - the new value of "X" (called "X_new"), - the new exponentially-averaged historical gradient (denoted by "V_new"), and - the new exponentially-averaged historical squared gradient (denoted by "H_new"). Those outputs are computed following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G // Update exponentially-averaged historical gradient. V_new = alpha * V + (1 - alpha) * G_regularized // Update exponentially-averaged historical squared gradient. H_new = beta * H + (1 - beta) * G_regularized * G_regularized // Compute the element-wise square-root of H_new. V_new will be element-wisely // divided by H_sqrt for a better update direction. H_sqrt = Sqrt(H_new) + epsilon // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power. R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R // Compute new value of "X". X_new = X - R_adjusted * V_new / H_sqrt // Post-update regularization. X_final = (1 - norm_coefficient_post) * X_new If there are multiple inputs to be optimized, the pseudo code will be applied independently to each of them. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
alpha : float (default is 0.9)
Coefficient of previously accumulated gradient in running average. Default to 0.9.
beta : float (default is 0.999)
Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.
epsilon : float (default is 0.0)
Small scalar to avoid dividing by zero.
norm_coefficient : float (default is 0.0)
Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
norm_coefficient_post : float (default is 0.0)
Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
#### Inputs (3 - ∞)
R : T1
The initial learning rate.
T : T2
The update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **ai.onnx.preview.training.Gradient-1** Gradient operator computes the partial derivatives of a specific tensor w.r.t. some other tensors. This operator is widely used in gradient-based training algorithms. To illustrate its use, let's consider a computation graph, ``` X -----. | v W --> Conv --> H --> Gemm --> Y ^ | Z ``` , where W and Z are trainable tensors. Note that operators' attributes are omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of Y with respect to W (Z). The user can compute gradient by inserting Gradient operator to form another graph shown below. ``` W --> Conv --> H --> Gemm --> Y | ^ ^ | | | | X Z | | | | | .----------' | | | (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in | | | "xs" followed by "zs") | v v '---> Gradient(xs=["W", "Z"], zs=["X"], y="Y") | | | '-----------------------------------> dY/dW (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` By definition, the tensor "y" is a function of independent variables in "xs" and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H" cannot appear in "xs" and "zs". The reason is that "H" can be determined by tensors "W" and "X" and therefore "H" is not an independent variable. All outputs are optional. If needed, for example, user can assign an empty string to the 1st output name of that Gradient to skip the generation of dY/dW. Note that the concept of optional outputs can also be found in ONNX's RNN, GRU, and LSTM. Gradient operator can compute derivative against intermediate tensors. For example, the gradient of Y with respect to H can be done via ``` W --> Conv --> H --> Gemm --> Y ^ | ^ | | | X | Z .-------' | | .----------' | | (H/Z is the 1st/2nd input of Gradient as shown in "xs") v v Gradient(xs=["H", "Z"], y="Y") | | | '-----------------------------------> dY/dH (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` It is possible to represent high-order differentiation using Gradient operators. For example, given the following linear model: ``` W --> Gemm --> Y --> Loss --> O ^ ^ | | X L ``` To compute the 2nd order derivative of O with respect to W (denoted by d^2O/dW^2), one can do ``` W --> Gemm --> Y --> Loss --> O | ^ ^ | | | | X .------------L | | | | | | | v +------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient) | | | | | | | '---> dO/dW (2nd output of Gradient) | v v '---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of | Gradient) | | '---> d^2O/dW^2 (2nd output of Gradient) ``` The tensors named in attributes "xs", "zs", and "y" define the differentiated computation graph, and the inputs to Gradient node define the values at which the gradient is computed. We can feed different tensors to the identified graph. For example, one can compute the gradient of Y with respect to H at a specific value of H, H_1, by providing that value as an input to the Gradient node. ``` W --> Conv --> H --> Gemm --> Y ^ ^ | | X Z Z_1 (2nd input of Gradient) | v H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1. | '------------------------------> dY/dZ (2nd output of Gradient) ``` When the inputs of Gradient are the tensors named in "xs" and "zs", the computation can be optimized. More specifically, intermediate variables in forward pass can be reused if the gradient is computed via reverse-mode auto-differentiation. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
xs : list of strings (required)
Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
y : string (required)
The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".
zs : list of strings
Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
#### Inputs (1 - ∞)
Inputs (variadic, heterogeneous) : T1
The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".
#### Outputs (1 - ∞)
Outputs (variadic, heterogeneous) : T2
The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Allow outputs to be any kind of tensor.
T2 : tensor(float16), tensor(float), tensor(double)
Allow inputs to be any kind of floating-point tensor.
### **ai.onnx.preview.training.Momentum-1** Compute one iteration of stochastic gradient update with momentum. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, SG with momentum requires several parameters: - The learning-rate "R". - The update count "T". That is, the number of conducted training iterations. It should be zero in the first training iteration. - A L2-norm regularization coefficient "norm_coefficient". - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha". - The scaling coefficient of current gradient "beta". - An attribute to choose either standard momentum or Nesterov's momentum "mode" should be used. For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized. Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new momentum (called "V_new"). This operator supports two different momentum algorithms. Set the attribute "mode" to "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to "standard" to use standard momentum. Computation details are described subsequently. Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting. Pseudo code for SG with standard momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized // Update X. X_new = X - R * V_new Pseudo code for SG with Nesterov's momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G; // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized; // Compute final update direction and then update X. X_new = X - R * (G_regularized + alpha * V_new) If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then our pseudo code becomes applicable. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
alpha : float (required)
The decay factor of momentum. It should be a scalar.
beta : float (required)
The coefficient of gradient in computing new momentum. It should be a scalar.
mode : string (required)
Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum
norm_coefficient : float (required)
Coefficient of 0.5 * norm_coefficient * ||X||^2.
#### Inputs (3 - ∞)
R : T1
The learning rate.
T : T2
Update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input types to float tensors.
onnx-onnx-bca0315/docs/DefineDifferentiability.md000066400000000000000000000122761511334557700221210ustar00rootroot00000000000000 # A Short Guide on the Differentiability Tag for ONNX Operators ## Differentiability Tag The ONNX operator schema for each operator includes a differentiability tag for each input and output. In this document, we explain the meaning of this tag and how to ensure the correctness of the tags. Briefly, the tag identifies the set of differentiable inputs and differentiable outputs of an operator. The meaning of the tag is that the partial derivative of each differentiable output is defined with respect to each differentiable output. ## Ways to Define Differentiability Tag The differentiability definition of an operator consists of several aspects. - Differentiable inputs, which can be referenced in Gradient's `xs` attribute. - Differentiable outputs, which can be referenced in Gradient's `y` attribute. - The math equation to compute the Jacobian matrix (or tensor). If a variable (input or output) is differentiable or not is judged by math. If the Jacobian matrix (or tensor) exists, then the considered operator has some differentiable inputs and outputs. There are several strategies to implement auto-differentiation such as forward accumulation, backward accumulation, and dual variable. Because most deep learning frameworks are backward-based, the reviewers should ensure the PR authors of tags provide enough details on that. We present a couple of methods below to verify the differentiability for ONNX operator. ### Method 1: Reuse Existing Deep Learning Frameworks The first way is to show that the considered operator's backward operation exists in an existing framework such as Pytorch or Tensorflow. In this case, the author should provide a runnable python script which computes the backward pass of the considered operator. The author should also point out how to map the Pytorch or Tensor code to ONNX format (for example, the author can call `torch.onnx.export` to save an ONNX model). The following script shows the differentiability of ONNX Reshape using Pytorch. ```python import torch import torch.nn as nn # A single-operator model. It's literally a Pytorch Reshape. # Note that Pytorch Reshape can be directly mapped to ONNX Reshape. class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() def forward(self, x): y = torch.reshape(x, (x.numel(),)) y.retain_grad() return y model = MyModel() x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True) y = model(x) dy = torch.tensor([1., 2., 3., 4.]) torch.autograd.backward([y], grad_tensors=[dy], retain_graph=True, create_graph=True, grad_variables=None) # This example shows the input and the output in Pytorch are differentiable. # From the exported ONNX model below, we also see that "x" is the first input # of ONNX Reshape and "y" the output of ONNX Reshape. Therefore, we can say # the first input and the output of ONNX Reshape are differentiable. print(x.grad) print(y.grad) with open('model.onnx', 'wb') as f: torch.onnx.export(model, x, f) ``` ### Method 2: Manually Do the Math The second way is formally proving the existence of the Jacobian matrix (or tensor) from outputs to inputs with at least two numerical examples. In this case, the reviewer should go through the math and confirm if the numerical result is correct. The author should add enough details so that any STEM graduated student can easily review it. For example, to show the differentiability of Add, the author may first write down its equation: ``` C = A + B ``` For the sake of simplicity, assume `A` and `B` are same-shape vector. ``` A = [a1, a2]^T B = [b1, b2]^T C = [c1, c2]^T ``` Here we use the symbol `^T` to denote transpose of the attached matrix or vector. Let `X = [a1, a2, b1, b2]^T` and `Y = [c1, c2]^T` and consider Add as a function which maps `X` to `Y`. Then, this function's Jacobian matrix is a 4-by-2 matrix, ``` J = [[dc1/da1, dc2/da1], [dc1/da2, dc2/da2], [dc1/db1, dc2/db1], [dc1/db2, dc2/db2]] = [[1, 0], [0, 1], [1, 0], [0, 1]] ``` If ``` dL/dC = [dL/dc1, dL/dc2]^T, ``` then `dL/dA = [dL/da1, dL/da2]^T` and `dL/dB = [dL/db1, dL/db2]^T` can be computed from elements in ``` [[dL/da1], [dL/da2], [dL/db1], [dL/db2]] = J * dL/dC = [[dL/dc1], [dL/dc2], [dL/dc1], [dL/dc2]] ``` where `*` is standard matrix multiplication. If `dL/dC = [0.2, 0.8]^T`, then `dL/dA = [0.2, 0.8]^T` and `dL/dB = [0.2, 0.8]^T`. Notice that the procedure to compute `dL/dA` and `dL/dB` from `dL/dC` is usually called backward of an operator. We can see backward operator of Add takes `dL/dC` as an input and produces two outputs `dL/dA` and `dL/dB`. Consequently, all of `A`, `B`, and `C` are differentiable. By flattening tensor into 1-D vector, this example can be extended to cover all tensors when shape broadcasting is not needed. If broadcasting happens, the broadcasted element's gradient is the sum of all associated elements' gradient in its **non-broadcasting** case. Let's consider the above example again. If `B = [b]^T` becomes an 1-element vector, `B` may be broadcasted to `[b1, b2]^T` and `dL/dB = [dL/ db]^T = [dL/db1 + dL/db2]^T`. For high-dimensional tensors, this is in fact a ReduceSum operation along all expanded axes. onnx-onnx-bca0315/docs/DimensionDenotation.md000066400000000000000000000107761511334557700213260ustar00rootroot00000000000000 # Dimension Denotation Dimension Denotation is an experimental attempt to give tensor axis semantic descriptions and thus types and perform verification steps based on them subsequently. ## Motivation The motivation of such a mechanism can be illustrated via a simple example. In the linear neural network specification below, we assume a NCHW model input: ``` input_in_NCHW -> Transpose(input, perm=[0, 2, 1, 3]) -> AveragePool(input, ...) ``` In this neural network, a user mistakenly constructed a neural network that transposes an NCHW input to a weird NHCW format and pass through spatial pooling that assumes a NCHW input format. As clearly a mistake as it is, no existing infrastructure will report an error to the user. This is should be deeply unnerving to programmers who rely heavily on type checking as an integral part of program correctness guarantee. This proposal seeks to resolve this vacuum of proper type-checking inherent in the current paradigm of neural network specification. This proposal consists of three key components: Denotation Definition, Denotation Propagation and Denotation Verification, each of which will be discussed in detail. ## Denotation Definition To begin with, we define a set of types for tensor types. Such types are defined based on the following principles: 1. Be fine grain enough to eliminate potential pitfalls. For instance, the above example illustrated in the motivation section mandates that we distinguish between a channel dimension and a spatial feature dimension to ensure the correctness of execution of the AveragePool op. 2. Be coarse grain enough to alleviate the mental burden of users. For instance, in the above example, there is significantly less need to distinguish between a width dimension and a height dimension because operations like pooling and convolution often do not draw a distinction between various spatial dimensions. Thus, we summarize all the spatial dimensions as feature dimensions. 3. As an important corollary of 2, be model agnostic. For instance, the semantics of feature dimensions in recurrent neural networks (RNN) and the semantics of spatial dimensions in convolutional neural network (CNN) are almost indistinguishable and therefore we permit users and developers to describe either as a feature dimension. Specifically, in our first proposal, we define the following set of standard denotations: 1. `DATA_BATCH` describes a batch dimension of the training data. This corresponds to the `N` dimension in the more commonly used tensor format notation `NCHW`. 2. `DATA_CHANNEL` describes a channel dimension of the training data. This corresponds to the `C` dimension. 3. `DATA_TIME` describes a time dimension. 4. `DATA_FEATURE` describes a feature dimension. This corresponds to the `H`, `W` dimension or the feature dimension in RNN. 5. `FILTER_IN_CHANNEL` describes a filter in-channel dimension. This is the dimension that is identical (in size) to the channel dimension of the input image feature maps. 6. `FILTER_OUT_CHANNEL` describes a filter out-channel dimension. This is the dimension that is identical (in size) to the channel dimension of the output image feature maps. 7. `FILTER_SPATIAL` describes a filter spatial dimension. ## Denotation Propagation Denotation Propagation happens when an operation permutes, destroys or creates dimensions with respect to its input tensor. In such scenarios, we will implement customized, operation-specific functions to infer the output tensor dimension denotation based on the input tensor dimension denotation. An example operation where denotation propagation happens is Transpose operation where the pseudocode for output dimension denotation inference can be formulated as a function of the input dimension denotation: ``` for i, j in enumerate(perm): out_dim_denotaion[i] = in_dim_denotation[j] ``` ## Denotation Verification Denotation Verification happens when an operation expects its input to arrive in a particular format. An example operation where denotation verification happens is AveragePool operation where the input, if annotated with dimension denotation, in the 2D case should have the denotation [`DATA_BATCH`, `DATA_CHANNEL`, `DATA_FEATURE`, `DATA_FEATURE`]. If there is a mismatch between the expected dimension denotation and the actual dimension denotation, an error should be reported. ## Type Denotation See the [type denotation documentation](TypeDenotation.md) for more details on how to describe images and other types. onnx-onnx-bca0315/docs/ExternalData.md000066400000000000000000000102431511334557700177150ustar00rootroot00000000000000 # External Data ## Loading an ONNX Model with External Data * [Default] If the external data is under the same directory of the model, simply use `onnx.load()` ```python import onnx onnx_model = onnx.load("path/to/the/model.onnx") ``` * If the external data is under another directory, use `load_external_data_for_model()` to specify the directory path and load after using `onnx.load()` ```python import onnx from onnx.external_data_helper import load_external_data_for_model onnx_model = onnx.load("path/to/the/model.onnx", load_external_data=False) load_external_data_for_model(onnx_model, "data/directory/path/") # Then the onnx_model has loaded the external data from the specific directory ``` ## Converting an ONNX Model to External Data ```python import onnx from onnx.external_data_helper import convert_model_to_external_data onnx_model = ... # Your model in memory as ModelProto convert_model_to_external_data(onnx_model, all_tensors_to_one_file=True, location="filename", size_threshold=1024, convert_attribute=False) # Must be followed by save_model to save the converted model to a specific path onnx.save_model(onnx_model, "path/to/save/the/model.onnx") # Then the onnx_model has converted raw data as external data and saved to specific directory ``` ## Converting and Saving an ONNX Model to External Data ```python import onnx onnx_model = ... # Your model in memory as ModelProto onnx.save_model(onnx_model, "path/to/save/the/model.onnx", save_as_external_data=True, all_tensors_to_one_file=True, location="filename", size_threshold=1024, convert_attribute=False) # Then the onnx_model has converted raw data as external data and saved to specific directory ``` ## onnx.checker for Models with External Data ### Models with External Data (<2GB) Current checker supports checking models with external data. Specify either loaded onnx model or model path to the checker. ### Large models >2GB However, for those models larger than 2GB, please use the model path for onnx.checker and the external data needs to be under the same directory. ```python import onnx onnx.checker.check_model("path/to/the/model.onnx") # onnx.checker.check_model(loaded_onnx_model) will fail if given >2GB model ``` ## TensorProto: data_location and external_data fields There are two fields related to the external data in TensorProto message type. ### data_location field `data_location` field stores the location of data for this tensor. Value MUST be one of: * `DEFAULT` - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specific field. * `EXTERNAL` - data stored in an external location as described by external_data field. If not set, behaves as if the value was `DEFAULT`. ### external_data field `external_data` field stores key-value pairs of strings describing data location Recognized keys are: * `"location"` (required) - file path relative to the filesystem directory where the ONNX protobuf model was stored. Up-directory path components such as .. are disallowed and should be stripped when parsing. * `"offset"` (optional) - position of byte at which stored data begins. Integer stored as string. Offset values SHOULD be multiples of the page size (usually 4kb) to enable mmap support. On Windows, offset values SHOULD be multiples of the VirtualAlloc [allocation granularity](https://learn.microsoft.com/en-us/windows/win32/api/sysinfoapi/ns-sysinfoapi-system_info) (usually 64kb) to enable [memory mapping](https://learn.microsoft.com/en-us/windows/win32/api/memoryapi/nf-memoryapi-mapviewoffile). * `"length"` (optional) - number of bytes containing data. Integer stored as string. * `"checksum"` (optional) - SHA1 digest of file specified in under 'location' key. After an ONNX file is loaded, all `external_data` fields may be updated with an additional key `("basepath")`, which stores the path to the directory from which he ONNX model file was loaded. ### External data files Data stored in external data files will be in the same binary bytes string format as is used by the `raw_data` field in current ONNX implementations. Reference https://github.com/onnx/onnx/pull/678 onnx-onnx-bca0315/docs/Hub.md000066400000000000000000000217441511334557700160670ustar00rootroot00000000000000 # ONNX Model Hub The ONNX Model Hub is a simple and fast way to get started with state of the art pre-trained ONNX models from the [ONNX Model Zoo](https://github.com/onnx/models). Furthermore, this allows researchers and model developers the opportunity to share their pre-trained models with the broader community. ## Install The ONNX Model hub is available after ONNX 1.11.0. ## Basic usage The ONNX Model Hub is capable of downloading, listing, and querying trained models from any git repository, and defaults to the official [ONNX Model Zoo](https://github.com/onnx/models). In this section we demonstrate some of the basic functionality. First please import the hub using: ```python from onnx import hub ``` #### Downloading a model by name The `load` function will default to searching the model zoo for the latest model with a matching name, download this model to a local cache, and load the model into a `ModelProto` object for use with the ONNX runtime. ```python model = hub.load("resnet50") ``` #### Downloading from custom repositories Any repository with the proper structure can be a ONNX model hub. To download from other hubs, or to specify a particular branch or commit on the main model hub one can provide the `repo` parameter: ```python model = hub.load("resnet50", repo="onnx/models:771185265efbdc049fb223bd68ab1aeb1aecde76") ``` #### Listing and inspecting Models The model hub provides APIs for querying the model zoo to learn more about available models. This does not download the models, but rather just returns information about models matching the given arguments ```python # List all models in the onnx/models:main repo all_models = hub.list_models() # List all versions/opsets of a specific model mnist_models = hub.list_models(model="mnist") # List all models matching a given "tag" vision_models = hub.list_models(tags=["vision"]) ``` One can also inspect the metadata of a model prior to download with the `get_model_info` function: ```python print(hub.get_model_info(model="mnist", opset=8)) ``` This will print something like: ``` ModelInfo( model=MNIST, opset=8, path=vision/classification/mnist/model/mnist-8.onnx, metadata={ 'model_sha': '2f06e72de813a8635c9bc0397ac447a601bdbfa7df4bebc278723b958831c9bf', 'model_bytes': 26454, 'tags': ['vision', 'classification', 'mnist'], 'io_ports': { 'inputs': [{'name': 'Input3', 'shape': [1, 1, 28, 28], 'type': 'tensor(float)'}], 'outputs': [{'name': 'Plus214_Output_0', 'shape': [1, 10], 'type': 'tensor(float)'}]}, 'model_with_data_path': 'vision/classification/mnist/model/mnist-8.tar.gz', 'model_with_data_sha': '1dd098b0fe8bc750585eefc02013c37be1a1cae2bdba0191ccdb8e8518b3a882', 'model_with_data_bytes': 25962} ) ``` ## Local Caching The ONNX Model hub locally caches downloaded models in a configurable location so that subsequent calls to `hub.load` do not require network connection. #### Default cache location The hub client looks for the following default cache locations in this order: 1) `$ONNX_HOME/hub` if the `ONNX_HOME` environment variable is defined 2) `$XDG_CACHE_HOME/hub` if the `XDG_CACHE_HOME` environment variable is defined 3) `~/.cache/onnx/hub` where `~` is the user home directory #### Setting the cache location To manually set the cache location use: ```python hub.set_dir("my/cache/directory") ``` Additionally one can inspect the cache location with: ```python print(hub.get_dir()) ``` #### Additional cache details To clear the model cache one can simply delete the cache directory using a python utility like `shutil` or `os`. Furthermore one can choose to override the cached model using the `force_reload` option: ```python model = hub.load("resnet50", force_reload=True) ``` We include this flag for completeness but note that models in the cache are disambiguated with sha256 hashes so the force_reload flag is not necessary for normal use. Finally we note that the model cache directory structure will mirror the directory structure specified by the `model_path` field of the manifest, but with file names disambiguated with model SHA256 Hashes. This way, the model cache is human readable, can disambiguate between multiple versions of models, and can re-use cached models across different hubs if they have the same name and hash. ## Architecture ![ONNX Hub Architecture](images/onnx_hub_arch.svg) The ONNX Hub consists of two main components, the client and the server. The client code currently is included in the `onnx` package and can be pointed at a server in the form of a hosted `ONNX_HUB_MANIFEST.json` within a github repository such as [the one in the ONNX Model Zoo](https://github.com/onnx/models/blob/main/ONNX_HUB_MANIFEST.json). This manifest file is a JSON document which lists all models and their metadata and is designed to be programming language agnostic. An example of a well formed model manifest entry is as follows: ```json { "model": "BERT-Squad", "model_path": "text/machine_comprehension/bert-squad/model/bertsquad-8.onnx", "onnx_version": "1.3", "opset_version": 8, "metadata": { "model_sha": "cad65b9807a5e0393e4f84331f9a0c5c844d9cc736e39781a80f9c48ca39447c", "model_bytes": 435882893, "tags": ["text", "machine comprehension", "bert-squad"], "io_ports": { "inputs": [ { "name": "unique_ids_raw_output___9:0", "shape": ["unk__475"], "type": "tensor(int64)" }, { "name": "segment_ids:0", "shape": ["unk__476", 256], "type": "tensor(int64)" }, { "name": "input_mask:0", "shape": ["unk__477", 256], "type": "tensor(int64)" }, { "name": "input_ids:0", "shape": ["unk__478", 256], "type": "tensor(int64)" } ], "outputs": [ { "name": "unstack:1", "shape": ["unk__479", 256], "type": "tensor(float)" }, { "name": "unstack:0", "shape": ["unk__480", 256], "type": "tensor(float)" }, { "name": "unique_ids:0", "shape": ["unk__481"], "type": "tensor(int64)" } ] }, "model_with_data_path": "text/machine_comprehension/bert-squad/model/bertsquad-8.tar.gz", "model_with_data_sha": "c8c6c7e0ab9e1333b86e8415a9d990b2570f9374f80be1c1cb72f182d266f666", "model_with_data_bytes": 403400046 } } ``` These important fields are: - `model`: The name of the model used for querying - `model_path`: The relative path of the model stored in Git LFS. - `onnx_version`: The ONNX version of the model - `opset_version`: The version of the opset. The client downloads the latest opset if left unspecified. - `metadata/model_sha`: Optional model sha specification for increased download security - `metadata/tags`: Optional high level tags to help users find models by a given type All other fields in the `metadata` field are optional for the client but provide important details for users. ## Adding to the ONNX Model Hub #### Contributing an official model The simplest way to add a model to the official `onnx/models` version model hub is to follow [these guidelines](https://github.com/onnx/models/blob/main/contribute.md) to contribute your model. Once contributed, ensure that your model has a markdown table in its `README.md` ([Example](https://github.com/onnx/models/tree/main/validated/vision/classification/mobilenet)). The model hub manifest generator will pull information from these markdown tables. To run the generator: ```shell script git clone https://github.com/onnx/models.git git lfs pull --include="*" --exclude="" cd models/workflow_scripts python generate_onnx_hub_manifest.py ``` Once a new manifest is generated add, submit it in a pull request to ``onnx/models`` #### Hosting your own ONNX Model Hub To host your own model hub, add an `ONNX_HUB_MANIFEST.json` to the top level of your github repository ([Example](https://github.com/onnx/models/blob/main/ONNX_HUB_MANIFEST.json)). At a minimum your manifest entries should include the fields mentioned in the [Architecture Section](Hub.md#Architecture) of this document. Once committed, check that you can download models using the "Downloading from custom repositories" section of this doc. ## Raise issue if any - For ONNX model problem or SHA mismatch issue, please raise issue in [Model Zoo]/(https://github.com/onnx/models/issues). - Other questions/issues regarding the usage of ONNX Model Hub, please raise issue in [this repo](https://github.com/onnx/onnx/issues). onnx-onnx-bca0315/docs/IR.md000066400000000000000000001362171511334557700156650ustar00rootroot00000000000000 # Open Neural Network Exchange Intermediate Representation (ONNX IR) Specification __Purpose__ This document contains the normative specification of the semantics of ONNX. The `.proto` and `.proto3` files found under the [onnx folder](/onnx/) form the normative specification of its syntax authored in the [Protocol Buffers](https://developers.google.com/protocol-buffers) definition language. Commentary found in the `.proto` and `.proto3` files are intended to improve readability of those files, but are not normative if they conflict with this document. Such conflicts should be reported as documentation bugs. __Notes on model validation__ A [tool](../onnx/checker.py) is available to perform general validation of models against this specification. It is implemented in C++ with a Python command-line wrapper. __Notes on language in this and all related documents__: 1. The use of SHOULD, MUST, MAY and so on in this document is consistent with [RFC 2119](https://www.ietf.org/rfc/rfc2119.txt). 2. The use of 'list' shall denote an ordered collection of items, 'set' shall denote an unordered collection of unique elements, and 'bag' an unordered collection of possibly non-unique elements. ## Components ONNX is an open specification that consists of the following components: 1) A definition of an extensible computation graph model. 2) Definitions of standard data types. 3) Definitions of built-in operators. #1 and #2 together make up the ONNX Intermediate Representation, or 'IR', specification which is covered herein; the built-in operators are covered in documents listed at the end. Specifically, built-in operators are divided into a set of primitive operators and functions. A function is an operator whose semantics is formally expressed via expansion into a sub-graph (called the function body) using other operators (and functions). Functionality-wise, an ONNX compatible framework or runtime may inline a function body to execute it if it does not have corresponding implementation of the function. There are two official ONNX variants; the main distinction between the two is found in the default operator sets. __ONNX-ML__ extends the __ONNX__ operator set with ML algorithms that are not based on neural networks. Up to IR version 6, the ONNX specification and model format addressed only inference (also known as scoring). Starting from IR version 7, the ONNX specification and model format also support training. An ONNX training model is an extension of the inference-model. An inference-only runtime can consume a training model ignoring the training-related extensions. However, an inference-only model may enable a representation that is more optimal for inference purposes than a training model. ## Runtime Agnostic ONNX does not pre-suppose or imply any particular method of runtime implementation. For example, an implementation may consist of a rich runtime which interprets the model; it may be a code generator that translates the model in its entirety to executable code for some target programming language; it may be a hardware implementation; it may be a combination of two or three of those. Nothing in this specification should be construed as advocating one implementation approach over any other; any comments on the inner workings of concrete implementations are to be interpreted as examples. ## ONNX Versioning The IR specification, individual models, and operator sets are all versioned. Furthermore, each individual operator indicates which version of its containing operator set it was introduced or stabilized in. Version numbers can be used as a simple number, or used to encode [semantic versions](https://semver.org/)(AKA SemVer). If using semantic versions, the convention is to use the two most significant bytes for the major number, the next two bytes for the minor number, and the least significant four bytes for the patch/build/bugfix number. When using semantic versioning, at least one of the major/minor numbers MUST be non-zero. The IR specification uses simple monotonically increasing numbers for its versions. The valid IR versions are defined by the `onnx.Version` enumeration in [onnx.proto](/onnx/onnx.proto). Operator sets use a simple version number. Each operator set version represents a snapshot of the set of operators, and their semantics at a particular point in time. This specification does not provide guidance on what versioning scheme model producers should be using. More details on conventions and best practices for versioning of IR, operator sets, and models can be found in [Versioning](Versioning.md). ## Extensible computation graph model ONNX specifies the portable, serialized format of a computation graph. It does not have to be the form a framework chooses to use internally. For example, an implementation may represent the model differently in memory if it is more efficient to manipulate during optimization passes. An implementation MAY extend ONNX by adding operators expressing semantics beyond the standard set of operators that all implementations MUST support. The mechanism for this is adding operator sets to the `opset_import` property in a model that depends on the extension operators. ### Models The top-level ONNX construct is a ‘Model.’, and is represented in protocol buffers as the type `onnx.ModelProto` The main purpose of the model structure is to associate metadata with a graph which contains all the executable elements. The metadata is used when first reading the model file, giving an implementation the information it needs in order to determine whether it will be able to execute the model, generate logging messages, error reports, etc. Further, the metadata is useful to tools, such as IDEs and model galleries, which need it for informing humans about a given model’s purpose and characteristics. Each model has the following components: |Name|Type|Description| |---|---|---| |ir_version|int64|The ONNX version assumed by the model.| |opset_import|OperatorSetId|A collection of operator set identifiers made available to the model. An implementation must support all operators in the set or reject the model.| |producer_name|string|The name of the tool used to generate the model.| |producer_version|string|The version of the generating tool.| |domain|string|A reverse-DNS name to indicate the model namespace or domain, for example, 'org.onnx'| |model_version|int64|The version of the model itself, encoded in an integer.| |doc_string|string|Human-readable documentation for this model. Markdown is allowed.| |graph|Graph|The parameterized graph that is evaluated to execute the model.| |metadata_props|map|Named metadata values; keys should be distinct.| |training_info|TrainingInfoProto[]|An optional extension that contains information for training.| |functions|FunctionProto[]|An optional list of functions local to the model.| |configuration|DeviceConfigurationProto[]|(IR version >= 11) An optional list of multi-device configurations for distributed execution.| Models MUST specify a domain and use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html). __Note: Exploring an ONNX file__ You can use the `protoc` tool that is part of the Protocol Buffers distribution to examine the contents of an ONNX file, you do so like this: ```bash protoc --decode=onnx.ModelProto onnx.proto < yourfile.onnx ``` Where [onnx.proto](/onnx/onnx.proto) is the file that is part of this repository. Alternatively, you can use a tool like [Netron](https://github.com/lutzroeder/netron) to explore the ONNX file. ### Model Semantics The semantics of an inference-model is a _stateless function_ (except possibly for the state used for random-number generation). Thus, whenever an inference-model (without random-generator operations) is used to perform inference on the same input, it is expected to produce the same output. The semantics of a training model is that of a _stateful object_, with the state consisting of the current values of trained-weights (and any other auxiliary state required, such as momentum, for example, used by the learning algorithm). Specifically, its semantics is captured via three methods: an initialization method (which is used to initialize or reset the values of state variables), a training step method (to train using a batch of input-output pairs), and an inference method to perform inference using the current values of the learned weights. The first two methods update the state of the object, while the third method is side-effect-free. ### Optional Metadata The 'metadata_props' field in the model is available for any kind of optional metadata that a tool or model developer chooses to place there. The following are the defined “standard” optional metadata properties of a model. Name|Type|Format|Description |---|---|---|---| model_author|string|A comma-separated list of names.|The personal name of the author(s) of the model, and/or their organizations. model_license|string|Name or URL.|The well-known name or URL of the license under which the model is made available. ### Operator Set Identifiers Each operator set is uniquely identified by a (domain, version) pair. Name|Type|Description |---|---|---| domain|string|The domain of the operator set being identified. version|int64|The version of the operator set being identified. Same as 'opset_version' in the operator set. The operator set version is a simple integer value that is monotonically increased as new versions of the operator set are published. Operator sets other than the default operator set MUST specify a domain and SHOULD use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html). ### Operator Sets Each model MUST explicitly name the operator sets that it relies on for its functionality. Operator sets define the available operators and their version. Each model defines the imported operator sets by their domains. All models implicitly import the default ONNX operator set. Each operator set SHALL be defined in a separate document, also using protobuf as the serialization format. How operator set documents are found at runtime is implementation-dependent. __Note: As of the publication of this document, no ONNX implementation is known to process operator set documents.__ The properties of an operator set are: Name|Type|Description |---|---|---| magic|string|The value ‘ONNXOPSET’ ir_version|int32|The ONNX version corresponding to the operators. ir_version_prerelease|string|The prerelease component of the SemVer of the IR. ir_build_metadata|string|The build metadata of this version of the operator set. domain|string|The domain of the operator set. Must be unique among all sets. opset_version|int64|The version of the operator set. doc_string|string|Human-readable documentation for this operator set. Markdown is allowed. operator|Operator[]|The operators contained in this operator set. The operator set version is a simple integer value that is monotonically increased as new versions of the operator set are published. Operator sets other than the default operator set MUST specify a domain and SHOULD use reverse domain names based on the responsible organization's identity, the same convention that is used for [naming Java packages](https://docs.oracle.com/javase/tutorial/java/package/namingpkgs.html). ### Operators Each operator used within a graph MUST be explicitly declared by one of the operator sets imported by the model. The properties of an operator definition are: Name|Type|Description |---|---|---| op_type|string|The name of the operator (case sensitive), as used in graph nodes. MUST be unique within the operator set’s domain. since_version|int64|The version of the operator set when this operator was introduced. status|OperatorStatus|One of ‘EXPERIMENTAL’ or ‘STABLE.’ doc_string|string|A human-readable documentation string for this operator. Markdown is allowed. The version value MUST be the same value as the operator set version when the operator was first published. Subsequent versions of the operator set MUST NOT alter the signature or semantics of the operator once published as STABLE. The ‘status’ property indicates whether the syntax, semantics, or presence of the operator is in an experimental or stable stage. Once an operator is published as STABLE, it’s syntax and semantics MUST NOT change in subsequent versions of the operator set. There are two distinct ways to pass information to operators – inputs and attributes. Inputs represent graph inputs or values computed elsewhere in the graph, while attributes are used for values that are constants in the graph. This distinction may be highly relevant to achieving good performance for some implementations, while completely irrelevant to others. ### Functions A _function_ may be thought of as an operator combined with an implementation of the operator using other, more primitive, ops, referred to as the _function body_. The function body consists of a topologically sorted list of nodes that form a graph. Thus, a function combines aspects of both an operator as well a graph (described below). Each function contained in a Model (also referred to as a model-local function) serves as a default or fallback implementation of the corresponding operator. A runtime, however, may choose to use an alternative implementation of the operator (usually as an optimized kernel). As such, the unique name of a function is significant as it is implicitly associated with a semantic specification. A serialized function (a _FunctionProto_) has the following properties: |Name|Type|Description| |---|---|---| name|string|The name of the function domain|string|The domain to which this function belongs overload|string|Part of unique id of function (added in IR version 10) doc_string|string|Human-readable documentation for this function. Markdown is allowed. attribute|string[]|The attribute parameters of the function attribute_proto|Attribute[]| (IR version 9+) The attribute parameters with default values of the function. A function attribute shall be represented either as a string attribute or an Attribute, not both. input|string[]|The input parameters of the function output|string[]|The output parameters of the function. node|Node[]|A list of nodes, forming a partially ordered computation graph. It must be in topological order. |opset_import|OperatorSetId|A collection of operator set identifiers used by the function implementation. |value_info|ValueInfo[]| (IR version >= 10) Used to store the type and shape information of values used in the function. |metadata_props|map|(IR version >= 10) Named metadata values; keys should be distinct. The name and domain serve to identify the operator uniquely in IR versions upto 9. IR version 10 adds the field overload, and the triple (name, domain, overload) acts as a unique-id across functions stored in a model. This is intended to support cases where distinct function-bodies are required for distinct calls to the function within the model. An opset version is not explicitly identified in a FunctionProto, but it is implicitly determined by the opset version of the domain included in the model. The input, output, attribute, and attribute_proto (added in IR version 9) constitute the signature part of the operator. No type information is explicitly included in the signature. The attribute_proto field describes attribute parameters of the function along with their default-value (when not specified by an call-site node), while the attribute field lists attribute parameters without a default-value. The names in these two lists must be distinct. When an attribute-parameter of the function is used in a node within the function, it is replaced by the actual parameter value specified for the attribute at a call-site node (of the function) when such a attribute is specified, and it is replaced by the default-value if the attribute has a default-value specified, and it is omitted otherwise. The opset_import and node fields describe the implementation of the function. The value_info field (added in IR version 10) allows a model to store type and shape information about the values used in a function, including its inputs and outputs. Note that this is optional, and ONNX allows functions to be polymorphic. ### Graphs A graph is used to describe a side-effect-free computation (function). A serialized graph is comprised of a set of metadata fields, a list of model parameters, and a list of computation nodes. Each computation dataflow graph is structured as a topologically sorted list of nodes that form a graph, which MUST be free of cycles. Each node represents a call to an operator or a model local function. Each node has zero or more inputs and one or more outputs. Graphs have the following properties: |Name|Type|Description| |---|---|---| name|string|The name of the model graph. node|Node[]|A list of nodes, forming a partially ordered computation graph based on input/output data dependencies. It is in topological order. initializer|Tensor[]|A list of named tensor values. When an initializer has the same name as a graph input, it specifies a default value for that input. When an initializer has a name different from all graph inputs, it specifies a constant value. The order of the list is unspecified. doc_string|string|Human-readable documentation for this model. Markdown is allowed. input|ValueInfo[]|The input parameters of the graph, possibly initialized by a default value found in ‘initializer.’ output|ValueInfo[]|The output parameters of the graph. Once all output parameters have been written to by a graph execution, the execution is complete. value_info|ValueInfo[]|Used to store the type and shape information of values that are not inputs or outputs. |metadata_props|map|(IR version >= 10) Named metadata values; keys should be distinct. ValueInfo has the following properties: Name|Type|Description |---|---|---| name|string|The name of the value/parameter. type|Type|The type of the value **including shape information**. doc_string|string|Human-readable documentation for this value. Markdown is allowed. Each main (top-level) graph MUST define the names, types and shapes of its inputs and outputs, which are specified as ‘value info’ structures. The main graph inputs and outputs are required to have a shape, indicating the rank, even though the exact dimensions need not be specified. Nested subgraphs (specified as attribute values) MUST define the names of its inputs and outputs and MAY define the types of its inputs and outputs. Each graph MUST specify a name. The graph MUST adhere to single static assignment (SSA) for all node outputs; this means that all node output names MUST be unique within a graph. Graphs SHOULD be populated with documentation strings, which MAY be interpreted using GitHub-style markdown syntax. HTML and other text-markup languages MAY NOT be used in documentation strings. ### Names Within a Graph All names SHOULD adhere to [C90 identifier syntax rules](https://en.cppreference.com/w/c/language/identifier). Names of nodes, inputs, outputs, initializers, and attributes are organized into several namespaces. Within a namespace, each name MUST be unique for each given graph. Please see below for further clarification in the case where a graph contains nested subgraphs (as attribute values). The namespaces are: Namespace|Description |---|---| Attribute|The names of attributes of an operator. Unique for each operator. Value|The names of values – node inputs & outputs, tensor values (if named), graph inputs, outputs. Node|The names of graph nodes. Graph|The names of graphs within a domain, unique within the model domain. Operator|The names of operators within a domain. Shape|The names of tensor shape variables – scoped to the value information records of a graph, which is where shape variables occur. ### Nodes Computation nodes are comprised of a name, the name of an operator that it invokes, a list of named inputs, a list of named outputs, and a list of attributes. Input and outputs are positionally associated with operator inputs and outputs. Attributes are associated with operator attributes by name. They have the following properties: Name|Type|Description |---|---|---| name|string|An optional name of the node, used for diagnostic purposes only. input|string[]|Names of the values used by the node to propagate input values to the node operator. It must refer to either a graph input, a graph initializer or a node output. output|string[]|Names of the outputs used by the node to capture data from the operator invoked by the node. It either introduces a value in the graph or refers to a graph output. op_type|string|The symbolic identifier of the operator to invoke. domain|string|The domain of the operator set that contains the operator named by the op_type. attribute|Attribute[]|Named attributes, another form of operator parameterization, used for constant values rather than propagated values. doc_string|string|Human-readable documentation for this value. Markdown is allowed. overload|string|Part of unique id of function (added in IR version 10) |metadata_props|map|(IR version >= 10) Named metadata values; keys should be distinct. |device_configurations|NodeDeviceConfigurationProto[]|(IR version >= 11) Multi-device execution configurations for this node. A name belonging to the Value namespace may appear in multiple places, namely as a graph input, a graph initializer, a graph output, a node input, or a node output. The occurrence of a name as a graph input, a graph initializer, or as a node output is said to be a definition and the occurrence of a name as a node input or as a graph output is said to be a use. A value name used in a graph must have a unique definition, with the exception that the same name MAY appear in both the graph input list and graph initializer list. (Further exceptions apply in the presence of nested subgraphs, as described later.) When a name appears in both the initializer list and the graph input list, a runtime MAY allow a caller to specify a value for this (input) name overriding the value specified in the initializer and a runtime MAY allow users to omit specifying a value for this (input) name, choosing the value specified in the initializer. Names of constants that are not meant to be overridden by the caller should appear only in the initializer list and not in the graph input list. In models with IR version >= 4, in nested subgraphs used as attribute values, users MUST NOT use the same name as both a subgraph initializer and subgraph input unless the corresponding op's specification explicitly allows it. In models with IR version <= 3, users MAY use the same name as both a subgraph initializer and subgraph input, but this is restricted to support constants via initializers that are not intended to correspond to any actual inputs passed from the node into the subgraph. In particular, the control-flow operator semantics determines the set of inputs supplied to the execution of the subgraph, and these input names MUST NOT appear as subgraph initializers. Subgraph initializer names must appear in the graph input list _after_ the actual inputs. This allows the actual inputs and formal inputs to be matched positionally. Edges in the computation graph are established by outputs of one node being referenced by name in the inputs of a subsequent node. The outputs of a given node introduce new names into the graph. The values of node outputs are computed by the node's operator. Node inputs MAY refer to node outputs, graph inputs, and graph initializers. When the name of a node output coincides with the name of a graph output, the graph output's value is the corresponding output value computed by that node. A node input in a nested subgraph MAY refer to names introduced in outer graphs (as node outputs, graph inputs, or graph initializers). The graph MUST use single static assignment for all node outputs, which means that all node output names MUST be unique within a graph. In the case of a nested subgraph, a node output name and names of inputs and initializers of the subgraph MUST be distinct from the names from the outer scopes that are visible in the nested subgraph. That is, variable shadowing is not allowed. Node dependencies MUST NOT create cycles in the computation graph. The number of inputs and outputs in a node, their types, the set of attributes specified in a node and their types MUST satisfy the constraints imposed by the signature of the node’s operator. The list of nodes defining the top-level computation graph MUST be ordered topologically; that is, if node K follows node N in the graph, none of the data inputs of N may refer to outputs of K. Node attributes are used to pass literal (static) values to operators. #### Input and Output Values The representation distinguishes between two kinds of values: attribute values, which are statically known, and input/output values. The types of values permitted in the two cases are different. Input and output values are found as graph inputs, outputs, and initializers, and as node inputs and outputs. Their values are determined at runtime, either by the code that initiates model execution, or by operators computing output values. #### Attributes Attribute values are only found in nodes, passed to operators by name association. Attribute values are runtime constants, in that their values are determined when a model graph is constructed and therefore not computed at runtime. A common use for attributes is to represent coefficients established during model training. Attributes have the following properties: Name|Type|Description |---|---|---| name|string|The name of the attribute. Must be unique among attributes, inputs, and outputs for any given operator and node. doc_string|string|Human-readable documentation for this value. Markdown is allowed. type|AttributeType|The type of the attribute, determining which of the remaining fields is used to hold the value of the attribute. f|float|A 32-bit floating-point value. i|int64|A 64-bit integer value. s|byte[]|UTF-8 string. t|Tensor|A tensor value. g|Graph|A graph. floats|float[]|A list of 32-bit floating-point values. ints|int64[]|A list of 64-bit integer values. strings|byte[][]|A list of UTF-8 strings. tensors|Tensor[]|A list of tensor values. graphs|Graph[]|A list of graphs. ref_attr_name|string|The name of a parent function's attribute. The properties ‘name’ and ‘type’ are required on all attributes, and ‘doc_string’ SHOULD be used on all attributes. An attribute MUST have only one of the value-carrying properties. In case ‘ref_attr_name’ is set, this attribute does not contain data, and instead it's a reference to the parent function's attribute of the given name. Can only be used within the function body. #### Variadic Inputs and Outputs The last input or output of an operator MAY be marked as variadic. For example, the operator 'Max()' can be used to compute the maximum of a varying number of input values. A variadic operator has a minimum arity, which specifies the minimum number of operands that must be specified. For each variadic operator input, N or more node inputs must be specified where N is the minimum arity of the operator. For each variadic operator output, N or more node outputs must be specified where N is the minimum arity of the operator. #### Optional Inputs and Outputs ##### Static Optional Some operators have inputs that are marked as optional, which means that a referring node MAY forgo providing values for such inputs. Some operators have outputs that are optional. When an actual output parameter of an operator is not specified, the operator implementation MAY forgo computing values for such outputs. There are two ways to leave an optional input or output unspecified: the first, available only for trailing inputs and outputs, is to simply not provide that input or output; the second method is to use an empty string in place of an input or output name. Each node referring to an operator with optional outputs MUST provide a name for each output that is computed and MUST NOT provide names for outputs that are not computed. Optional inputs and outputs of the above kind are referred to as _static-optional_. ##### Dynamic Optional (since IR-8) **IR-8 Version** introduced a new type-constructor to represent _dynamic-optional_ inputs and outputs, in addition to the earlier static-optional version described above. A dynamic-optional INT64 tensor is a distinct type from an INT64 tensor type. In contrast, a static-optional INT64 tensor does not have a distinct type, it has the same type as a INT64 tensor. The operators `Optional` and `OptionalGetElement` MUST be explicitly used to convert between the dynamic-optional type and the underlying non-optional type. The dynamic-optional allows for more expressiveness than static-optional. #### External Tensor Data The raw data for large constant tensors, such as initializers, MAY be serialised in a separate file. In such a case, the tensor MUST provide the filename relative to the model file and MUST NOT use the value fields. It MAY provide a byte offset and length within that file. It MAY also specify a SHA1 digest of the file. One file MAY contain the data for multiple tensors. More details can be found in [External Data](ExternalData.md). ## Standard data types There are two official ONNX variants; the main distinction between the two is found in the supported types and the supported operators. With respect to supported types, both __ONNX__ and __ONNX-ML__ definition recognize tensors, sparse tensors, sequences, maps, and optionals as input and output types. Sequences and maps were supported from the IR version 6 (ONNX 1.6.0 release). Optional type was supported from IR version 8 (ONNX 1.10.0 release). The following data types are supported by ONNX for inputs and outputs of graphs and nodes as well as the initializers of a graph. Primitive numeric, string, and Boolean types MUST be used as elements of tensors. ### Tensor Definition Tensors are a generalization of vectors and matrices; whereas vectors have one dimension, and matrices two, tensors can have any number of dimensions, including zero. A zero-dimensional tensor is logically equivalent to a scalar value. Mathematically, a tensor can be defined as a pair of sequences/lists (V, S) where S is the shape of the tensor (a list of non-negative integers) and V is a list of values with length equal to the product of the dimensions in S. Two tensors (V, S) and (V', S') are equal if and only if V = V' and S = S'. The length of S is referred to as the rank. - If S has length 0, V must have length 1, since the empty product is defined to be 1. In this case, the tensor represents a scalar. - S can contain dimensions of value 0. If any dimensions are 0, V must have length 0. - If S has length 1, V has length equal to the single dimension in S. In this case, the tensor represents a vector. - A tensor representing a vector of length 1 has shape [1], while a tensor representing a scalar has shape []. They both have a single element, but scalars are _not_ vectors of length 1. A tensor's shape S is a list but can be represented as a tensor with values S and shape [R] where R is the rank of the tensor. - For a tensor (V, S), the tensor representing its shape is (S, [R]). - The shape of a scalar is []. Represented as a tensor, [] has shape [0]. #### Representation It is common to represent a tensor as a nested list. This generally works fine, but is problematic when zero dimensions are involved. A tensor of shape (5, 0) can be represented as [[], [], [], [], []], but (0, 5) is represented as [] which loses the information that the second dimension is 5. - A nested list is not a complete representation of a tensor with dimensions of value zero. ### Tensor Element Types |Group|Types|Description| |---|---|---| Floating Point Types|float16, float32, float64, bfloat16, float8e4m3fn, float8e5m2, float8e4m3fnuz, float8e5m2fnuz, float4e2m1|Values adhering to the IEEE 754-2008 standard representation of floating-point data or defined in papers [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433), [8-bit Numerical Formats for Deep Neural Networks](https://arxiv.org/abs/2206.02915), and the [Open Compute Project](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) Signed Integer Types|int2, int4, int8, int16, int32, int64|Signed integers are supported for 2-64 bit widths. Unsigned Integer Types|uint2, uint4, uint8, uint16, uint32, uint64|Unsigned integers are supported for 2-64 bit widths. Complex Types|complex64, complex128|A complex number with either 32- or 64-bit real and imaginary parts. Other|string|Strings represent textual data. All strings are encoded using UTF-8. Other|bool|Boolean values represent data with only two values, typically true and false. ### Input / Output Data Types The following types are used to define the types of graph and node inputs and outputs. |Variant | Type | Description | |---|---|---| ONNX|dense tensors|Represents a Tensor. See definition above. ONNX|sequence|Sequences are dense, ordered, collections of elements that are of homogeneous types. ONNX|map|Maps are associative tables, defined by a key type and a value type. ONNX|optional|Optionals are wrappers that may contain an element of tensor, sequence, or map type, or may be empty (containing none). [Details](ONNXTypes.md) #### Static tensor shapes In addition to element type, tensor types have a **static** shape. The static shape of a tensor variable is related to, but different from, the runtime (dynamic) shape of a tensor value. A static tensor shape is a list of records that indicates whether the tensor is a vector, a matrix, or a higher-dimensional value. For example, a 100x100 matrix has the shape [100,100]. The static shape is defined by 'TensorShapeProto': ```proto message TensorShapeProto { message Dimension { oneof value { int64 dim_value = 1; string dim_param = 2; }; }; repeated Dimension dim = 1; } ``` Which is referenced by the Tensor type message: ```proto message Tensor { optional TensorProto.DataType elem_type = 1; optional TensorShapeProto shape = 2; } ``` The empty list of dimension sizes, [], is a valid tensor shape, denoting a zero-dimension (scalar) value. A zero-dimension tensor is distinct from a tensor of unknown dimensionality, which is indicated by an absent 'shape' property in the Tensor message. When the shape property is absent in the type of a value (including node input), it indicates that the corresponding runtime value may have any shape. This sub-section describes how to interpret a missing-shape or a shape with missing dimensions etc. However, specific usage contexts may impose further constraints on a type and shape. For example, the inputs and outputs of a model (top-level graph) are required to *have* a shape, indicating the rank of inputs and outputs, even though the exact dimensions need not be specified. Each size in the list MAY be expressed as an integral value or as a "dimension variable," a string denoting that the actual size of the dimension is not statically constrained to a particular number. This is useful for declaring interfaces that care about the number of dimensions, but not the exact size of each dimension. A dimension MAY have neither dim_value nor dim_param set. Such a dimension represents an unknown dimension unrelated to other unknown dimensions. For example, a NxM matrix would have the shape list [N,M]. The name of each dimension variable SHOULD adhere to [C90 identifier syntax rules](https://en.cppreference.com/w/c/language/identifier). Currently, dimension variables are not scoped. A dimension variable "N" represents the same value across the entire graph in a model. For example, if the graph has two inputs X and Y each with shape ["N"], then at runtime the values passed in for X and Y MUST be tensors of rank 1 with the same dimension. Nested sub-graphs currently share the same scope for dimension variables as the main-graph. This allows a model to relate the dimensions of tensors inside the subgraph to the dimensions of tensors in the outer graph. ONNX supports types such as Sequences of Tensors. The global scoping of dimension variables means that a variable with type "Sequence" represents a sequence of tensors that *all have the same shape*. The dimension variables M or N must be omitted from the above type if that dimension does not have a fixed size across all tensors in the sequence. The entire shape must be omitted from the type if different tensors in the sequence may have different ranks. For example, a graph that performs matrix cross-product may be defined as taking two inputs of shape [K,M] and [M,N], and producing an output of shape [K,N]. Shapes MAY be defined using a combination of integers and variables. _Historical Notes_: The following extensions were considered early on, but were never implemented or supported. * The use of an empty string (as a dimension variable) to denote an unknown dimension not related to any other dimension. This was discarded in favor of using a Dimension with neither dim_value nor dim_param set. * The use of the string "\*" (as a dimension variable) to denote a sequence of zero or more dimensions of unknown cardinality. This is not supported. In the current implementation, the number of dimensions in a shape MUST represent the rank of the tensor. A tensor of unknown rank is represented using a TypeProto::Tensor object with no shape, which is legal. * A scoping mechanism to allow dimension variables that are local to a sub-graph (such as the body of a loop) may be useful, but is not currently supported. * ONNX supports types such as Sequences of Tensors. A scoping mechanism for the dimension variables local to a type may be useful to distinguish between the following two types: a sequence of square matrices (of differing sizes) vs a sequence of square matrices (all of same size). This is not currently supported. ### Attribute Types The type system used for attributes is related to but slightly different from that used for of inputs and outputs. Attribute values may be a dense tensor, sparse tensor, a scalar numerical value, a string, a graph, or repeated values of one of the above mentioned types. ## Other Metadata The ModelProto structure, and in IR versions >= 10, various other structures (GraphProto, FunctionProto, NodeProto) contain a metadata_props field allowing users to store other metadata in the form of key-value pairs. It is recommended that users use key names qualified with a reverse-DNS name as prefix (such as "ai.onnxruntime.key1") to avoid conflicts between different uses. Unqualified names may be used in the future by the ONNX standard. ## Training Related Information Training related information is described by one or more instances of _TrainingInfoProto_ contained in a model. Each TrainingInfoProto contains information describing both an initialization step and a training step. The initialization step is described using a Graph (TrainingInfoProto.initialization) and an initialization-binding map (TrainingInfoProto.initialization_binding). The initialization step is performed by evaluating the Graph, and assigning the outputs produced by the Graph to the _state variables_ of the training model as specified in the initialization-binding. The initialization-binding is conceptually a map, specified as a list of key-value pairs, where each key is the name of a state variable, and the value is the name of an output of the (initialization) Graph. Each name specified as a key in the binding MUST be the name of an initializer that appears in the main inference graph (i.e., in ModelProto.graph.initializer) or the name of an initializer that appears in TrainingInfoProto.algorithm.initializer. Each name specified as a value in the binding MUST be the name of an output of the TrainingInfoProto.initialization graph. Key values specified in the repeated initialization_binding field MUST be unique. The training step is similarly described using a Graph (TrainingInfoProto.algorithm) and an update-binding map (TrainingInfoProto.update_binding). The training step is performed by evaluating the Graph and assigning the outputs produced by the Graph to the state variables as specified in the update-binding. The constraints and description presented above for the initialization apply to the training step as well. Thus, the state variables of the training model consist of a subset of the initializers of the main inference graph (i.e., ModelProto.graph.initializer) and the training-algorithm graph (TrainingInfoProto.algorithm.initializer) as identified by the keys of the bindings (in TrainingInfoProto.initialization_binding and TrainingInfoProto.update_binding). Note that the state variables are not constant values in the context of training. They represent mutable variables shared by multiple graphs (implicitly declared in the top-level training model scope). This implicit declaration of shared mutable variables is used instead of an explicit declaration for purposes of backward compatibility with the inference graph representation. All state variables are pre-initialized to the value specified in the corresponding initializer. A subsequent call to perform the initialization step (using the appropriate API exposed by a runtime) updates the values of the state variables as described above. If the training model has more than one instance of TrainingInfoProto, the initialization step corresponding to each is performed in order. A TrainingInfoProto.initialization MAY be omitted (only if there are no initialization_bindings). For the training step, a runtime MAY allow users to invoke any one of the TrainingInfoProto.algorithm, allowing the training process to interleave the different algorithms as desired. The order in which the different TrainingProto.algorithms are called affects the training result, and it is the callers responsibility to call them in the correct order. ## Multi-Device Configuration (IR version >= 11) ONNX supports multi-device execution through device configuration specifications that enable distributed inference and training. This includes support for tensor parallelism (sharding tensors across multiple devices) and pipeline parallelism (distributing different subgraphs to different devices). ### Device Configurations A model MAY specify one or more multi-device configurations using _DeviceConfigurationProto_ contained in the model. Each configuration describes a specific arrangement of devices that can be used for model execution. The properties of a device configuration are: |Name|Type|Description| |---|---|---| |name|string|The name of the configuration. This field MUST be present for this version of the IR.| |num_devices|int32|Number of devices in this configuration. This field MUST be present for this version of the IR.| |device|string[]|Optional names of the devices. MUST be length of num_devices if provided.| ### Node Device Configuration Individual nodes can specify device-specific execution information through _NodeDeviceConfigurationProto_. This allows fine-grained control over how computation is distributed across devices. The properties of a node device configuration are: |Name|Type|Description| |---|---|---| |configuration_id|string|ID of the configuration. MUST match the name of a DeviceConfigurationProto. This field MUST be present for this version of the IR.| |sharding_spec|ShardingSpecProto[]|Sharding specifications for the node's inputs and outputs.| |pipeline_stage|int32|Optional pipeline stage identifier for this node.| ### Sharding Specification Sharding describes how tensors are partitioned or replicated across multiple devices. A _ShardingSpecProto_ defines the sharding behavior for a specific input or output tensor of a node. The properties of a sharding specification are: |Name|Type|Description| |---|---|---| |tensor_name|string|Identifies the input or output tensor being sharded. Must match a name in the node's input or output list. This field MUST be present for this version of the IR.| |device|int64[]|List of devices across which the tensor is sharded or replicated.| |index_to_device_group_map|IntIntListEntryProto[]|Optional map indicating device groups when a device ID represents multiple physical devices.| |sharded_dim|ShardedDimProto[]|Sharding specification for each axis of the tensor.| ### Sharded Dimension A _ShardedDimProto_ describes how a single axis of a tensor is sharded across devices. The properties of a sharded dimension are: |Name|Type|Description| |---|---|---| |axis|int64|The tensor axis being sharded. Must be in range [-r, r-1] where r is tensor rank. This field MUST be present for this version of the IR.| |simple_sharding|SimpleShardedDimProto[]|Describes how the axis is divided into shards.| ### Simple Sharded Dimension A _SimpleShardedDimProto_ specifies that N blocks are divided into M shards, where N may be symbolic but M must be constant. The properties of a simple sharded dimension are: |Name|Type|Description| |---|---|---| |dim_value|int64|Dimension value to be sharded (alternative to dim_param).| |dim_param|string|Symbolic dimension parameter to be sharded (alternative to dim_value).| |num_shards|int64|Number of shards to split the dimension into. This field MUST be present for this version of the IR.| ### Multi-Device Execution Semantics The multi-device annotations are hints to execution backends and do not affect the computational semantics of the model. Backends MAY ignore these annotations if the specified configurations are not supported or available. All communication operations required for multi-device execution (such as data transfers between devices) are implicit and handled by the runtime. For tensor parallelism, tensors can be: - **Split** across devices along specified axes, distributing different portions of the data to different devices - **Replicated** across devices, where the same tensor data is duplicated on multiple devices Pipeline parallelism is indicated through optional pipeline stage identifiers that suggest how to distribute subgraphs across devices for pipelined execution. For more detailed information about multi-device execution patterns and examples, see the [Multi-Device Proposal](proposals/ONNXMultiDeviceProposal.md). ## Other Specification Documents The ONNX specification is comprised of this document, which defines the semantics of the IR and the standard data types, and the following documents defining standard operator semantics and the IR syntax. The latter is specified as Protobuf v2 and v3 schema files. See the [metadata category documentation](MetadataProps.md) for more details. ### Operators [Neural Network Operators](Operators.md) [Classical Machine Learning operators](Operators-ml.md) ### Syntax [ONNX Models and Graphs - protobuf v2](../onnx/onnx.proto) [ONNX Models and Graphs - protobuf v3](../onnx/onnx.proto3) [ONNX-ML Models and Graphs - protobuf v2](../onnx/onnx-ml.proto) [ONNX-ML Models and Graphs - protobuf v3](../onnx/onnx-ml.proto3) [ONNX Operator Sets - protobuf v2](../onnx/onnx-operators.proto) [ONNX Operator Sets - protobuf v3](../onnx/onnx-operators.proto3) [ONNX-ML Operator Sets - protobuf v2](../onnx/onnx-operators-ml.proto) [ONNX-ML Operator Sets - protobuf v3](../onnx/onnx-operators-ml.proto3) ### Versioning Conventions and Best Practices [Versioning](Versioning.md) onnx-onnx-bca0315/docs/ImplementingAnOnnxBackend.md000066400000000000000000000212761511334557700223730ustar00rootroot00000000000000 # Implementing an ONNX backend ## What is an ONNX backend An ONNX backend is a library that can run ONNX models. Since many deep learning frameworks already exist, you likely won't need to create everything from scratch. Rather, you'll likely create a converter that converts ONNX models to the corresponding framework specific representation and then delegate the execution to the framework. For example, [onnx-caffe2 (as part of caffe2)](https://github.com/pytorch/pytorch/tree/v2.3.1/caffe2/python/onnx) , [onnx-coreml](https://github.com/onnx/onnx-coreml), and [onnx-tensorflow](https://github.com/onnx/onnx-tensorflow) are all implemented as converters. ## Unified backend interface ONNX has defined a unified (Python) backend interface at [onnx/backend/base.py](/onnx/backend/base.py). There are three core concepts in this interface: `Device`, `Backend` and `BackendRep`. - `Device` is a lightweight abstraction over various hardware, e.g., CPU, GPU, etc. - `Backend` is the entity that will take an ONNX model with inputs, perform a computation, and then return the output. For one-off execution, users can use `run_node` and `run_model` to obtain results quickly. For repeated execution, users should use `prepare`, in which the `Backend` does all of the preparation work for executing the model repeatedly (e.g., loading initializers), and returns a `BackendRep` handle. - `BackendRep` is the handle that a `Backend` returns after preparing to execute a model repeatedly. Users will then pass inputs to the `run` function of `BackendRep` to retrieve the corresponding results. Note that even though the ONNX unified backend interface is defined in Python, your backend does not need to be implemented in Python. For example, yours can be created in C++, and tools such as [pybind11](https://github.com/pybind/pybind11) or [cython](http://cython.org/) can be used to fulfill the interface. ## ONNX backend test ONNX provides a standard backend test suite to assist backend implementation verification. It's strongly encouraged that each ONNX backend runs this test. Integrating the ONNX Backend Test suite into your CI is simple. The following are some examples demonstrating how a backend performs the integration: - [onnx-caffe2 onnx backend test](https://github.com/pytorch/pytorch/blob/v2.3.1/caffe2/python/onnx/tests/onnx_backend_test.py) - [onnx-tensorflow onnx backend test](https://github.com/onnx/onnx-tensorflow/blob/main/test/backend/test_onnx_backend.py) - [onnx-coreml onnx backend test](https://github.com/onnx/onnx-coreml/blob/master/tests/onnx_backend_models_test.py) If you have [pytest](https://docs.pytest.org/en/latest/) installed, you can get a coverage report after running the ONNX backend test to see how well your backend is doing: ``` ---------- onnx coverage: ---------- Operators (passed/loaded/total): 21/21/70 ------------------------------------ ╒════════════════════╤════════════════════╕ │ Operator │ Attributes │ │ │ (name: #values) │ ╞════════════════════â•Ē════════════════════╡ │ Slice │ axes: 2 │ │ │ ends: 3 │ │ │ starts: 3 │ ├────────────────────â”ŧ────────────────────┤ │ Constant │ value: 1 │ ├────────────────────â”ŧ────────────────────┤ │ Concat │ axis: 0 │ ├────────────────────â”ŧ────────────────────┤ │ Conv │ group: 6 │ │ │ kernel_shape: 5 │ │ │ pads: 4 │ │ │ strides: 3 │ │ │ auto_pad: 0 │ │ │ dilations: 0 │ ├────────────────────â”ŧ────────────────────┤ │ Reshape │ shape: 9 │ ├────────────────────â”ŧ────────────────────┤ │ BatchNormalization │ consumed_inputs: 1 │ │ │ epsilon: 2 │ │ │ is_test: 1 │ │ │ momentum: 0 │ │ │ spatial: 0 │ ├────────────────────â”ŧ────────────────────┤ │ Dropout │ is_test: 1 │ │ │ ratio: 2 │ ├────────────────────â”ŧ────────────────────┤ │ MaxPool │ kernel_shape: 2 │ │ │ pads: 3 │ │ │ strides: 2 │ │ │ auto_pad: 0 │ │ │ dilations: 0 │ ├────────────────────â”ŧ────────────────────┤ │ Transpose │ perm: 1 │ ├────────────────────â”ŧ────────────────────┤ │ MatMul │ No attributes │ ├────────────────────â”ŧ────────────────────┤ │ Relu │ No attributes │ ├────────────────────â”ŧ────────────────────┤ │ LRN │ alpha: 2 │ │ │ beta: 1 │ │ │ bias: 2 │ │ │ size: 1 │ ├────────────────────â”ŧ────────────────────┤ │ Add │ axis: 1 │ │ │ broadcast: 1 │ ├────────────────────â”ŧ────────────────────┤ │ Abs │ No attributes │ ├────────────────────â”ŧ────────────────────┤ │ Pad │ mode: 3 │ │ │ paddings: 2 │ │ │ value: 1 │ ├────────────────────â”ŧ────────────────────┤ │ Softmax │ axis: 0 │ ├────────────────────â”ŧ────────────────────┤ │ GlobalAveragePool │ No attributes │ ├────────────────────â”ŧ────────────────────┤ │ Mul │ axis: 1 │ │ │ broadcast: 1 │ ├────────────────────â”ŧ────────────────────┤ │ Sum │ No attributes │ ├────────────────────â”ŧ────────────────────┤ │ Gemm │ broadcast: 1 │ │ │ transB: 1 │ │ │ alpha: 0 │ │ │ beta: 0 │ │ │ transA: 0 │ ├────────────────────â”ŧ────────────────────┤ │ AveragePool │ kernel_shape: 3 │ │ │ pads: 3 │ │ │ strides: 2 │ │ │ auto_pad: 0 │ ╘════════════════════╧════════════════════╛ ``` The numbers in the line `Operators (passed/loaded/total): 21/21/70` indicate 21 operators covered in all test cases of your backend have passed, 21 operators were covered in all test cases of the ONNX backend test, and ONNX has a total of 70 operators. onnx-onnx-bca0315/docs/ManagingExperimentalOps.md000066400000000000000000000054611511334557700221300ustar00rootroot00000000000000 # Managing Experimental Operators ## Deprecated Experimental Operators The following experimental operators were deprecated and removed from ONNX. They should be removed from models, either substituted with newer superseding operators or decomposed into functionally equivalent operators: Old operator |New Operator --------------------|-------------------------- `ATen` |NA `Affine` |`Add(Mul(X, alpha), beta)` `ConstantFill` |`ConstantOfShape` `Crop` |`Slice-1` `DynamicSlice` |`Slice-10` `GRUUnit` |NA `GivenTensorFill` |`Const` or `ConstantOfShape` `ImageScaler` |`Add(Mul(X, scale), Unsqueeze(bias, axes=[0, 2, 3]))` `ParametricSoftplus`|`Mul(alpha, Softplus(Mul(beta, X)))` `Scale` |`Mul(X, scale)` `ScaledTanh` |`Mul(Tanh(Mul(X, beta)), alpha)` ## Adding Experimental Operators [Deprecated - as of v1.5 experimental ops are no longer supported] The experimental flag in ONNX operator definitions indicates that a customer of ONNX may not be able to take a long term dependency on that op. Ops in the ONNX namespace (ai.onnx) in the _main_ branch, whether experimental or not, go through the regular review process. Experimental ops that are being worked on that do not have consensus yet can be managed in one of 2 ways: 1. Use a fork or branch – what you do in the fork or branch is entirely up to you. When you are ready, you can submit a PR using the normal process. This is the recommended way. 2. If a fork/branch is not workable (for example due to complexity of mapping different branches between multiple repos), put the experimental ops in a custom namespace in the main branch. The specific process for this is: * Submit an Issue with a proposal explaining the motivation and plan. It does not need to include detailed technical design. Issues will be tagged as "experimental op". * Reviewers will generally approve by default unless the proposal directly conflicts with existing ops or somehow goes against general ONNX strategy. Approval is indicated by adding the "experiment approved" tag. * The approval is good for 3 months, but can be renewed if needed. * Experimental ops should be submitted in a PR in a custom namespace that is the name of the proposal, i.e. “proposal.controlflow”. The name should be descriptive rather than a company or entity name. These PRs will be approved by default as long as the parent proposal is approved and active. * Once experimentation is done, the ops can be submitted for addition to the ONNX namespace via the regular process. The owner can also choose to end the experiment without promoting the ops. * Either way, the custom namespace is deleted once experimentation is complete or when the approval expires. onnx-onnx-bca0315/docs/MetadataProps.md000066400000000000000000000051271511334557700201120ustar00rootroot00000000000000 # Metadata In addition to the core metadata recommendations listed in the [extensibility documentation](IR.md#optional-metadata) there is additional experimental metadata to help provide information for model inputs and outputs. This metadata applies to all input and output tensors of a given category. The first such category we define is: `Image`. ## Motivation The motivation of such a mechanism is to allow model authors to convey to model consumers enough information for them to consume the model. In the case of images there are many option for providing valid image data. However a model which consumes images was trained with a particular set of these options which must be used during inferencing. The goal is this proposal is to provide enough metadata that the model consumer can perform their own featurization prior to running the model and provide a compatible input or retrieve an output and know what its format is. ## Image Category Definition For every tensor in this model that uses [Type Denotation](TypeDenotation.md) to declare itself an `IMAGE`, you SHOULD provide metadata to assist the model consumer. Note that any metadata provided using this mechanism is global to ALL types with the accompanying denotation. Keys and values are case insensitive. Specifically, we define here the following set image metadata: |Key|Value|Description| |-----|----|-----------| |`Image.BitmapPixelFormat`|__string__|Specifies the format of pixel data. Each enumeration value defines a channel ordering and bit depth. Possible values:
  • `Gray8`: 1 channel image, the pixel data is 8 bpp grayscale.
  • `Rgb8`: 3 channel image, channel order is RGB, pixel data is 8bpp (No alpha)
  • `Bgr8`: 3 channel image, channel order is BGR, pixel data is 8bpp (No alpha)
  • `Rgba8`: 4 channel image, channel order is RGBA, pixel data is 8bpp (Straight alpha)
  • `Bgra8`: 4 channel image, channel order is BGRA, pixel data is 8bpp (Straight alpha)
| |`Image.ColorSpaceGamma`|__string__|Specifies the gamma color space used. Possible values:
  • `Linear`: Linear color space, gamma == 1.0
  • `SRGB`: sRGB color space, gamma == 2.2
| |`Image.NominalPixelRange`|__string__|Specifies the range that pixel values are stored. Possible values:
  • `NominalRange_0_255`: [0...255] for 8bpp samples
  • `Normalized_0_1`: [0...1] pixel data is stored normalized
  • `Normalized_1_1`: [-1...1] pixel data is stored normalized
  • `NominalRange_16_235`: [16...235] for 8bpp samples
| onnx-onnx-bca0315/docs/ONNXTypes.md000066400000000000000000000064301511334557700171530ustar00rootroot00000000000000 # ONNX Types ## Optional Type An optional type represents a reference to either an element (could be Tensor, Sequence, Map, or Sparse Tensor) or a null value. The optional type appears in model inputs, outputs, as well as intermediate values. ### Use-cases Optional type enables users to represent more dynamic typing scenarios in ONNX. Similar to Optional[X] type hint in Python typing which is equivalent to Union[None, X], Optional types in ONNX may reference a single element, or null. ### Examples in PyTorch Optional type only appears in TorchScript graphs generated by jit script compiler. Scripting a model captures dynamic types where an optional value can be assigned either None or a value. - Example 1 class Model(torch.nn.Module): def forward(self, x, y:Optional[Tensor]=None): if y is not None: return x + y return x Corresponding TorchScript graph: Graph( %self : __torch__.Model, %x.1 : Tensor, %y.1 : Tensor? ): %11 : int = prim::Constant[value=1]() %4 : None = prim::Constant() %5 : bool = aten::__isnot__(%y.1, %4) %6 : Tensor = prim::If(%5) block0(): %y.4 : Tensor = prim::unchecked_cast(%y.1) %12 : Tensor = aten::add(%x.1, %y.4, %11) -> (%12) block1(): -> (%x.1) return (%6) ONNX graph: Graph( %x.1 : Float(2, 3), %y.1 : Float(2, 3) ): %2 : Bool(1) = onnx::OptionalHasElement(%y.1) %5 : Float(2, 3) = onnx::If(%2) block0(): %3 : Float(2, 3) = onnx::OptionalGetElement(%y.1) %4 : Float(2, 3) = onnx::Add(%x.1, %3) -> (%4) block1(): %x.2 : Float(2, 3) = onnx::Identity(%x.1) -> (%x.2) return (%5) - Example 2 class Model(torch.nn.Module): def forward( self, src_tokens, return_all_hiddens=torch.tensor([False]), ): encoder_states: Optional[Tensor] = None if return_all_hiddens: encoder_states = src_tokens return src_tokens, encoder_states Corresponding TorchScript graph: Graph( %src_tokens.1 : Float(3, 2, 4,), %return_all_hiddens.1 : Bool(1) ): %3 : None = prim::Constant() %encoder_states : Tensor? = prim::If(%return_all_hiddens.1) block0(): -> (%src_tokens.1) block1(): -> (%3) return (%src_tokens.1, %encoder_states) ONNX graph: Graph( %src_tokens.1 : Float(3, 2, 4), %return_all_hiddens.1 : Bool(1) ): %2 : Float(3, 2, 4) = onnx::Optional[type=tensor(float)]() %3 : Float(3, 2, 4) = onnx::If(%return_all_hiddens.1) block0(): -> (%src_tokens.1) block1(): -> (%2) return (%3) onnx-onnx-bca0315/docs/OnnxBackendTest.md000066400000000000000000000046621511334557700204030ustar00rootroot00000000000000 # ONNX Backend Test ## What is ONNX Backend Test ONNX Backend Test is a test suite that each ONNX backend should run to verify whether it fulfills ONNX's standard. It serves both as a verification tool for backend implementations and one of the two ways to define each operator's expected behavior (the other way is to add it to the documentation). There are two types of tests in this suite – Node Tests and Model Tests: - **Node Tests** verify whether a backend is performing the correct computation, having the expected behavior of handling various attributes for each individual operator. In each test case, the backend will be given a node with some input, and the returned output will be compared with an expected output. - **Model Tests** verify the backend at the model level. The test cases are similar to those of Node Tests', but instead of a node, the backend will be given an ONNX model. ## Contributing As ONNX aims to become the spec of deep learning models format, it's important to ensure that there is no ambiguity in each ONNX operator's definition; adding more test cases is the only way to enforce this. Node Tests are created as Python/Numpy code in [onnx/backend/test/case/node](/onnx/backend/test/case/node), and then exported to protobuf files to [onnx/backend/test/data/node](/onnx/backend/test/data/node) as the source of truth by invoking the shell command `backend-test-tools generate-data`. Test cases of each operator lives in one standalone file, e.g. for the operator [Add](/docs/Operators.md#Add), its test cases are in [add.py](/onnx/backend/test/case/node/add.py), and each `expect(...)` statement in the code corresponds to one test case. The source code of all `export.*` functions will be also embedded as example code snippets in the [Operators documentation page](/docs/Operators.md). You are contributing to both the test and the documentation! For Model Tests, since each model protobuf file can be large in size, we don't place the file directly in the repo. Rather, we upload them to the cloud, and download them on demand when running the tests. Each test case consists of one model definition protobuf file, and several pairs of input and output files. Adding a new test case involves some manual work from admins (like uploading the files to the cloud), so if you have an ONNX model that you would like to contribute, please contact us. onnx-onnx-bca0315/docs/OnnxReleases.md000066400000000000000000000274431511334557700177610ustar00rootroot00000000000000 # ONNX Releases The ONNX project, going forward, will plan to release roughly on a four month cadence. We follow the [Semver](https://semver.org/) versioning approach and will make decisions as a community on a release by release basis on whether to do a major or minor release. ## Preparation * Reach out to the SIG Arch/Infra leads to confirm whether the required status checks for the release branches are still valid and up to date, and whether any rely on outdated hardcoded runner image versions that may need updatingCheck whether the 'required checks' for the release branches are still up to date or need to be adjusted: 'Branches' -> 'Branch protection rules' * Determine version (X.Y.Z) for the new release * Discuss in Slack channel for Releases (https://lfaifoundation.slack.com/archives/C018VGGJUGK) * For (v.X.Y.Z), if release is to be 1.16.0, * X=1, Y=16, Z=0 * The new branch will be `rel-1.16.0` * Branch protections rules are automatically applied to branches following this format. * The new tag will be `v1.16.0` * Create new page for the release in [Release logistics wiki](https://github.com/onnx/onnx/wiki) * Before creating a release branch, it is highly recommended to have in mind to compile **preliminary release notes** — ideally maintained in a shared location such as the **release wiki page**. These notes should include a clear summary of the **new features**, a list of **bug fixes**, any **known issues**, and especially any **deprecations or removals**, with links to relevant tickets or documentation where applicable. Having this information ready ensures that the team can confidently and promptly create a `rc1` (release candidate 1) immediately after the branch is cut, without delays. Acting quickly at this stage also helps to **reduce the need for parallel work on both the main and release branches**, minimizing merge conflicts, duplicated effort, and coordination overhead. This practice supports a smoother, more transparent release process. * To generate good release notes, it is helpful if pull requests have meaningful names and corresponding labels. Labels can also be added retrospectively to PRs that have already been merged. * The labels used can be found [here](https://github.com/onnx/onnx/blob/main/.github/release.yml) * The preliminary release notes one gets if one drafts a release on GitHub. ## Create Release Branch * In `main` branch, before creating the release branch: 1. Bump the `LAST_RELEASE_VERSION` in [version.h](/onnx/common/version.h). * Set to X.Y.Z, which is same as the release branch you are currently creating. * After the release branch is cut, `VERSION_NUMBER` in `main` will be increased to the next future version. 1. Make sure the release version, IR version, ai.onnx opset version, ai.onnx.ml opset version, and ai.onnx.training opset version are correct for the new release in [ONNX proto files](/onnx/onnx.in.proto), [Versioning.md](Versioning.md), [schema.h](/onnx/defs/schema.h), [helper.py](/onnx/helper.py), and [helper_test.py](/onnx/test/helper_test.py). * Create a release branch 1. Click "New branch" from [branches](https://github.com/onnx/onnx/branches) and choose `main` as Source. 1. Make sure all tests pass on the new branch. * After cutting the release branch: 1. Create PR to set [VERSION_NUMBER](/VERSION_NUMBER) file in `main` to the next future release, `X.Y+1.0`. 1. Create PR to set `VERSION_NUMBER` file in the new release's branch to `X.Y.Zrc1`. * For example the first release candidate for 1.16.0 would be `1.16.0rc1` 1. Bump opset version for ai.onnx domain in `onnx/defs/operator_sets.h` and `onnx/defs/schema.h` for use by future operator additions and changes. * For example, this [demo PR](https://github.com/onnx/onnx/pull/6001). ## Upload release candidate to TestPyPI without offline steps (starting with onnx version 1.19) * Go to "Actions" -> select ["Create Releases"](https://github.com/onnx/onnx/actions/workflows/create_release.yml) -> Push the button "Run workflow" with the following config: RunWorkflow RC-Candidates * Published to https://test.pypi.org/ * Build-mode: Release * This button triggers the build of the different OS create_releases_overview_jobs * All artifacts of the single runs could be found associated to the job create_releases_artifact_overview * Before the final merge, it must be confirmed manually via the set up deployment environments. ## Package verification **Partner Validation** * User should install the rc-packages with *pip install --no-deps -i https://test.pypi.org/simple/ onnx* (and manually install its dependencies so they are not obtained from test-pypi) * Test with onnxruntime package: * Run the test script from [test_with_ort.py](/onnx/test/test_with_ort.py) with installed onnxruntime package. * The scripts tests ONNX functions like `load`, `checker.check_model`, and `shape_inference.infer_shapes`, with onnxruntime functions like `InferenceSession` and `InferenceSession.run` on certain example ONNX model. * Open Issues for external repos: * Create GitHub issues in converters' repos to provide them the package links and oppuruntity to test the release before it goes public. * https://github.com/microsoft/onnxruntime * Example: https://github.com/microsoft/onnxruntime/issues/19783 * Note: [How_To_Update_ONNX_Dev_Notes](https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md) exists in their repo documenting how to pull in new ONNX releases. * https://github.com/pytorch/pytorch * Example: https://github.com/pytorch/pytorch/issues/121258 * https://github.com/onnx/tensorflow-onnx * Example: https://github.com/onnx/tensorflow-onnx/issues/2310 * https://github.com/onnx/onnx-tensorrt * Example: https://github.com/onnx/onnx-tensorrt/issues/956 * https://github.com/onnx/sklearn-onnx * Example: https://github.com/onnx/sklearn-onnx/issues/1079 * https://github.com/microsoft/onnxconverter-common * Example: https://github.com/microsoft/onnxconverter-common/issues/277 * https://github.com/onnx/onnxmltools * Example: https://github.com/onnx/onnxmltools/issues/685 * https://github.com/Quantco/spox * https://github.com/conda-forge/onnx-feedstock * If issues are found, the bugs are to be fixed in the onnx `main` branch and then cherry-picked into the release branch. * Follow up with reporter to ensure issues are resolved (and validated in a new rc) or deferred to a new release. # Official Release Validation steps must be completed before this point! This is the point of new return. * git tags should not be changed once published * Once pushed to PyPI there is no way to update the release. A new release must be made instead ## Set final version number * Create PR to remove "`rcX`" suffix from `VERSION_NUMBER` file in the new release's branch. ## Create release tag * [Draft a release](https://github.com/onnx/onnx/releases/new) based on the release branch: * DO NOT click `Publish release` until you are sure no more changes are needed. * Use `Save Draft` if need to save and update more later. * Publishing will create the new git tag * Tag: See top of [Preparation](#Preparation) for tag to create. * Target: The release branch that was just cut * Previous tag: Select the previous release. * Write: * Use [previous releases](https://github.com/onnx/onnx/releases) as a template * Use information from [Release logistics wiki](https://github.com/onnx/onnx/wiki) which should have been created prior to branch cut. * Add any additional commits that merged into the release in since wiki was written. * .tar.gz and .zip will be auto-generated after publishing the release. ## Upload to Official PyPI * Starting with the release of 1.19, the final release will also be pushed to pypi via Github “Action" -> "Create releases" (see above). Use the following config for official release: RunWorkflow_Final ### NOTES: * Once the packages are uploaded to PyPI, **you cannot overwrite it on the same PyPI instance**. * Please make sure everything is good on TestPyPI before uploading to PyPI** * PyPI has separate logins, passwords, and API tokens from TestPyPI but the process is the same. An ONNX PyPI owner will need to grant access, etc. ## After PyPI Release **Announce** * Slack: * Post in the [onnx-release](https://lfaifoundation.slack.com/archives/C018VGGJUGK) and [onnx-general](https://lfaifoundation.slack.com/archives/C016UBNDBL2) channels. * Notify ONNX partners via email lists: * onnxdiscussions@service.microsoft.com * onnxconverterteam@service.microsoft.com * onnxruntimeteam@microsoft.com * [ONNX News](https://onnx.ai/news.html) Post * Update [news.json](https://github.com/onnx/onnx.github.io/blob/main/js/news.json), see [example news.json PR](https://github.com/onnx/onnx.github.io/pull/197) **Update conda-forge package with the new ONNX version** Conda builds of ONNX are done via [conda-forge/onnx-feedstock](https://github.com/conda-forge/onnx-feedstock), which runs infrastructure for building packages and uploading them to conda-forge. * A PR should be created automatically by `regro-cf-autotick-bot` a few hours after the release is available at https://github.com/onnx/onnx/releases. * If the automatic PR has build failures: 1. Make a personal fork of conda-forge/onnx-feedstock 1. Create a personal branch based on the automated PR branch 1. Resolve the build issue 1. Submit a replacement PR based on your branch * Example: https://github.com/conda-forge/onnx-feedstock/pull/116 * If the automatic PR is not created, you need to submit a PR manually * Example: https://github.com/conda-forge/onnx-feedstock/pull/50 * Note: Use the sha256 hash (`sha256sum onnx-X.Y.Z.tar.gz`) of the release's tar.gz file from https://github.com/onnx/onnx/releases. **Merge into main branch** * Check which changes to the release branch are also relevant for main: * If urgent changes were made directly into the release branch, merge the release branch back into main branch. * If all PRs merged into the release branch (after it was cut) were cherry-picks from main, the merge PR will show as empty and this step is not needed. **Remove old onnx-weekly packages on PyPI** * Remove all [onnx-weekly packages](https://pypi.org/project/onnx-weekly/#history) from PyPI for the just released version to save space. * Steps: * Go to [PyPI onnx-weekly/releases](https://pypi.org/manage/project/onnx-weekly/releases/) * This is a separate project than the onnx releases so you may need to request access from an owner * Click target package -> Options -> Delete. **Remove old release-candidate packages on PyPI** * Remove [onnx-release-candidate packages](https://test.pypi.org/project/onnx/#history) from PyPI up to at least the time specified by the previous release version to save space. * Steps: * Go to [PyPI onnx-weekly/releases](https://test.pypi.org/manage/project/onnx/releases/) * This is a separate project than the onnx releases so you may need to request access from an owner * Click target package -> Options -> Delete. onnx-onnx-bca0315/docs/OpConventions.md000066400000000000000000000014611511334557700201470ustar00rootroot00000000000000 # Operator Conventions To maintain consistency in operator signatures, we use the following principles: - All attribute names should be lower case and use underscores when it helps with readability - Any input/output represented by a single letter is capitalized (i.e. X) - Any input/output represented by a full word or multiple words is all lower case and uses underscores when it helps with readability - Any input/output representing a bias tensor will utilize the name "B" - Any input/output representing a weight tensor will utilize the name “W” - “axes” is used when an input, output or attribute is representing multiple axes - “axis” is used when an input, output or attribute is representing a single axis onnx-onnx-bca0315/docs/Operators-ml.md000066400000000000000000001301301511334557700177230ustar00rootroot00000000000000 ## Operator Schemas *This file is automatically generated from the [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py). Do not modify directly and instead edit operator definitions.* For an operator input/output's differentiability, it can be differentiable, non-differentiable, or undefined. If a variable's differentiability is not specified, that variable has undefined differentiability. ### ai.onnx.ml |**Operator**|**Since version**|| |-|-|-| |ai.onnx.ml.ArrayFeatureExtractor|1| |ai.onnx.ml.Binarizer|1| |ai.onnx.ml.CastMap|1| |ai.onnx.ml.CategoryMapper|1| |ai.onnx.ml.DictVectorizer|1| |ai.onnx.ml.FeatureVectorizer|1| |ai.onnx.ml.Imputer|1| |ai.onnx.ml.LabelEncoder|4, 2, 1| |ai.onnx.ml.LinearClassifier|1| |ai.onnx.ml.LinearRegressor|1| |ai.onnx.ml.Normalizer|1| |ai.onnx.ml.OneHotEncoder|1| |ai.onnx.ml.SVMClassifier|1| |ai.onnx.ml.SVMRegressor|1| |ai.onnx.ml.Scaler|1| |ai.onnx.ml.TreeEnsemble|5| |ai.onnx.ml.TreeEnsembleClassifier (deprecated)|5, 3, 1| |ai.onnx.ml.TreeEnsembleRegressor (deprecated)|5, 3, 1| |ai.onnx.ml.ZipMap|1| ## ai.onnx.ml ### **ai.onnx.ml.ArrayFeatureExtractor** Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Inputs
X : T
Data to be selected
Y : tensor(int64)
The indices, based on 0 as the first index of any dimension.
#### Outputs
Z : T
Selected output data as an array
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string)
The input must be a tensor of a numeric type or string. The output will be of the same tensor type.
#### Examples
arrayfeatureextractor ```python node = onnx.helper.make_node( "ArrayFeatureExtractor", inputs=["x", "y"], outputs=["z"], domain="ai.onnx.ml", ) x = np.arange(12).reshape((3, 4)).astype(np.float32) y = np.array([0, 1], dtype=np.int64) z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T expect( node, inputs=[x, y], outputs=[z], name="test_ai_onnx_ml_array_feature_extractor", ) ```
### **ai.onnx.ml.Binarizer** Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
threshold : float (default is 0.0)
Values greater than this are mapped to 1, others to 0.
#### Inputs
X : T
Data to be binarized
#### Outputs
Y : T
Binarized output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type. The output will be of the same tensor type.
#### Examples
binarizer ```python threshold = 1.0 node = onnx.helper.make_node( "Binarizer", inputs=["X"], outputs=["Y"], threshold=threshold, domain="ai.onnx.ml", ) x = np.random.randn(3, 4, 5).astype(np.float32) y = compute_binarizer(x, threshold)[0] expect(node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_binarizer") ```
### **ai.onnx.ml.CastMap** Converts a map to a tensor.
The map key must be an int64 and the values will be ordered in ascending order based on this key.
The operator supports dense packing or sparse packing. If using sparse packing, the key cannot exceed the max_map-1 value. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cast_to : string (default is TO_FLOAT)
A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.
map_form : string (default is DENSE)
Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.
max_map : int (default is 1)
If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.
#### Inputs
X : T1
The input map that is to be cast to a tensor
#### Outputs
Y : T2
A tensor representing the same data as the input map, ordered by their keys
#### Type Constraints
T1 : map(int64, string), map(int64, float)
The input must be an integer map to either string or float.
T2 : tensor(string), tensor(float), tensor(int64)
The output is a 1-D tensor of string, float, or integer.
### **ai.onnx.ml.CategoryMapper** Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings, with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cats_int64s : list of ints
The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.
cats_strings : list of strings
The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence
default_int64 : int (default is -1)
An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
default_string : string (default is _Unused)
A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
#### Inputs
X : T1
Input data
#### Outputs
Y : T2
Output data. If strings are input, the output values are integers, and vice versa.
#### Type Constraints
T1 : tensor(string), tensor(int64)
The input must be a tensor of strings or integers, either [N,C] or [C].
T2 : tensor(string), tensor(int64)
The output is a tensor of strings or integers. Its shape will be the same as the input shape.
### **ai.onnx.ml.DictVectorizer** Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.
For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``, then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
int64_vocabulary : list of ints
An integer vocabulary array.
One and only one of the vocabularies must be defined.
string_vocabulary : list of strings
A string vocabulary array.
One and only one of the vocabularies must be defined.
#### Inputs
X : T1
A dictionary.
#### Outputs
Y : T2
A 1-D tensor holding values from the input dictionary.
#### Type Constraints
T1 : map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double)
The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.
T2 : tensor(int64), tensor(float), tensor(double), tensor(string)
The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.
### **ai.onnx.ml.FeatureVectorizer** Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C]. Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
inputdimensions : list of ints
The size of each input in the input list
#### Inputs (1 - ∞)
X (variadic) : T1
An ordered collection of tensors, all with the same element type.
#### Outputs
Y : tensor(float)
The output array, elements ordered as the inputs.
#### Type Constraints
T1 : tensor(int32), tensor(int64), tensor(float), tensor(double)
The input type must be a tensor of a numeric type.
### **ai.onnx.ml.Imputer** Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor holds floats, integers if the input tensor holds integers. The imputed values must all fit within the width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined, which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
imputed_value_floats : list of floats
Value(s) to change to
imputed_value_int64s : list of ints
Value(s) to change to.
replaced_value_float : float (default is 0.0)
A value that needs replacing.
replaced_value_int64 : int (default is 0)
A value that needs replacing.
#### Inputs
X : T
Data to be processed.
#### Outputs
Y : T
Imputed output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.
### **ai.onnx.ml.LabelEncoder** Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. Note that the 'keys_*' and 'values_*' attributes must have the same length. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value. The type of the 'default_*' attribute must match the 'values_*' attribute chosen.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
Float keys with value 'NaN' match any input 'NaN' value regardless of bit value. If a key is repeated, the last key takes precedence. #### Version This version of the operator has been available since version 4 of the 'ai.onnx.ml' operator set. Other versions of this operator: 1, 2 #### Attributes
default_float : float (default is -0.0)
A float.
default_int64 : int (default is -1)
An integer.
default_string : string (default is _Unused)
A string.
default_tensor : tensor
A default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.
keys_floats : list of floats
A list of floats.
keys_int64s : list of ints
A list of ints.
keys_strings : list of strings
A list of strings.
keys_tensor : tensor
Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.
values_floats : list of floats
A list of floats.
values_int64s : list of ints
A list of ints.
values_strings : list of strings
A list of strings.
values_tensor : tensor
Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.
#### Inputs
X : T1
Input data. It must have the same element type as the keys_* attribute set.
#### Outputs
Y : T2
Output data. This tensor's element type is based on the values_* attribute set.
#### Type Constraints
T1 : tensor(string), tensor(int64), tensor(float), tensor(int32), tensor(int16), tensor(double)
The input type is a tensor of any shape.
T2 : tensor(string), tensor(int64), tensor(float), tensor(int32), tensor(int16), tensor(double)
Output type is determined by the specified 'values_*' attribute.
#### Examples
string_int_label_encoder ```python node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], default_int64=42, ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, -1, 2, -1]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int_no_default", ) ```
tensor_based_label_encoder ```python tensor_keys = make_tensor( "keys_tensor", onnx.TensorProto.STRING, (3,), ["a", "b", "c"] ) repeated_string_keys = ["a", "b", "c"] x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int16) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_tensor=tensor_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_mapping", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=repeated_string_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_value_only_mapping", ) ```
### **ai.onnx.ml.LinearClassifier** Linear classifier #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_ints : list of ints
Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.
classlabels_strings : list of strings
Class labels when using string labels. One and only one 'classlabels' attribute must be defined.
coefficients : list of floats (required)
A collection of weights of the model(s).
intercepts : list of floats
A collection of intercepts.
multi_class : int (default is 0)
Indicates whether to do OvR or multinomial (0=OvR is the default).
post_transform : string (default is NONE)
Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
#### Inputs
X : T1
Data to be classified.
#### Outputs
Y : T2
Classification outputs (one class per example).
Z : tensor(float)
Classification scores ([N,E] - one score for each class and example
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]
T2 : tensor(string), tensor(int64)
The output will be a tensor of strings or integers.
### **ai.onnx.ml.LinearRegressor** Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
coefficients : list of floats
Weights of the model(s).
intercepts : list of floats
Weights of the intercepts, if used.
post_transform : string (default is NONE)
Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
targets : int (default is 1)
The total number of regression targets, 1 if not defined.
#### Inputs
X : T
Data to be regressed.
#### Outputs
Y : tensor(float)
Regression outputs (one per target, per example).
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.Normalizer** Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':

Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
norm : string (default is MAX)
One of 'MAX,' 'L1,' 'L2'
#### Inputs
X : T
Data to be encoded, a tensor of shape [N,C] or [C]
#### Outputs
Y : tensor(float)
Encoded output data
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.OneHotEncoder** Replace each input element with an array of ones and zeros, where a single one is placed at the index of the category that was passed in. The total category count will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8, the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast to integers and the cats_int64s category list will be used for the lookups. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
cats_int64s : list of ints
List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.
cats_strings : list of strings
List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.
zeros : int (default is 1)
If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.
#### Inputs
X : T
Data to be encoded.
#### Outputs
Y : tensor(float)
Encoded output data, having one more dimension than X.
#### Type Constraints
T : tensor(string), tensor(int64), tensor(int32), tensor(float), tensor(double)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.SVMClassifier** Support Vector Machine classifier #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_ints : list of ints
Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.
coefficients : list of floats
kernel_params : list of floats
List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
kernel_type : string (default is LINEAR)
The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
prob_a : list of floats
First set of probability coefficients.
prob_b : list of floats
Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.
rho : list of floats
support_vectors : list of floats
vectors_per_class : list of ints
#### Inputs
X : T1
Data to be classified.
#### Outputs
Y : T2
Classification outputs (one class per example).
Z : tensor(float)
Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.
#### Type Constraints
T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type, either [C] or [N,C].
T2 : tensor(string), tensor(int64)
The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the batch size of the input.
### **ai.onnx.ml.SVMRegressor** Support Vector Machine regression prediction and one-class SVM anomaly detection. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
coefficients : list of floats
Support vector coefficients.
kernel_params : list of floats
List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
kernel_type : string (default is LINEAR)
The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
n_supports : int (default is 0)
The number of support vectors.
one_class : int (default is 0)
Flag indicating whether the regression is a one-class SVM or not.
post_transform : string (default is NONE)
Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'
rho : list of floats
support_vectors : list of floats
Chosen support vectors
#### Inputs
X : T
Data to be regressed.
#### Outputs
Y : tensor(float)
Regression outputs (one score per target per example).
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input type must be a tensor of a numeric type, either [C] or [N,C].
### **ai.onnx.ml.Scaler** Rescale input data, for example to standardize features by removing the mean and scaling to unit variance. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
offset : list of floats
First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
scale : list of floats
Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'
#### Inputs
X : T
Data to be scaled.
#### Outputs
Y : tensor(float)
Scaled output data.
#### Type Constraints
T : tensor(float), tensor(double), tensor(int64), tensor(int32)
The input must be a tensor of a numeric type.
### **ai.onnx.ml.TreeEnsemble** Tree Ensemble operator. Returns the regressed values for each input in a batch. Inputs have dimensions `[N, F]` where `N` is the input batch size and `F` is the number of input features. Outputs have dimensions `[N, num_targets]` where `N` is the batch size and `num_targets` is the number of targets, which is a configurable attribute. The encoding of this attribute is split along interior nodes and the leaves of the trees. Notably, attributes with the prefix `nodes_*` are associated with interior nodes, and attributes with the prefix `leaf_*` are associated with leaves. The attributes `nodes_*` must all have the same length and encode a sequence of tuples, as defined by taking all the `nodes_*` fields at a given position. All fields prefixed with `leaf_*` represent tree leaves, and similarly define tuples of leaves and must have identical length. This operator can be used to implement both the previous `TreeEnsembleRegressor` and `TreeEnsembleClassifier` nodes. The `TreeEnsembleRegressor` node maps directly to this node and requires changing how the nodes are represented. The `TreeEnsembleClassifier` node can be implemented by adding a `ArgMax` node after this node to determine the top class. To encode class labels, a `LabelEncoder` or `GatherND` operator may be used. #### Version This version of the operator has been available since version 5 of the 'ai.onnx.ml' operator set. #### Attributes
aggregate_function : int (default is 1)
Defines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)
leaf_targetids : list of ints (required)
The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).
leaf_weights : tensor (required)
The weight for each leaf.
membership_values : tensor
Members to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.
n_targets : int
The total number of targets.
nodes_falseleafs : list of ints (required)
1 if false branch is leaf for each node and 0 if an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry
nodes_falsenodeids : list of ints (required)
If `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.
nodes_featureids : list of ints (required)
Feature id for each node.
nodes_hitrates : tensor
Popularity of each node, used for performance and may be omitted.
nodes_missing_value_tracks_true : list of ints
For each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.
nodes_modes : tensor (required)
The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.
nodes_splits : tensor (required)
Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.
nodes_trueleafs : list of ints (required)
1 if true branch is leaf for each node and 0 an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry
nodes_truenodeids : list of ints (required)
If `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.
post_transform : int (default is 0)
Indicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)
tree_roots : list of ints (required)
Index into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.
#### Inputs
X : T
Input of shape [Batch Size, Number of Features]
#### Outputs
Y : T
Output of shape [Batch Size, Number of targets]
#### Type Constraints
T : tensor(float), tensor(double), tensor(float16)
The input type must be a tensor of a numeric type.
#### Examples
tree_ensemble_set_membership ```python node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=4, aggregate_function=1, membership_values=make_tensor( "membership_values", onnx.TensorProto.FLOAT, (8,), [1.2, 3.7, 8, 9, np.nan, 12, 7, np.nan], ), nodes_missing_value_tracks_true=None, nodes_hitrates=None, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 6, 6], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.FLOAT, (3,), np.array([11, 232344.0, np.nan], dtype=np.float32), ), nodes_trueleafs=[0, 1, 1], nodes_truenodeids=[1, 0, 1], nodes_falseleafs=[1, 0, 1], nodes_falsenodeids=[2, 2, 3], leaf_targetids=[0, 1, 2, 3], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.FLOAT, (4,), [1, 10, 1000, 100] ), ) x = np.array([1.2, 3.4, -0.12, np.nan, 12, 7], np.float32).reshape(-1, 1) expected = np.array( [ [1, 0, 0, 0], [0, 0, 0, 100], [0, 0, 0, 100], [0, 0, 1000, 0], [0, 0, 1000, 0], [0, 10, 0, 0], ], dtype=np.float32, ) expect( node, inputs=[x], outputs=[expected], name="test_ai_onnx_ml_tree_ensemble_set_membership", ) ```
tree_ensemble_single_tree ```python node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=2, membership_values=None, nodes_missing_value_tracks_true=None, nodes_hitrates=None, aggregate_function=1, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 0, 0], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.DOUBLE, (3,), np.array([3.14, 1.2, 4.2], dtype=np.float64), ), nodes_truenodeids=[1, 0, 1], nodes_trueleafs=[0, 1, 1], nodes_falsenodeids=[2, 2, 3], nodes_falseleafs=[0, 1, 1], leaf_targetids=[0, 1, 0, 1], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.DOUBLE, (4,), np.array([5.23, 12.12, -12.23, 7.21], dtype=np.float64), ), ) x = np.array([1.2, 3.4, -0.12, 1.66, 4.14, 1.77], np.float64).reshape(3, 2) y = np.array([[5.23, 0], [5.23, 0], [0, 12.12]], dtype=np.float64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_tree_ensemble_single_tree", ) ```
### **ai.onnx.ml.TreeEnsembleClassifier** (deprecated) This operator is DEPRECATED. Please use TreeEnsemble with provides similar functionality. In order to determine the top class, the ArgMax node can be applied to the output of TreeEnsemble. To encode class labels, use a LabelEncoder operator. Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. #### Version This version of the operator has been deprecated since version 5 of the 'ai.onnx.ml' operator set. Other versions of this operator: 1, 3 ### **ai.onnx.ml.TreeEnsembleRegressor** (deprecated) This operator is DEPRECATED. Please use TreeEnsemble instead which provides the same functionality.
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF #### Version This version of the operator has been deprecated since version 5 of the 'ai.onnx.ml' operator set. Other versions of this operator: 1, 3 ### **ai.onnx.ml.ZipMap** Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes. Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
#### Version This version of the operator has been available since version 1 of the 'ai.onnx.ml' operator set. #### Attributes
classlabels_int64s : list of ints
The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.
classlabels_strings : list of strings
The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.
#### Inputs
X : tensor(float)
The input values
#### Outputs
Z : T
The output map
#### Type Constraints
T : seq(map(string, float)), seq(map(int64, float))
The output will be a sequence of string or integer maps to float.
onnx-onnx-bca0315/docs/Operators.md000066400000000000000000043423161511334557700173340ustar00rootroot00000000000000 ## Operator Schemas *This file is automatically generated from the [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py). Do not modify directly and instead edit operator definitions.* For an operator input/output's differentiability, it can be differentiable, non-differentiable, or undefined. If a variable's differentiability is not specified, that variable has undefined differentiability. ### ai.onnx (default) |**Operator**|**Since version**|| |-|-|-| |Abs|13, 6, 1| |Acos|22, 7| |Acosh|22, 9| |Add|14, 13, 7, 6, 1| |And|7, 1| |ArgMax|13, 12, 11, 1| |ArgMin|13, 12, 11, 1| |Asin|22, 7| |Asinh|22, 9| |Atan|22, 7| |Atanh|22, 9| |AveragePool|22, 19, 11, 10, 7, 1| |BatchNormalization|15, 14, 9, 7, 6, 1| |BitShift|11| |BitwiseAnd|18| |BitwiseNot|18| |BitwiseOr|18| |BitwiseXor|18| |Cast|25, 24, 23, 21, 19, 13, 9, 6, 1| |Ceil|13, 6, 1| |Col2Im|18| |Compress|11, 9| |Concat|13, 11, 4, 1| |ConcatFromSequence|11| |Constant|25, 24, 23, 21, 19, 13, 12, 11, 9, 1| |ConstantOfShape|25, 24, 23, 21, 20, 9| |Conv|22, 11, 1| |ConvInteger|10| |ConvTranspose|22, 11, 1| |Cos|22, 7| |Cosh|22, 9| |CumSum|14, 11| |DFT|20, 17| |DeformConv|22, 19| |DepthToSpace|13, 11, 1| |DequantizeLinear|25, 24, 23, 21, 19, 13, 10| |Det|22, 11| |Div|14, 13, 7, 6, 1| |Dropout|22, 13, 12, 10, 7, 6, 1| |Einsum|12| |Equal|19, 13, 11, 7, 1| |Erf|13, 9| |Exp|13, 6, 1| |Expand|13, 8| |EyeLike|22, 9| |Flatten|25, 24, 23, 21, 13, 11, 9, 1| |Floor|13, 6, 1| |GRU|22, 14, 7, 3, 1| |Gather|13, 11, 1| |GatherElements|13, 11| |GatherND|13, 12, 11| |Gemm|13, 11, 9, 7, 6, 1| |GlobalAveragePool|22, 1| |GlobalLpPool|22, 2, 1| |GlobalMaxPool|22, 1| |Greater|13, 9, 7, 1| |GridSample|22, 20, 16| |Hardmax|13, 11, 1| |Identity|25, 24, 23, 21, 19, 16, 14, 13, 1| |If|25, 24, 23, 21, 19, 16, 13, 11, 1| |ImageDecoder|20| |InstanceNormalization|22, 6, 1| |IsInf|20, 10| |IsNaN|20, 13, 9| |LRN|13, 1| |LSTM|22, 14, 7, 1| |Less|13, 9, 7, 1| |Log|13, 6, 1| |Loop|25, 24, 23, 21, 19, 16, 13, 11, 1| |LpNormalization|22, 1| |LpPool|22, 18, 11, 2, 1| |MatMul|13, 9, 1| |MatMulInteger|10| |Max|13, 12, 8, 6, 1| |MaxPool|22, 12, 11, 10, 8, 1| |MaxRoiPool|22, 1| |MaxUnpool|22, 11, 9| |Mean|13, 8, 6, 1| |MelWeightMatrix|17| |Min|13, 12, 8, 6, 1| |Mod|13, 10| |Mul|14, 13, 7, 6, 1| |Multinomial|22, 7| |Neg|13, 6, 1| |NonMaxSuppression|11, 10| |NonZero|13, 9| |Not|1| |OneHot|11, 9| |Optional|15| |OptionalGetElement|18, 15| |OptionalHasElement|18, 15| |Or|7, 1| |Pad|25, 24, 23, 21, 19, 18, 13, 11, 2, 1| |Pow|15, 13, 12, 7, 1| |QLinearConv|10| |QLinearMatMul|21, 10| |QuantizeLinear|25, 24, 23, 21, 19, 13, 10| |RNN|22, 14, 7, 1| |RandomNormal|22, 1| |RandomNormalLike|22, 1| |RandomUniform|22, 1| |RandomUniformLike|22, 1| |Reciprocal|13, 6, 1| |ReduceMax|20, 18, 13, 12, 11, 1| |ReduceMean|18, 13, 11, 1| |ReduceMin|20, 18, 13, 12, 11, 1| |ReduceProd|18, 13, 11, 1| |ReduceSum|13, 11, 1| |RegexFullMatch|20| |Reshape|25, 24, 23, 21, 19, 14, 13, 5, 1| |Resize|19, 18, 13, 11, 10| |ReverseSequence|10| |RoiAlign|22, 16, 10| |Round|22, 11| |STFT|17| |Scan|25, 24, 23, 21, 19, 16, 11, 9, 8| |Scatter (deprecated)|11, 9| |ScatterElements|18, 16, 13, 11| |ScatterND|18, 16, 13, 11| |SequenceAt|11| |SequenceConstruct|11| |SequenceEmpty|11| |SequenceErase|11| |SequenceInsert|11| |SequenceLength|11| |Shape|25, 24, 23, 21, 19, 15, 13, 1| |Sigmoid|13, 6, 1| |Sign|13, 9| |Sin|22, 7| |Sinh|22, 9| |Size|25, 24, 23, 21, 19, 13, 1| |Slice|13, 11, 10, 1| |SpaceToDepth|13, 1| |Split|18, 13, 11, 2, 1| |SplitToSequence|24, 11| |Sqrt|13, 6, 1| |Squeeze|25, 24, 23, 21, 13, 11, 1| |StringConcat|20| |StringNormalizer|10| |StringSplit|20| |Sub|14, 13, 7, 6, 1| |Sum|13, 8, 6, 1| |Tan|22, 7| |Tanh|13, 6, 1| |TensorScatter|24| |TfIdfVectorizer|9| |Tile|13, 6, 1| |TopK|24, 11, 10, 1| |Transpose|25, 24, 23, 21, 13, 1| |Trilu|14| |Unique|11| |Unsqueeze|25, 24, 23, 21, 13, 11, 1| |Upsample (deprecated)|10, 9, 7| |Where|16, 9| |Xor|7, 1| |**Function**|**Since version**|**Function version**| |AffineGrid|20|20| |Attention|24, 23|24| |Bernoulli|22, 15|22| |BlackmanWindow|17|17| |CastLike|25, 24, 23, 21, 19, 15|25| |Celu|12|12| |CenterCropPad|18|18| |Clip|13, 12, 11, 6, 1|13| |DynamicQuantizeLinear|11|11| |Elu|22, 6, 1|18| |Gelu|20|20| |GreaterOrEqual|16, 12|16| |GroupNormalization|21, 18|21| |HammingWindow|17|17| |HannWindow|17|17| |HardSigmoid|22, 6, 1|18| |HardSwish|22, 14|22| |LayerNormalization|17|17, 18| |LeakyRelu|16, 6, 1|16| |LessOrEqual|16, 12|16| |LogSoftmax|13, 11, 1|13, 18| |MeanVarianceNormalization|13, 9|13, 18| |Mish|22, 18|22| |NegativeLogLikelihoodLoss|22, 13, 12|22| |PRelu|16, 9, 7, 6, 1|16| |RMSNormalization|23|23| |Range|11|11| |ReduceL1|18, 13, 11, 1|18| |ReduceL2|18, 13, 11, 1|18| |ReduceLogSum|18, 13, 11, 1|18| |ReduceLogSumExp|18, 13, 11, 1|18| |ReduceSumSquare|18, 13, 11, 1|18| |Relu|14, 13, 6, 1|18| |RotaryEmbedding|23|23| |Selu|22, 6, 1|18| |SequenceMap|17|17| |Shrink|9|18| |Softmax|13, 11, 1|13, 18| |SoftmaxCrossEntropyLoss|13, 12|13| |Softplus|22, 1|18| |Softsign|22, 1|18| |Swish|24|24| |ThresholdedRelu|22, 10|18| ### ai.onnx.preview.training |**Operator**|**Since version**|| |-|-|-| |ai.onnx.preview.training.Adagrad|1| |ai.onnx.preview.training.Adam|1| |ai.onnx.preview.training.Gradient|1| |ai.onnx.preview.training.Momentum|1| ## ai.onnx (default) ### **Abs** Absolute takes one input data (Tensor) and produces one output data (Tensor) where absolute value, y = abs(x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
abs ```python node = onnx.helper.make_node( "Abs", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.abs(x) expect(node, inputs=[x], outputs=[y], name="test_abs") ```
#### Sample Implementation
Abs ```python # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np def abs(input: np.ndarray) -> np.ndarray: # noqa: A001 return np.abs(input) # type: ignore[no-any-return] ```
### **Acos** Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arccosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
acos ```python node = onnx.helper.make_node( "Acos", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos") ```
### **Acosh** Calculates the hyperbolic arccosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arccosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
acosh ```python node = onnx.helper.make_node( "Acosh", inputs=["x"], outputs=["y"], ) x = np.array([10, np.e, 1]).astype(np.float32) y = np.arccosh(x) # expected output [2.99322295, 1.65745449, 0.] expect(node, inputs=[x], outputs=[y], name="test_acosh_example") x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32) y = np.arccosh(x) expect(node, inputs=[x], outputs=[y], name="test_acosh") ```
### **Add** Performs element-wise binary addition (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 13 #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
add ```python node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint64") ```
add_broadcast ```python node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast") ```
### **AffineGrid** Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta (https://pytorch.org/docs/stable/generated/torch.nn.functional.affine_grid.html). An affine matrix `theta` is applied to a position tensor represented in its homogeneous expression. Here is an example in 3D: ``` [r00, r01, r02, t0] [x] [x'] [r10, r11, r12, t1] * [y] = [y'] [r20, r21, r22, t2] [z] [z'] [0, 0, 0, 1 ] [1] [1 ] ``` where `(x, y, z)` is the position in the original space, `(x', y', z')` is the position in the output space. The last row is always `[0, 0, 0, 1]` and is not stored in the affine matrix. Therefore we have `theta` of shape `(N, 2, 3)` for 2D or `(N, 3, 4)` for 3D. Input `size` is used to define grid of positions evenly spaced in the original 2D or 3D space, with dimensions ranging from `-1` to `1`. The output `grid` contains positions in the output space. When `align_corners=1`, consider `-1` and `1` to refer to the centers of the corner pixels (mark `v` in illustration). ``` v v v v |-------------------|------------------| -1 0 1 ``` When `align_corners=0`, consider `-1` and `1` to refer to the outer edge of the corner pixels. ``` v v v v |------------------|-------------------| -1 0 1 ``` #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
align_corners : int (default is 0)
if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.
#### Inputs
theta (non-differentiable) : T1
input batch of affine matrices with shape (N, 2, 3) for 2D or (N, 3, 4) for 3D
size (non-differentiable) : T2
the target output image size (N, C, H, W) for 2D or (N, C, D, H, W) for 3D
#### Outputs
grid (differentiable) : T1
output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
T2 : tensor(int64)
Constrain size's type to int64 tensors.
#### Examples
2d_no_reference_evaluator ```python theta_2d = create_theta_2d() N, C, H, W = len(theta_2d), 3, 5, 6 data_size = (H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_2d, original_grid) test_name = "test_affine_grid_2d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_2d, np.array([N, C, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) ```
3d_no_reference_evaluator ```python theta_3d = create_theta_3d() N, C, D, H, W = len(theta_3d), 3, 4, 5, 6 data_size = (D, H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_3d, original_grid) test_name = "test_affine_grid_3d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_3d, np.array([N, C, D, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) ```
### **And** Returns the tensor resulted from performing the `and` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
and ```python node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and4d") ```
and_broadcast ```python node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d") ```
### **ArgMax** Computes the indices of the max elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the max is selected if the max appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11, 12 #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
reduced (non-differentiable) : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
default_axes_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims ) # result: [[1, 1]] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random", ) ```
default_axes_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[1, 1]] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random_select_last_index", ) ```
keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random" ) ```
keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random_select_last_index", ) ```
negative_axis_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random", ) ```
negative_axis_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random_select_last_index", ) ```
no_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [0, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random" ) ```
no_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [1, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random_select_last_index", ) ```
### **ArgMin** Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11, 12 #### Attributes
axis : int (default is 0)
The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index : int (default is 0)
Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
reduced (non-differentiable) : tensor(int64)
Reduced output tensor with integer data type.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
default_axes_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims ) # The content of result is : [[0], [0]] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random", ) ```
default_axes_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[0, 0]] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random_select_last_index", ) ```
keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random" ) ```
keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random_select_last_index", ) ```
negative_axis_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random", ) ```
negative_axis_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random_select_last_index", ) ```
no_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1, 0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random" ) ```
no_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1, 0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random_select_last_index", ) ```
### **Asin** Calculates the arcsine (inverse of sine) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arcsine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
asin ```python node = onnx.helper.make_node( "Asin", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin") ```
### **Asinh** Calculates the hyperbolic arcsine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arcsine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
asinh ```python node = onnx.helper.make_node( "Asinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arcsinh(x) # expected output [-0.88137358, 0., 0.88137358] expect(node, inputs=[x], outputs=[y], name="test_asinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arcsinh(x) expect(node, inputs=[x], outputs=[y], name="test_asinh") ```
### **Atan** Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The arctangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
atan ```python node = onnx.helper.make_node( "Atan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan") ```
### **Atanh** Calculates the hyperbolic arctangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic arctangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
atanh ```python node = onnx.helper.make_node( "Atanh", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arctanh(x) # expected output [-0.54930615, 0., 0.54930615] expect(node, inputs=[x], outputs=[y], name="test_atanh_example") x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32) y = np.arctanh(x) expect(node, inputs=[x], outputs=[y], name="test_atanh") ```
### **Attention** Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed. This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V. For self attention, `kv_sequence_length` equals to `q_sequence_length`. For cross attention, query and key might have different lengths. This operator also covers the 3 following variants based on the number of heads: 1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, `q_num_heads = kv_num_heads`. 2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, `q_num_heads > kv_num_heads`, `q_num_heads % kv_num_heads == 0`. 3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, `q_num_heads > kv_num_heads`, `kv_num_heads=1`. Attention bias to be added is calculated based on `attn_mask` input and `is_causal` attribute: 1) `attn_mask`: A boolean mask where a value of `True` indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. 2) If `is_causal` is set to `1`, attention scores above the diagonal are masked out, regardless of the `attn_mask` input. With respect to KV cache update, this operator allows the following two use cases: 1) Cache update happens inside the Attention operator. In this case, the `K` and `V` inputs contain only the incoming tokens for the current autoregressive step, and the four optional inputs/outputs past and present key and value are all needed. The Attention op performs a Concat operation on the past and incoming key and value to form the present key and value, respectively. Note that this only works correctly for the special case where the past key and value do not contain padded tokens. 2) Cache update happens outside the Attention operator (for example, through the `TensorScatter` operator). In this case, the `K` and `V` inputs correspond to the entire cache tensor, so the four optional inputs/outputs past and present key and value should not be used. An additional input `nonpad_kv_seqlen` of shape (batch_size,) may be provided to indicate the number of non-padding tokens in each sample of the batch to save unnecessary computation. Here, the kv_sequence dimension of `attn_mask` can be shorter than `K` and `V`, but still needs to be at least as long as the maximum value of `nonpad_kv_seqlen`. Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: ``` The following pattern is applied by this operator: Q K V | | | Q*sqrt(scale) K*sqrt(scale) | | | | | Transpose | | | | ---MatMul--- | | | at_mask---Add | | | softcap (if provided) | | | Softmax | | | -----MatMul------ | Y ``` #### Version This version of the operator has been available since version 24 of the default ONNX operator set. Other versions of this operator: 23 #### Attributes
is_causal : int (default is 0)
If set to `1`, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment.
kv_num_heads : int
Number of heads of key and value. Must be used with 3D inputs of Q, K and V.
q_num_heads : int
Number of heads of query. Must be used with 3D inputs of Q, K and V.
qk_matmul_output_mode : int (default is 0)
If set to `0`, qk_matmul_output is the output of qk matmul. If set to `1`, qk_matmul_output includes the addition of the attention mask to the output of qk matmul. If set to `2`, qk_matmul_output is the output after the softcap operation. If set to `3`, qk_matmul_output is the output after the softmax operation. Default value is 0.
scale : float
Scaling factor applied to $Q*K^T$. Default value is `1/sqrt(head_size)`. To prevent [numerical overflow](https://tinyurl.com/sudb9s96), scale `Q`, `K` by `sqrt(scale)` before matmul.
softcap : float (default is 0.0)
Softcap value for attention weights. Default value is 0.
softmax_precision : int
The floating-point precision used in softmax computation. If softmax precision is not provided, the same precision as the input of softmax (Q and K) is used.
#### Inputs (3 - 7)
Q : T1
Query tensor. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, q_hidden_size)`. For cases with a 3D input tensor, `q_hidden_size = q_num_heads * head_size`
K : T1
Key tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, k_hidden_size)`. For cases with a 3D input tensor, `k_hidden_size = kv_num_heads * head_size`
V : T2
Value tensor. 4D tensor with shape `(batch_size, kv_num_heads, kv_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, kv_sequence_length, v_hidden_size)`. For cases with a 3D input tensor, `v_hidden_size = kv_num_heads * v_head_size`
attn_mask (optional) : U
Attention mask. Shape must be broadcastable to `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length.` The last dimension can also be shorter than `total_sequence_length` and will be padded to `total_sequence_length` with negative infinity. Two types of masks are supported: a boolean mask where a value of `True` indicates that the element should take part in attention, or a float mask of the same type as query, key, value that is added to the attention score.
past_key (optional) : T1
past state cache for key with shape `(batch_size, kv_num_heads, past_sequence_length, head_size)`
past_value (optional) : T2
past state cache for value with shape `(batch_size, kv_num_heads, past_sequence_length, v_head_size)`
nonpad_kv_seqlen (optional) : tensor(int64)
A vector of integers of shape `(batch_size,)` that indicates the number of valid (ie, non-padding) tokens in each sample. A padding mask can be derived from this. This should not be used together with `past_key` and `past_value` inputs or `present_key` and `present_value` outputs (See the KV cache use cases in the operator description).
#### Outputs (1 - 4)
Y : T1
The output tensor . 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size, q_sequence_length, hidden_size)`. For cases with a 3D input tensor, `hidden_size = q_num_heads * v_head_size`
present_key (optional) : T1
Updated key cache with shape `(batch_size, kv_num_heads, total_sequence_length, head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
present_value (optional) : T2
Updated value cache with shape `(batch_size, kv_num_heads, total_sequence_length, v_head_size)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
qk_matmul_output (optional) : T1
The output of QK matmul. 4D tensor with shape `(batch_size, q_num_heads, q_sequence_length, total_sequence_length)` where `total_sequence_length = past_sequence_length + kv_sequence_length`.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain Q and K inputs types to float tensors.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain V input types to float tensors.
U : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output 'mask' types to boolean tensors and input types.
#### Examples
attention ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_attn_mask ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_causal ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_attn_mask ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_causal ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_with_past_and_present ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_attn_mask ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_causal ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_softcap ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_with_past_and_present ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_transpose_verification ```python """Test case to verify correct 3D to 4D transpose behavior. This test verifies that 3D inputs are correctly reshaped and transposed according to the ONNX specification: [batch_size, seq_length, hidden_size] -> [batch_size, seq_length, num_heads, head_size] -> [batch_size, num_heads, seq_length, head_size] """ q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) # Test inputs that will clearly demonstrate the transpose behavior batch_size = 1 q_seq_length = 2 kv_seq_length = 2 head_size = 4 q_hidden_size = q_num_heads * head_size # 3 * 4 = 12 kv_hidden_size = kv_num_heads * head_size # 3 * 4 = 12 # Create structured inputs to verify correct transpose behavior # Q has a pattern where each position in hidden dimension has a specific value Q = np.zeros((batch_size, q_seq_length, q_hidden_size), dtype=np.float32) # Fill Q with pattern: head0=[1,1,1,1], head1=[2,2,2,2], head2=[3,3,3,3] for head in range(q_num_heads): start_idx = head * head_size end_idx = start_idx + head_size Q[0, :, start_idx:end_idx] = float(head + 1) K = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 V = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_transpose_verification", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_bias ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_softmax ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_4d_diff_heads_mask4d_padded_kv ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "", "", "nonpad_kv_seqlen"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 4).astype(np.float32) nonpad_kv_seqlen = np.array([3, 4], dtype=np.int64) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, nonpad_kv_seqlen=nonpad_kv_seqlen, ) expect( node, inputs=[Q, K, V, attn_mask, nonpad_kv_seqlen], outputs=[Y], name="test_attention_4d_diff_heads_mask4d_padded_kv", opset_imports=[onnx.helper.make_opsetid("", 24)], ) ```
attention_attn_3d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_3d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_4d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_4d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask_bool ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask_bool_4d ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=2.0, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present_mask3D ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present_mask4D ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_fp16 ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_with_past_and_present_fp16 ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float16) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_3d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_3d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_4d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_4d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y", "", "", "qk_matmul_output"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_bias ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], softcap=2.0, qk_matmul_output_mode=2, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_softmax ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=3, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
### **AveragePool** AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 7, 10, 11, 19 #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
count_include_pad : int (default is 0)
Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
averagepool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default") ```
averagepool_2d_ceil ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil") ```
averagepool_2d_ceil_last_window_starts_on_pad ```python """input_shape: [1, 3, 2, 2] output_shape: [1, 3, 1, 1] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[3, 3], pads=[1, 1, 1, 1], ceil_mode=True, count_include_pad=1, ) x = np.array( [ [ [[0.8580, 0.0786], [0.2692, 0.1537]], [[0.8816, 0.4353], [0.5772, 0.6623]], [[0.9067, 0.9483], [0.5970, 0.7630]], ] ] ).astype(np.float32) y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil_last_window_starts_on_pad", ) ```
averagepool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default") ```
averagepool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4] x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations") ```
averagepool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads") ```
averagepool_2d_pads_count_include_pad ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) dilations = (1, 1) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, count_include_pad=1, ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads_count_include_pad", ) ```
averagepool_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [7, 7.5, 8, 8.5, 9], [9.5, 10, 10.5, 11, 11.5], [12, 12.5, 13, 13.5, 14], [14.5, 15, 15.5, 16, 16.5], [17, 17.5, 18, 18.5, 19], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads" ) ```
averagepool_2d_precomputed_pads_count_include_pad ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [2.5200, 3.6000, 4.8000, 4.0800, 3.2400], [4.5600, 6.4000, 8.4000, 7.0400, 5.5200], [7.2000, 10.0000, 13.0000, 10.8000, 8.4000], [6.9600, 9.6000, 12.4000, 10.2400, 7.9200], [6.1200, 8.4000, 10.8000, 8.8800, 6.8400], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads_count_include_pad", ) ```
averagepool_2d_precomputed_same_upper ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype( np.float32 ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_same_upper", ) ```
averagepool_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_strides", ) ```
averagepool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower") ```
averagepool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper") ```
averagepool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( None, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = x y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=None, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides") ```
averagepool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default") ```
averagepool_3d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4, 4] x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small" ) ```
averagepool_3d_dilations_large ```python x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) count_include_pad = 0 for count_include_pad in (0, 1): for ceil_mode in (True, False): node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, count_include_pad=count_include_pad, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, extra_pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations=dilations, ceil_mode=ceil_mode, ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[3]), (extra_pads[1], extra_pads[4]), (extra_pads[2], extra_pads[5]), ), mode="constant", constant_values=0 if count_include_pad == 1 else np.nan, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=None, dilations=dilations, count_include_pad=count_include_pad, ) test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}" expect(node, inputs=[x], outputs=[y], name=test_name) ```
### **BatchNormalization** Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 9, 14 #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
momentum : float (default is 0.9)
Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
training_mode : int (default is 0)
If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
scale (differentiable) : T1
Scale tensor of shape (C).
B (differentiable) : T1
Bias tensor of shape (C).
input_mean (differentiable) : T2
running (training) or estimated (testing) mean tensor of shape (C).
input_var (differentiable) : T2
running (training) or estimated (testing) variance tensor of shape (C).
#### Outputs (1 - 3)
Y (differentiable) : T
The output tensor of the same shape as X
running_mean (optional, non-differentiable) : T2
The running mean after the BatchNormalization operator.
running_var (optional, non-differentiable) : T2
The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain scale and bias types to float tensors.
T2 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain mean and variance types to float tensors.
#### Examples
batchnormalization ```python # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_example", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) epsilon = 1e-2 y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_epsilon", ) ```
train ```python # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) # using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'" # working around by using np.byte(1).astype(bool) training_mode = 1 y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_example_training_mode", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) training_mode = 1 momentum = 0.9 epsilon = 1e-2 y, output_mean, output_var = _batchnorm_training_mode( x, s, bias, mean, var, momentum, epsilon ) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], epsilon=epsilon, training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_epsilon_training_mode", ) ```
### **Bernoulli** Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p). This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 15 #### Attributes
dtype : int
The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
All values in input have to be in the range:[0, 1].
#### Outputs
output : T2
The returned output tensor only has values 0 or 1, same shape as input tensor.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output types to all numeric tensors and bool tensors.
#### Examples
bernoulli_with_dtype ```python node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double") ```
bernoulli_with_seed ```python seed = float(0) node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], seed=seed, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, np.float32) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed") ```
bernoulli_without_dtype ```python node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], ) x = np.random.uniform(0.0, 1.0, 10).astype(float) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli") ```
### **BitShift** Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
direction : string (required)
Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).
#### Inputs
X (non-differentiable) : T
First operand, input to be shifted.
Y (non-differentiable) : T
Second operand, amounts of shift.
#### Outputs
Z (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)
Constrain input and output types to integer tensors.
#### Examples
left_unit16 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16") ```
left_unit32 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32") ```
left_unit64 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64") ```
left_unit8 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8") ```
right_unit16 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16") ```
right_unit32 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32") ```
right_unit64 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64") ```
right_unit8 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8") ```
### **BitwiseAnd** Returns the tensor resulting from performing the bitwise `and` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
#### Examples
bitwiseand ```python node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d") ```
bitwiseand_broadcast ```python node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_and(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d") ```
### **BitwiseNot** Returns the bitwise not of the input tensor element-wise. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input/output to integer tensors.
#### Examples
bitwisenot ```python node = onnx.helper.make_node( "BitwiseNot", inputs=["x"], outputs=["bitwise_not"], ) # 2d x = create_random_int((3, 4), np.int32) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d") # 3d x = create_random_int((3, 4, 5), np.uint16) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d") # 4d x = create_random_int((3, 4, 5, 6), np.uint8) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d") ```
### **BitwiseOr** Returns the tensor resulting from performing the bitwise `or` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
#### Examples
bitwiseor ```python node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d") # 4d x = create_random_int((3, 4, 5, 6), np.int8) y = create_random_int((3, 4, 5, 6), np.int8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d") ```
bitwiseor_broadcast ```python node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d") # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d") ```
### **BitwiseXor** Returns the tensor resulting from performing the bitwise `xor` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Inputs
A (non-differentiable) : T
First input operand for the bitwise operator.
B (non-differentiable) : T
Second input operand for the bitwise operator.
#### Outputs
C (non-differentiable) : T
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
Constrain input to integer tensors.
#### Examples
bitwiseor_broadcast ```python node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_xor(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d") ```
bitwisexor ```python node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d") ```
### **BlackmanWindow** Generates a Blackman window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Blackman window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
#### Examples
blackmanwindow ```python # Test periodic window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) y += a2 * np.cos(4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow", ) # Test symmetric window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) y += a2 * np.cos( 4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow_symmetric", ) ```
### **Cast** The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 types (E4M3FN, E4M3FNUZ, E5M2, E5M2FNUZ) were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | -Inf | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | FLOAT8E8M0 type was introduced to enable [Microscaling (MX) formats](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf). When casting to FLOAT8E8M0, the rounding behavior can be specified using the `round_mode` and `saturate` attributes. The current CUDA behavior is to round up and saturate. Casting negative values to FLOAT8E8M0 gives undefined behavior. The following table describes the casting behavior of special values to FLOAT8E8M0 in the two most common cases. | x | saturate + up | non-saturate + nearest | | ----------------- | ------------- | --------------------- | | 0 | 0 | NaN | | -0 | Unspecified | Unspecified | | NaN | NaN | NaN | | Inf | E8M0_MAX | NaN | | x > E8M0_MAX | E8M0_MAX | NaN | | x \< E8M0_MIN | E8M0_MIN | NaN | | x \< 0 | Unspecified | Unspecified | #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 6, 9, 13, 19, 21, 23, 24 #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. All cases are fully described in the tables inserted in the operator description.
to : int (required)
The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
#### Outputs
output (differentiable) : T2
Output tensor with the same shape as input with type specified by the 'to' argument
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types. Casting to complex is not supported.
#### Examples
cast ```python test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in F8_TYPES or to_type in F8_TYPES: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, ) expect( node, inputs=[input], outputs=[output], name="test_cast_" + from_type + "_to_" + to_type, ) ```
e8m0 ```python np_fp32 = np.array( [ "0.0", "0.124", "0.25", "0.5", "1.1", "2.0", "4.0", "8.0", ], dtype=np.float32, ) test_cases = [ ("FLOAT", "FLOAT8E8M0"), ("FLOAT16", "FLOAT8E8M0"), ("FLOAT8E8M0", "FLOAT"), ("FLOAT8E8M0", "FLOAT16"), ] for from_type, to_type in test_cases: if from_type == "FLOAT": input_np = np_fp32 output_np = to_float8e8m0(np_fp32) elif from_type == "FLOAT16": input_np = np_fp32.astype(np.float16) output_np = to_float8e8m0(input_np) elif from_type == "FLOAT8E8M0": input_np = to_float8e8m0(np_fp32) if to_type == "FLOAT": output_np = input_np.astype(np.float32) elif to_type == "FLOAT16": output_np = input_np.astype(np.float16) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) input = make_tensor( "input", getattr(TensorProto, from_type), [2, 4], input_np, raw=True, ) output = make_tensor( "output", getattr(TensorProto, to_type), [2, 4], output_np, raw=True, ) if to_type == "FLOAT8E8M0": node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), saturate=1, round_mode="up", ) else: node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), ) expect( node, inputs=[input], outputs=[output], name="test_cast_e8m0_" + from_type + "_to_" + to_type, ) ```
saturate_false ```python test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, saturate=0, ) expect( node, inputs=[input], outputs=[output], name="test_cast_no_saturate_" + from_type + "_to_" + to_type, ) ```
### **CastLike** The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 15, 19, 21, 23, 24 #### Attributes
round_mode : string (default is up)
Rounding mode for conversion to float8e8m0. It only applies to casting to float8e8m0 and is `up` by default. `up`: round to nearest value away from zero, `down`: round to nearest value towards zero, `nearest`: round to nearest value and ties round up. Please refer to operator Cast description for further details.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, float8e8m0). It is true by default. Please refer to operator Cast description for further details.
#### Inputs
input (differentiable) : T1
Input tensor to be cast.
target_type (non-differentiable) : T2
The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
#### Outputs
output (differentiable) : T2
Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input types. Casting from complex is not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types. Casting to complex is not supported.
#### Examples
castlike ```python test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] f8_types = {"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"} for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in f8_types or to_type in f8_types: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_" + from_type + "_to_" + to_type, ) ```
saturate_false ```python test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], saturate=0, ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_no_saturate_" + from_type + "_to_" + to_type, ) ```
### **Ceil** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
ceil ```python node = onnx.helper.make_node( "Ceil", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2]).astype(np.float32) y = np.ceil(x) # expected output [-1., 2.] expect(node, inputs=[x], outputs=[y], name="test_ceil_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.ceil(x) expect(node, inputs=[x], outputs=[y], name="test_ceil") ```
### **Celu** Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula: ``` max(0,x) + min(0,alpha*(exp(x/alpha)-1)) ``` #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float)
Constrain input and output types to float32 tensors.
#### Examples
celu ```python alpha = 2.0 node = onnx.helper.make_node( "Celu", inputs=["X"], outputs=["Y"], alpha=alpha, ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data positive_input = np.maximum(0, input_data) negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1)) expected_output = positive_input + negative_input expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu") ```
### **CenterCropPad** Center crop or pad an input to given dimensions. The crop/pad dimensions can be specified for a subset of the `axes`; unspecified dimensions will remain unchanged. If the input dimensions are larger than the target crop dimensions, a centered cropping window will be extracted from the input. The starting value for the cropping window is rounded down, which means that if the difference between the input shape and the crop shape is odd, the cropping window will be shifted half a pixel to the left of the input center. If the input dimensions are smaller than the target crop dimensions, the input will be padded equally on both sides to center it in the output. In cases where the total number of padding pixels is odd, an additional pixel will be added to the right side. The padding value used is zero. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
axes : list of ints
If provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
#### Inputs
input_data (differentiable) : T
Input to extract the centered crop from.
shape (non-differentiable) : Tind
1-D tensor representing the cropping window dimensions.
#### Outputs
output_data (differentiable) : T
Output data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
center_crop_pad_crop ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(20, 10, 3).astype(np.float32) shape = np.array([10, 7, 3], dtype=np.int64) y = x[5:15, 1:8, :] expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop") ```
center_crop_pad_crop_and_pad ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 10, 3], dtype=np.int64) y = np.zeros([10, 10, 3], dtype=np.float32) y[:, 1:9, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_and_pad", ) ```
center_crop_pad_crop_axes_chw ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[1, 2], ) # Cropping on second dim, padding on third, first stays the same x = np.random.randn(3, 20, 8).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([3, 10, 9], dtype=np.float32) y[:, :, :8] = x[:, 5:15, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_chw", ) ```
center_crop_pad_crop_axes_hwc ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[0, 1], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_hwc", ) ```
center_crop_pad_crop_negative_axes_hwc ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[-3, -2], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_negative_axes_hwc", ) ```
center_crop_pad_pad ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(10, 7, 3).astype(np.float32) shape = np.array([20, 10, 3], dtype=np.int64) y = np.zeros([20, 10, 3], dtype=np.float32) y[5:15, 1:8, :] = x expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad") ```
### **Clip** Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. When 'min' is greater than 'max', the clip operator sets all the 'input' values to the value of 'max'. Thus, this is equivalent to 'Min(max, Max(input, min))'. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 11, 12 #### Inputs (1 - 3)
input (differentiable) : T
Input tensor whose elements to be clipped
min (optional, non-differentiable) : T
Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).
max (optional, non-differentiable) : T
Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
#### Outputs
output (differentiable) : T
Output tensor with clipped input elements
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
clip ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) x = np.array([-2, 0, 2]).astype(np.float32) min_val = np.float32(-1) max_val = np.float32(1) y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.] expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example" ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, max_val) expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip") node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) min_val = np.float32(-5) max_val = np.float32(5) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds" ) x = np.array([-6, 0, 6]).astype(np.float32) y = np.array([-5, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds" ) x = np.array([-1, 0, 6]).astype(np.float32) y = np.array([-1, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_splitbounds", ) x = np.array([-2, 0, 6]).astype(np.float32) y = np.array([1, 1, 1]).astype(np.float32) min_val = np.float32(2) max_val = np.float32(1) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_min_greater_than_max", ) ```
clip_default ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, np.inf) expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min") no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, -np.inf, max_val) expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max") no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds") ```
clip_default_int8 ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, min_val, np.iinfo(np.int8).max) expect( node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min" ) no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, np.iinfo(np.int8).min, max_val) expect( node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max" ) no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.int8) y = np.array([-1, 0, 1]).astype(np.int8) expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds") ```
### **Col2Im** The operator rearranges column blocks back into a multidimensional image Col2Im behaves similarly to PyTorch's fold https://pytorch.org/docs/stable/generated/torch.nn.Fold.html, but it only supports *batched* multi-dimensional image tensors. Another implementation in Python with N-dimension support can be found at https://github.com/f-dangel/unfoldNd/. NOTE: Although specifying image_shape looks redundant because it could be calculated from convolution formulas, it is required as input for more advanced scenarios as explained at PyTorch's implementation (https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Col2Im.cpp#L10) #### Version This version of the operator has been available since version 18 of the default ONNX operator set. #### Attributes
dilations : list of ints
1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.
pads : list of ints
1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
input (differentiable) : T
Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).
image_shape (non-differentiable) : tensor(int64)
The shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.
block_shape (non-differentiable) : tensor(int64)
The shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.
#### Outputs
output (differentiable) : T
Output tensor produced by rearranging blocks into an image.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all numeric tensor types.
#### Examples
col2im ```python input = np.array( [ [ [1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5) [2.0, 7.0, 12.0, 17.0, 22.0], [3.0, 8.0, 13.0, 18.0, 23.0], [4.0, 9.0, 14.0, 19.0, 24.0], [5.0, 0.0, 15.0, 20.0, 25.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) output = np.array( [ [ [ [1.0, 2.0, 3.0, 4.0, 5.0], # (1, 1, 5, 5) [6.0, 7.0, 8.0, 9.0, 0.0], [11.0, 12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0, 20.0], [21.0, 22.0, 23.0, 24.0, 25.0], ] ] ] ).astype(np.float32) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im", ) ```
col2im_5d ```python input = np.array( [ [ [1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12) [2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57], [3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58], [4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59], [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60], [61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116], [62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117], [63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118], [64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119], [65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120], ] ] ).astype(np.float32) image_shape = np.array([3, 4, 5]).astype(np.int64) block_shape = np.array([1, 1, 5]).astype(np.int64) output = np.array( [ [ [ [ [1, 2, 3, 4, 5], # (1, 2, 3, 4, 5) [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], ], [ [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35], [36, 37, 38, 39, 40], ], [ [41, 42, 43, 44, 45], [46, 47, 48, 49, 50], [51, 52, 53, 54, 55], [56, 57, 58, 59, 60], ], ], [ [ [61, 62, 63, 64, 65], [66, 67, 68, 69, 70], [71, 72, 73, 74, 75], [76, 77, 78, 79, 80], ], [ [81, 82, 83, 84, 85], [86, 87, 88, 89, 90], [91, 92, 93, 94, 95], [96, 97, 98, 99, 100], ], [ [101, 102, 103, 104, 105], [106, 107, 108, 109, 110], [111, 112, 113, 114, 115], [116, 117, 118, 119, 120], ], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_5d", ) ```
col2im_dilations ```python input = np.array( [ [ [1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5) [2.0, 6.0, 10.0, 14.0, 18], [3.0, 7.0, 11.0, 15.0, 19], [4.0, 8.0, 12.0, 16.0, 20], ] ] ).astype(np.float32) image_shape = np.array([6, 6]).astype(np.int64) block_shape = np.array([2, 2]).astype(np.int64) output = np.array( [ [ [ [1.0, 0.0, 0.0, 0.0, 0.0, 2.0], # (1, 1, 6, 6) [8.0, 0.0, 0.0, 0.0, 0.0, 10.0], [16.0, 0.0, 0.0, 0.0, 0.0, 18.0], [24.0, 0.0, 0.0, 0.0, 0.0, 26.0], [32.0, 0.0, 0.0, 0.0, 0.0, 34.0], [19.0, 0.0, 0.0, 0.0, 0.0, 20.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], dilations=[1, 5], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_dilations", ) ```
col2im_pads ```python input = np.array( [ [ [ 1.0, 6.0, 11.0, 16.0, 21.0, 26, 31, 36, 41, 46, 51, 56, 61, 66, 71, ], # (1, 5, 15) [ 2.0, 7.0, 12.0, 17.0, 22.0, 27, 32, 37, 42, 47, 52, 57, 62, 67, 72, ], [ 3.0, 8.0, 13.0, 18.0, 23.0, 28, 33, 38, 43, 48, 53, 58, 63, 68, 73, ], [ 4.0, 9.0, 14.0, 19.0, 24.0, 29, 34, 39, 44, 49, 54, 59, 64, 69, 74, ], [ 5.0, 10.0, 15.0, 20.0, 25.0, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, ], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) output = np.array( [ [ [ [8.0, 21.0, 24.0, 27.0, 24.0], # (1, 1, 5, 5) [38.0, 66.0, 69.0, 72.0, 54.0], [68.0, 111.0, 114.0, 117.0, 84.0], [98.0, 156.0, 159.0, 162.0, 114.0], [128.0, 201.0, 204.0, 207.0, 144.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], pads=[0, 1, 0, 1], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_pads", ) ```
col2im_strides ```python input = np.array( [ [ [0.0, 0.0, 0.0, 0.0], # (1, 9, 4) [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([3, 3]).astype(np.int64) output = np.array( [ [ [ [0.0, 1.0, 1.0, 1.0, 1.0], # (1, 1, 5, 5) [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 2.0, 1.0, 2.0, 1.0], [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], strides=[2, 2], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_strides", ) ```
### **Compress** Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html #### Version This version of the operator has been available since version 11 of the default ONNX operator set. Other versions of this operator: 9 #### Attributes
axis : int
(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
Tensor of rank r >= 1.
condition (non-differentiable) : T1
Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
#### Outputs
output (differentiable) : T
Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(bool)
Constrain to boolean tensors.
#### Examples
compress_0 ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=0, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 1]) output = np.compress(condition, input, axis=0) # print(output) # [[ 3. 4.] # [ 5. 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_0", ) ```
compress_1 ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_1", ) ```
compress_default_axis ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 0, 0, 1]) output = np.compress(condition, input) # print(output) # [ 2., 5.] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_default_axis", ) ```
compress_negative_axis ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=-1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=-1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_negative_axis", ) ```
### **Concat** Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 4, 11 #### Attributes
axis : int (required)
Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..
#### Inputs (1 - ∞)
inputs (variadic, differentiable) : T
List of tensors for concatenation
#### Outputs
concat_result (differentiable) : T
Concatenated tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
#### Examples
concat ```python test_cases: dict[str, Sequence[Any]] = { "1d": ([1, 2], [3, 4]), "2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]), "3d": ( [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]], ), } for test_case, values_ in test_cases.items(): values = [np.asarray(v, dtype=np.float32) for v in values_] for i in range(len(values[0].shape)): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_" + str(i), ) for i in range(-len(values[0].shape), 0): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)), ) ```
### **ConcatFromSequence** Concatenate a sequence of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. By default 'new_axis' is 0, the behavior is similar to numpy.concatenate. When 'new_axis' is 1, the behavior is similar to numpy.stack. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int (required)
Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`.
new_axis : int (default is 0)
Insert and concatenate on a new axis or not, default 0 means do not insert new axis.
#### Inputs
input_sequence : S
Sequence of tensors for concatenation
#### Outputs
concat_result : T
Concatenated tensor
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input types to any tensor type.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain output types to any tensor type.
### **Constant** This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 9, 11, 12, 13, 19, 21, 23, 24 #### Attributes
sparse_value : sparse_tensor
The value for the elements of the output tensor in sparse format.
value : tensor
The value for the elements of the output tensor.
value_float : float
The value for the sole element for the scalar, float32, output tensor.
value_floats : list of floats
The values for the elements for the 1D, float32, output tensor.
value_int : int
The value for the sole element for the scalar, int64, output tensor.
value_ints : list of ints
The values for the elements for the 1D, int64, output tensor.
value_string : string
The value for the sole element for the scalar, UTF-8 string, output tensor.
value_strings : list of strings
The values for the elements for the 1D, UTF-8 string, output tensor.
#### Inputs #### Outputs
output : T
Output tensor containing the same value of the provided tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
#### Examples
constant ```python values = np.random.randn(5, 5).astype(np.float32) node = onnx.helper.make_node( "Constant", inputs=[], outputs=["values"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=values.shape, vals=values.flatten().astype(float), ), ) expect(node, inputs=[], outputs=[values], name="test_constant") ```
### **ConstantOfShape** Generate a tensor with given value and shape. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 9, 20, 21, 23, 24 #### Attributes
value : tensor
(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
#### Inputs
input : T1
1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
#### Outputs
output : T2
Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
#### Type Constraints
T1 : tensor(int64)
Constrain input types.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint4), tensor(int4), tensor(bool), tensor(bfloat16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain output types to be numerics or boolean.
#### Examples
float_ones ```python x = np.array([4, 3, 2]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.FLOAT, [1], [1] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.ones(x, dtype=np.float32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_float_ones") ```
int32_shape_zero ```python x = np.array( [ 0, ] ).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect( node, inputs=[x], outputs=[y], name="test_constantofshape_int_shape_zero" ) ```
int32_zeros ```python x = np.array([10, 6]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_int_zeros") ```
### **Conv** The convolution operator consumes an input tensor and a filter, and computes the output. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
conv ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[1, 1, 1, 1], ) y_with_padding = np.array( [ [ [ [12.0, 21.0, 27.0, 33.0, 24.0], # (1, 1, 5, 5) output tensor [33.0, 54.0, 63.0, 72.0, 51.0], [63.0, 99.0, 108.0, 117.0, 81.0], [93.0, 144.0, 153.0, 162.0, 111.0], [72.0, 111.0, 117.0, 123.0, 84.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_basic_conv_with_padding", ) # Convolution without padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[0, 0, 0, 0], ) y_without_padding = np.array( [ [ [ [54.0, 63.0, 72.0], # (1, 1, 3, 3) output tensor [99.0, 108.0, 117.0], [144.0, 153.0, 162.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_basic_conv_without_padding", ) ```
conv_with_autopad_same ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with auto_pad='SAME_LOWER' and strides=2 node = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], auto_pad="SAME_LOWER", kernel_shape=[3, 3], strides=[2, 2], ) y = np.array( [[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same") ```
conv_with_strides ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 7, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with strides=2 and padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_padding = np.array( [ [ [ [12.0, 27.0, 24.0], # (1, 1, 4, 3) output tensor [63.0, 108.0, 81.0], [123.0, 198.0, 141.0], [112.0, 177.0, 124.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_conv_with_strides_padding", ) # Convolution with strides=2 and no padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_without_padding = np.array( [ [ [ [54.0, 72.0], # (1, 1, 3, 2) output tensor [144.0, 162.0], [234.0, 252.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_conv_with_strides_no_padding", ) # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor) node_with_asymmetric_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 0, 1, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_asymmetric_padding = np.array( [ [ [ [21.0, 33.0], # (1, 1, 4, 2) output tensor [99.0, 117.0], [189.0, 207.0], [171.0, 183.0], ] ] ] ).astype(np.float32) expect( node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding], name="test_conv_with_strides_and_asymmetric_padding", ) ```
### **ConvInteger** The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point, and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.
group : int (default is 1)
number of groups input channels and output channels are divided into. default is 1.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input 'w'.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.
#### Inputs (2 - 4)
x : T1
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
w : T2
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
x_zero_point (optional) : T1
Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.
w_zero_point (optional) : T2
Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)
#### Outputs
y : T3
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input x and its zero point data type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain input w and its zero point data type to 8-bit integer tensor.
T3 : tensor(int32)
Constrain output y data type to 32-bit integer tensor.
#### Examples
with_padding ```python x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w_zero_points = np.array([0, 1], dtype=np.uint8) w = np.array([1, 1, 1, 1, 1, 1, 1, 1]).astype(np.uint8).reshape((2, 1, 2, 2)) y = ( np.array( [ 1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ] ) .astype(np.int32) .reshape((1, 2, 4, 4)) ) # ConvInteger with padding convinteger_node_with_padding = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point", "w_zero_points"], outputs=["y"], pads=[1, 1, 1, 1], ) expect( convinteger_node_with_padding, inputs=[x, w, x_zero_point, w_zero_points], outputs=[y], name="test_convinteger_with_padding", ) ```
without_padding ```python x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2)) y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2) # ConvInteger without padding convinteger_node = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"] ) expect( convinteger_node, inputs=[x, w, x_zero_point], outputs=[y], name="test_convinteger_without_padding", ) ```
### **ConvTranspose** The convolution transpose operator consumes an input tensor and a filter, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding : list of ints
Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.
output_shape : list of ints
The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
W (differentiable) : T
The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
B (optional, differentiable) : T
Optional 1D bias to be added to the convolution, has size of M.
#### Outputs
Y (differentiable) : T
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
convtranspose ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], # (1, 2, 5, 5) [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose") ```
convtranspose_1d ```python x = np.array([[[0.0, 1.0, 2.0]]]).astype(np.float32) # (1, 1, 3) W = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]).astype( # (1, 2, 3) np.float32 ) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [[[0.0, 1.0, 3.0, 3.0, 2.0], [0.0, 1.0, 3.0, 3.0, 2.0]]] # (1, 2, 5) ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_1d") ```
convtranspose_3d ```python x = np.array( [ [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 3, 4, 5) [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], ], [ [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], [35.0, 36.0, 37.0, 38.0, 39.0], ], [ [40.0, 41.0, 42.0, 43.0, 44.0], [45.0, 46.0, 47.0, 48.0, 49.0], [50.0, 51.0, 52.0, 53.0, 54.0], [55.0, 56.0, 57.0, 58.0, 59.0], ], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [ [1.0, 1.0, 1.0], # (1, 2, 3, 3, 3) [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], # (1, 2, 5, 6, 7) [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_3d") ```
convtranspose_attributes ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], # (1, 2, 10, 8) [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_shape=[10, 8] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_output_shape") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_padding=[1, 1] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pad") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], name="test", strides=[3, 2], output_shape=[10, 8], kernel_shape=[3, 3], output_padding=[1, 1], ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_kernel_shape") ```
convtranspose_autopad_same ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2] ) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_autopad_same") ```
convtranspose_dilations ```python x = np.array( [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2] ) y = np.array( [ [ [ [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5] [63.0, 35.0, 67.0, 10.0, 14.0], [24.0, 22.0, 76.0, 76.0, 21.0], [9.0, 5.0, 88.0, 45.0, 63.0], [3.0, 2.0, 33.0, 18.0, 54.0], ] ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_dilations") ```
convtranspose_group_2 ```python x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ] ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2") ```
convtranspose_group_2_image_3 ```python x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [18.0, 37.0, 57.0, 39.0, 20.0], [39.0, 80.0, 123.0, 84.0, 43.0], [63.0, 129.0, 198.0, 135.0, 69.0], [45.0, 92.0, 141.0, 96.0, 49.0], [24.0, 49.0, 75.0, 51.0, 26.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], ] ).astype(np.float32) expect( node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2_image_3" ) ```
convtranspose_pads ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], pads=[1, 2, 1, 2] ) y = np.array( [ [ [ [1.0, 1.0, 3.0], # (1, 2, 7, 3) [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], [ [1.0, 1.0, 3.0], [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pads") ```
### **Cos** Calculates the cosine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The cosine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
cos ```python node = onnx.helper.make_node( "Cos", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos") ```
### **Cosh** Calculates the hyperbolic cosine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic cosine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
cosh ```python node = onnx.helper.make_node( "Cosh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cosh(x) # expected output [1.54308069, 1., 1.54308069] expect(node, inputs=[x], outputs=[y], name="test_cosh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cosh(x) expect(node, inputs=[x], outputs=[y], name="test_cosh") ```
### **CumSum** Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 11 #### Attributes
exclusive : int (default is 0)
If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.
reverse : int (default is 0)
If set to 1 will perform the sums in reverse direction.
#### Inputs
x (differentiable) : T
An input tensor that is to be processed.
axis (non-differentiable) : T2
A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.
#### Outputs
y (differentiable) : T
Output tensor of the same type as 'x' with cumulative sums of the x's elements
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
T2 : tensor(int32), tensor(int64)
axis tensor can be int32 or int64 only
#### Examples
cumsum_1d ```python node = onnx.helper.make_node("CumSum", inputs=["x", "axis"], outputs=["y"]) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([1.0, 3.0, 6.0, 10.0, 15.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d") ```
cumsum_1d_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([0.0, 1.0, 3.0, 6.0, 10.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_exclusive") ```
cumsum_1d_int32_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1, 2, 3, 4, 5]).astype(np.int32) axis = np.int32(0) y = np.array([0, 1, 3, 6, 10]).astype(np.int32) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_int32_exclusive" ) ```
cumsum_1d_reverse ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([15.0, 14.0, 12.0, 9.0, 5.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse") ```
cumsum_1d_reverse_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1, exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([14.0, 12.0, 9.0, 5.0, 0.0]).astype(np.float64) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse_exclusive" ) ```
cumsum_2d_axis_0 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(0) y = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 9.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_0") ```
cumsum_2d_axis_1 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_1") ```
cumsum_2d_int32 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.int32).reshape((2, 3)) axis = np.int32(0) y = np.array([1, 2, 3, 5, 7, 9]).astype(np.int32).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_int32") ```
cumsum_2d_negative_axis ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(-1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis") ```
### **DFT** Computes the discrete Fourier Transform (DFT) of the input. Assuming the input has shape `[M, N]`, where `N` is the dimension over which the DFT is computed and `M` denotes the conceptual "all other dimensions," the DFT `y[m, k]` of shape `[M, N]` is defined as $$y[m, k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[m, n] ,$$ and the inverse transform is defined as $$x[m, n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[m, k] ,$$ where $j$ is the imaginary unit. The actual shape of the output is specified in the "output" section. Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html #### Version This version of the operator has been available since version 20 of the default ONNX operator set. Other versions of this operator: 17 #### Attributes
inverse : int (default is 0)
Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.
onesided : int (default is 0)
If `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.
#### Inputs (1 - 3)
input (non-differentiable) : T1
For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.
dft_length (optional, non-differentiable) : T2
The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal.
axis (optional, non-differentiable) : tensor(int64)
The axis as a scalar on which to perform the DFT. Default is `-2` (last signal axis). Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.
#### Outputs
output : T1
The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain scalar length types to integers.
#### Examples
dft ```python node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(1, dtype=np.int64) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft") node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(2, dtype=np.int64) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_axis") node = onnx.helper.make_node( "DFT", inputs=["x", "", "axis"], outputs=["y"], inverse=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape(10, 10) axis = np.array(1, dtype=np.int64) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_inverse") ```
opset19 ```python node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_axis_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node( "DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape( 10, 10, ) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_inverse_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) ```
### **DeformConv** Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 19 #### Attributes
dilations : list of ints
Dilation value along each spatial axis of the kernel. Default is 1 along each axis.
group : int (default is 1)
Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.
kernel_shape : list of ints
Shape of the convolution kernel. If not present, it is inferred from the shape of input W.
offset_group : int (default is 1)
Number of groups of offset. C must be divisible by offset_group. Default is 1.
pads : list of ints
Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.
strides : list of ints
Stride along each spatial axis. Default is 1 along each axis.
#### Inputs (3 - 5)
X : T
Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.
W : T
Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).
offset : T
Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.
B (optional) : T
Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.
mask (optional) : T
The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.
#### Outputs
Y : T
Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD data
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
deformconv ```python X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) # Convolution with padding offset_with_padding = np.zeros((1, 8, 4, 4), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_with_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [1, 2] offset_with_padding[0, 5, 1, 2] = -0.1 node_with_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_with_padding"], outputs=["Y_with_padding"], kernel_shape=[2, 2], pads=[1, 1, 1, 1], ) Y_with_padding = np.array( [ [ [ [0.0, 1.0, 3.0, 2.0], # (1, 1, 4, 4) output tensor [3.0, 8.0, 11.9, 7.0], [9.0, 20.0, 24.0, 13.0], [6.0, 13.0, 15.0, 8.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[X, W, offset_with_padding], outputs=[Y_with_padding], name="test_basic_deform_conv_with_padding", ) # Convolution without padding offset_without_padding = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_without_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset_without_padding[0, 5, 0, 1] = -0.1 node_without_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_without_padding"], outputs=["Y_without_padding"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y_without_padding = np.array( [ [ [ [9.5, 11.9], # (1, 1, 2, 2) output tensor [20.0, 24.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[X, W, offset_without_padding], outputs=[Y_without_padding], name="test_basic_deform_conv_without_padding", ) ```
deformconv_with_mask_bias ```python X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) B = np.ones((1,), dtype=np.float32) offset = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset[0, 5, 0, 1] = -0.1 mask = np.ones((1, 4, 2, 2), dtype=np.float32) mask[0, 2, 1, 1] = 0.2 # [1, 0] element of kernel at output position [1, 1] node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset", "B", "mask"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y = np.array( [ [ [ [10.5, 12.9], # (1, 1, 2, 2) output tensor [21.0, 19.4], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset, B, mask], outputs=[Y], name="test_deform_conv_with_mask_bias", ) ```
deformconv_with_multiple_offset_groups ```python X = np.zeros((1, 2, 3, 3), dtype=np.float32) X[0, 0] = np.reshape(np.arange(9).astype(np.float32), (3, 3)) X[0, 1] = np.reshape(np.arange(8, -1, -1).astype(np.float32), (3, 3)) X.shape = (1, 2, 3, 3) W = np.ones((1, 2, 2, 2), dtype=np.float32) offset = np.zeros((1, 16, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel in channel 0, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel in channel 1, at output position [0, 1] offset[0, 13, 0, 1] = -0.1 node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], offset_group=2, ) Y = np.array( [ [ [ [33.5, 32.1], # (1, 1, 2, 2) output tensor [32.0, 32.0], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset], outputs=[Y], name="test_deform_conv_with_multiple_offset_groups", ) ```
### **DepthToSpace** DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) ``` In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
mode : string (default is DCR)
DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.
#### Inputs
input (differentiable) : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output (differentiable) : T
Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
#### Examples
crd_mode_example ```python node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="CRD" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 9.0, 1.0, 10.0, 2.0, 11.0], [18.0, 27.0, 19.0, 28.0, 20.0, 29.0], [3.0, 12.0, 4.0, 13.0, 5.0, 14.0], [21.0, 30.0, 22.0, 31.0, 23.0, 32.0], ], [ [36.0, 45.0, 37.0, 46.0, 38.0, 47.0], [54.0, 63.0, 55.0, 64.0, 56.0, 65.0], [39.0, 48.0, 40.0, 49.0, 41.0, 50.0], [57.0, 66.0, 58.0, 67.0, 59.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_crd_mode_example") ```
default_mode_example ```python node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="DCR" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 18.0, 1.0, 19.0, 2.0, 20.0], [36.0, 54.0, 37.0, 55.0, 38.0, 56.0], [3.0, 21.0, 4.0, 22.0, 5.0, 23.0], [39.0, 57.0, 40.0, 58.0, 41.0, 59.0], ], [ [9.0, 27.0, 10.0, 28.0, 11.0, 29.0], [45.0, 63.0, 46.0, 64.0, 47.0, 65.0], [12.0, 30.0, 13.0, 31.0, 14.0, 32.0], [48.0, 66.0, 49.0, 67.0, 50.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_example") ```
### **DequantizeLinear** The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type is the same as `x_scale`. The output type also determines the precision of the multiplication operation. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 10, 13, 19, 21, 23, 24 #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `x_scale` data type (`T2`)
#### Inputs (2 - 3)
x : T1
N-D quantized input tensor to be de-quantized.
x_scale : T2
Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
x_zero_point (optional) : T1
Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
#### Outputs
y : T3
N-D full precision output tensor. It has the same shape as input `x`. The data type is specified by the `output_dtype` attribute or, in its absence, the type of `x_scale`.
#### Type Constraints
T1 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(uint2), tensor(int2)
The type of the inputs 'x_zero_point' and 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(float8e8m0)
The type of the input 'x_scale'.
T3 : tensor(float), tensor(float16), tensor(bfloat16)
The type of the output 'y'.
#### Examples
axis ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # 1-D tensor zero point and scale of size equal to axis 1 of the input tensor x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array([2, 4, 5], dtype=np.float32) x_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = ( x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32) ) * x_scale.reshape(1, 3, 1, 1) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_axis", ) ```
blocked ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[5, 12], [12, 33], [65, 42]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array( [ [ [[3.0, 2.0], [4.0, 1.0], [2.0, 2.0]], [[5.0, 2.0], [4.0, 3.0], [5.0, 2.0]], ], ], dtype=np.float32, ) x_zero_point = np.array( [ [ [[1, 0], [0, 1], [2, 20]], [[3, 2], [4, 3], [15, 2]], ], ], dtype=np.uint8, ) # x.shape = (1, 4, 3, 2) # x_scale.shape = (1, 2, 3, 2) assert x_scale.shape == x_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // x_scale.shape[i] for i in range(len(x.shape))] = (1, 2, 1, 1) assert all( x.shape[i] == x_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % x_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // x_scale.shape[block_axis] # Create element-wise scale and zero point x_scale_elementwise = np.repeat(x_scale, repeats=repeats, axis=block_axis) x_zero_point_elementwise = np.repeat( x_zero_point, repeats=repeats, axis=block_axis ) y = ( x.astype(np.float32) - x_zero_point_elementwise.astype(np.float32) ) * x_scale_elementwise expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_blocked", ) ```
dequantizelinear ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # scalar zero point and scale x = np.array([0, 3, 128, 255]).astype(np.uint8) x_scale = np.float32(2) x_zero_point = np.uint8(128) y = np.array([-256, -250, 0, 254], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear", ) ```
e4m3fn ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn", ) ```
e4m3fn_float16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float16(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float16) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn_float16", ) ```
e4m3fn_zero_point ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) zero_point = make_tensor("zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale, zero_point], outputs=[y], name="test_dequantizelinear_e4m3fn_zero_point", ) ```
e5m2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, -96]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 98304.0, -192.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e5m2", ) ```
float4e2m1 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT4E2M1, [5], [0, 1, -1, 1.5, -4]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.FLOAT4E2M1, (1,), [0]) y = np.array([0, 2, -2, 3, -8], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_float4e2m1", ) ```
int16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([-300, -30, -1025, 1270]).astype(np.int16) x_scale = np.float32(2) x_zero_point = np.int16(-1024) y = np.array([1448.0, 1988.0, -2.0, 4588.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int16", ) ```
int2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT2, [4], [0, 1, -1, -2]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT2, (1,), [1]) y = np.array([-2, 0, -4, -6], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int2", ) ```
int4 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT4, [5], [0, 1, 7, -4, -8]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT4, (1,), [1]) y = np.array([-2, 0, 12, -10, -18], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int4", ) ```
uint16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([30000, 31000, 32768, 33000]).astype(np.uint16) x_scale = np.float32(2) x_zero_point = np.uint16(32767) y = np.array([-5534.0, -3534.0, 2.0, 466.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint16", ) ```
uint2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT2, [4], [0, 1, 2, 3]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT2, (1,), [1]) y = np.array([-2, 0, 2, 4], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint2", ) ```
uint4 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT4, [5], [0, 1, 7, 10, 15]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT4, (1,), [1]) y = np.array([-2, 0, 12, 18, 28], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint4", ) ```
### **Det** Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions, and the inner-most 2 dimensions form square matrices. The output is a tensor of shape `[*]`, containing the determinants of all input submatrices. e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 11 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to floating-point tensors.
#### Examples
2d ```python node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.arange(4).reshape(2, 2).astype(np.float32) y = np.linalg.det(x) # expect -2 expect(node, inputs=[x], outputs=[y], name="test_det_2d") ```
nd ```python node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype( np.float32 ) y = np.linalg.det(x) # expect array([-2., -3., -8.]) expect(node, inputs=[x], outputs=[y], name="test_det_nd") ```
### **Div** Performs element-wise binary division (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 13 #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
div ```python node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.array([3, 4]).astype(np.float32) y = np.array([1, 2]).astype(np.float32) z = x / y # expected output [3., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_div_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint64") ```
div_broadcast ```python node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast") ```
### **Dropout** Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 10, 12, 13 #### Attributes
seed : int
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs (1 - 3)
data (differentiable) : T
The input data as Tensor.
ratio (optional, non-differentiable) : T1
The ratio of random dropout, with value in [0, 1). If set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
training_mode (optional, non-differentiable) : T2
If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.
#### Outputs (1 - 2)
output (differentiable) : T
The output.
mask (optional, non-differentiable) : T2
The output mask.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input and output types to float tensors.
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input 'ratio' types to float tensors.
T2 : tensor(bool)
Constrain output 'mask' types to boolean tensors.
#### Examples
default ```python seed = np.int64(0) node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x) expect(node, inputs=[x], outputs=[y], name="test_dropout_default") ```
default_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, return_mask=True) expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask") ```
default_mask_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, r, return_mask=True) expect( node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio" ) ```
default_old ```python node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_default_old", opset_imports=[helper.make_opsetid("", 11)], ) ```
default_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x, r) expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio") ```
random_old ```python node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ratio=0.2, ) x = np.random.randn(3, 4, 5).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_random_old", opset_imports=[helper.make_opsetid("", 11)], ) ```
training ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout") ```
training_default ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default" ) ```
training_default_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_default_mask", ) ```
training_default_zero_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio" ) ```
training_default_zero_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_zero_ratio_mask", ) ```
training_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask" ) ```
### **DynamicQuantizeLinear** A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as: ``` y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin) ``` * where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8 * data range is adjusted to include 0. Zero point is calculated as: ``` intermediate_zero_point = qmin - min(x)/y_scale y_zero_point = cast(round(saturate(intermediate_zero_point))) ``` * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. Data quantization formula is: ``` y = saturate (round (x / y_scale) + y_zero_point) ``` * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
x : T1
Input tensor
#### Outputs
y : T2
Quantized output tensor
y_scale : tensor(float)
Output scale. It's a scalar, which means a per-tensor/layer quantization.
y_zero_point : T2
Output zero point. It's a scalar, which means a per-tensor/layer quantization.
#### Type Constraints
T1 : tensor(float)
Constrain 'x' to float tensor.
T2 : tensor(uint8)
Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.
#### Examples
dynamicquantizelinear ```python node = onnx.helper.make_node( "DynamicQuantizeLinear", inputs=["x"], outputs=["y", "y_scale", "y_zero_point"], ) # expected scale 0.0196078438 and zero point 153 X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear", ) # expected scale 0.0156862754 and zero point 255 X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_max_adjusted", ) X = ( np.array([1, 2.1, 1.3, 2.5, 3.34, 4.0, 1.5, 2.6, 3.9, 4.0, 3.0, 2.345]) .astype(np.float32) .reshape((3, 4)) ) # expected scale 0.0156862754 and zero point 0 x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_min_adjusted", ) ```
### **Einsum** An einsum of the form `term1, term2 -> output-term` produces an output tensor using the following equation ``` output[output-term] = reduce-sum( input1[term1] * input2[term2] ) ``` where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term. The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions. This sequence may be followed by "->" to separate the left and right hand side of the equation. If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation. When a dimension character is repeated in the left-hand side, it represents summation along the dimension. The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character. #### Version This version of the operator has been available since version 12 of the default ONNX operator set. #### Attributes
equation : string (required)
Einsum expression string.
#### Inputs (1 - ∞)
Inputs (variadic, differentiable) : T
Operands
#### Outputs
Output (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to all numerical tensor types.
#### Examples
einsum_batch_diagonal ```python Eqn = "...ii ->...i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 5, 5) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal") ```
einsum_batch_matmul ```python Eqn = "bij, bjk -> bik" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5, 2, 3) Y = np.random.randn(5, 3, 4) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul") ```
einsum_inner_prod ```python Eqn = "i,i" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5) Y = np.random.randn(5) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod") ```
einsum_scalar ```python Eqn = "->" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.array(5.0) # scalar input Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_scalar") ```
einsum_sum ```python Eqn = "ij->i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum") ```
einsum_transpose ```python Eqn = "ij->ji" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Y = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose") ```
### **Elu** Elu takes one input data (Tensor) and produces one output data (Tensor) where the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Attributes
alpha : float (default is 1.0)
Coefficient of ELU.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
elu ```python node = onnx.helper.make_node("Elu", inputs=["x"], outputs=["y"], alpha=2.0) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-1.2642411, 0., 1.] y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu") ```
elu_default ```python default_alpha = 1.0 node = onnx.helper.make_node( "Elu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_elu_default") ```
### **Equal** Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 19 of the default ONNX operator set. Other versions of this operator: 1, 7, 11, 13 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(string)
Constrain input types to all (non-complex) tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
equal ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(3, 4, 5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal") x = (np.random.randn(3, 4, 5) * 10).astype(np.int8) y = (np.random.randn(3, 4, 5) * 10).astype(np.int8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int8") x = (np.random.randn(3, 4, 5) * 10).astype(np.int16) y = (np.random.randn(3, 4, 5) * 10).astype(np.int16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint64") ```
equal_broadcast ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast") ```
equal_string ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1", "string3"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string") ```
equal_string_broadcast ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string_broadcast") ```
### **Erf** Computes the error function of the given input tensor element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The error function of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
erf ```python node = onnx.helper.make_node( "Erf", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) y = np.vectorize(math.erf)(x).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_erf") ```
### **Exp** Calculates the exponential of the given input tensor, element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The exponential of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
exp ```python node = onnx.helper.make_node( "Exp", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.exp(x) # expected output [0.36787945, 1., 2.71828175] expect(node, inputs=[x], outputs=[y], name="test_exp_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.exp(x) expect(node, inputs=[x], outputs=[y], name="test_exp") ```
### **Expand** Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimensions must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 8 #### Inputs
input (differentiable) : T
Input tensor
shape (non-differentiable) : tensor(int64)
A 1-D tensor indicates the shape you want to expand to, following the broadcast rule
#### Outputs
output (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensors.
#### Examples
dim_changed ```python node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] new_shape = [2, 1, 6] expanded = data * np.ones(new_shape, dtype=np.float32) # print(expanded) # [[[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]], # # [[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_changed", ) ```
dim_unchanged ```python node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] new_shape = [3, 4] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] expanded = np.tile(data, 4) # print(expanded) # [[1., 1., 1., 1.], # [2., 2., 2., 2.], # [3., 3., 3., 3.]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_unchanged", ) ```
### **EyeLike** Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the 'dtype' argument. If 'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor. If not specified, the data type of the input tensor T1 is used.
k : int (default is 0)
(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.
#### Inputs
input : T1
2D input tensor to copy shape, and optionally, type information from.
#### Outputs
output : T2
Output tensor, same shape as input tensor T1.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain input types. Strings and complex are not supported.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(bool)
Constrain output types. Strings and complex are not supported.
#### Examples
populate_off_main_diagonal ```python shape = (4, 5) off_diagonal_offset = 1 node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], k=off_diagonal_offset, dtype=onnx.TensorProto.FLOAT, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32) expect( node, inputs=[x], outputs=[y], name="test_eyelike_populate_off_main_diagonal", ) ```
with_dtype ```python shape = (3, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.float64) expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype") ```
without_dtype ```python shape = (4, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype") ```
### **Flatten** Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 9, 11, 13, 21, 23, 24 #### Attributes
axis : int (default is 1)
Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
#### Inputs
input (differentiable) : T
A tensor of rank >= axis.
#### Outputs
output (differentiable) : T
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output to all tensor types up to IRv13.
#### Examples
flatten ```python shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(len(shape)): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i)) ```
flatten_negative_axis ```python shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(-len(shape), 0): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect( node, inputs=[a], outputs=[b], name="test_flatten_negative_axis" + str(abs(i)), ) ```
flatten_with_default_axis ```python node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], # Default value for axis: axis=1 ) shape = (5, 4, 3, 2) a = np.random.random_sample(shape).astype(np.float32) new_shape = (5, 24) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis") ```
### **Floor** Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
floor ```python node = onnx.helper.make_node( "Floor", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2, 2]).astype(np.float32) y = np.floor(x) # expected output [-2., 1., 2.] expect(node, inputs=[x], outputs=[y], name="test_floor_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.floor(x) expect(node, inputs=[x], outputs=[y], name="test_floor") ```
### **GRU** Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `z` - update gate * `r` - reset gate * `h` - hidden gate * `t` - time step (t-1 means previous time step) * `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates * `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates * `Wb[zrh]` - W bias vectors for update, reset, and hidden gates * `Rb[zrh]` - R bias vectors for update, reset, and hidden gates * `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates * `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates * `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates * `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha * x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha * Tanh(beta * x) * HardSigmoid(x) - min(max(alpha * x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha * (e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): * zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) * rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) * ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 * ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 * Ht = (1 - zt) (.) ht + zt (.) Ht-1 This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 3, 7, 14 #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
linear_before_reset : int (default is 0)
When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
#### Examples
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 6 number_of_gates = 3 weight_scale = 0.2 layout = 1 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_gru_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 5 weight_scale = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R) _, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_gru_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 3 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_with_initial_bias", ) ```
seq_length ```python input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype( np.float32 ) R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype( np.float32 ) # Adding custom bias W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_seq_length", ) ```
### **Gather** Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). It is an indexing operation that indexes into the input `data` along a single (specified) axis. Each entry in `indices` produces a `r-1` dimensional slice of the input tensor. The entire operation produces, conceptually, a `q`-dimensional tensor of `r-1` dimensional slices, which is arranged into a `q + (r-1)`-dimensional tensor, with the `q` dimensions taking the place of the original `axis` that is being indexed into. The following few examples illustrate how `Gather` works for specific shapes of `data`, `indices`, and given value of `axis`: | data shape | indices shape | axis | output shape | output equation | | --- | --- | --- | --- | --- | | (P, Q) | ( ) (a scalar) | 0 | (Q) | output[q] = data[indices, q] | | (P, Q, R) | ( ) (a scalar) | 1 | (P, R) | output[p, r] = data[p, indices, r] | | (P, Q) | (R, S) | 0 | (R, S, Q) | output[r, s, q] = data[ [indices[r, s], q] | | (P, Q) | (R, S) | 1 | (P, R, S) | output[p, r, s] = data[ p, indices[r, s]] | More generally, if `axis = 0`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` If `axis = 1`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of rank q + (r - 1).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
gather_0 ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=0) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_0", ) ```
gather_1 ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_1", ) ```
gather_2d_indices ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(3, 3).astype(np.float32) indices = np.array([[0, 2]]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_2d_indices", ) ```
gather_negative_indices ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.arange(10).astype(np.float32) indices = np.array([0, -9, -10]) y = np.take(data, indices, axis=0) # print(y) # [0. 1. 0.] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_negative_indices", ) ```
### **GatherElements** GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [1, 1], [4, 3], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [4, 8, 3], [7, 2, 3], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 11 #### Attributes
axis : int (default is 0)
Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of the same shape as indices.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
gather_elements_0 ```python axis = 1 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2], [3, 4]], dtype=np.float32) indices = np.array([[0, 0], [1, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[1, 1], # [4, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_0", ) ```
gather_elements_1 ```python axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[4, 8, 3], # [7, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_1", ) ```
gather_elements_negative_indices ```python axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[7, 5, 3], # [4, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_negative_indices", ) ```
### **GatherND** Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. **Example 1** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] ``` **Example 2** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] ``` **Example 3** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` **Example 4** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] ``` **Example 5** ``` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 11, 12 #### Attributes
batch_dims : int (default is 0)
The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
#### Outputs
output (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
#### Examples
float32 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32) indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_float32", ) ```
int32 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[0, 1], [2, 3]], dtype=np.int32) indices = np.array([[0, 0], [1, 1]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([0, 3], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32", ) ```
int32_batchdim_1 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], batch_dims=1, ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32) indices = np.array([[1], [0]], dtype=np.int64) output = gather_nd_impl(data, indices, 1) expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32_batch_dim1", ) ```
### **Gelu** Gelu takes one input data (Tensor) and produces one output data (Tensor) where the gaussian error linear units function, $y = 0.5 * x * (1 + erf(x/sqrt(2)))$ is applied to the tensor elementwise. If the attribute "approximate" is set to "tanh", the function estimation, $y = 0.5 * x * (1 + Tanh(sqrt(2/\pi) * (x + 0.044715 * x^3)))$ is used and applied to the tensor elementwise. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
approximate : string (default is none)
Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
gelu_default ```python node = onnx.helper.make_node("Gelu", inputs=["x"], outputs=["y"]) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.15865526, 0., 0.84134474] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.99595031, 3.99987331, 4.99999857] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_2") ```
gelu_tanh ```python node = onnx.helper.make_node( "Gelu", inputs=["x"], outputs=["y"], approximate="tanh" ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.158808, 0., 0.841192] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.9963627, 3.99993, 4.9999995] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_2") ```
### **Gemm** General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 * A' = transpose(A) if transA else A * B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [the doc](Broadcasting.md). This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 9, 11 #### Attributes
alpha : float (default is 1.0)
Scalar multiplier for the product of input tensors A * B.
beta : float (default is 1.0)
Scalar multiplier for input tensor C.
transA : int (default is 0)
Whether A should be transposed
transB : int (default is 0)
Whether B should be transposed
#### Inputs (2 - 3)
A (differentiable) : T
Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (differentiable) : T
Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (optional, differentiable) : T
Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
#### Outputs
Y (differentiable) : T
Output tensor of shape (M, N).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
Constrain input and output types to float/int tensors.
#### Examples
all_attributes ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.25, beta=0.35, transA=1, transB=1, ) a = np.random.ranf([4, 3]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.random.ranf([1, 5]).astype(np.float32) y = gemm_reference_implementation( a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35 ) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes") ```
alpha ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5 ) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, alpha=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha") ```
beta ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5 ) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, beta=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta") ```
default_matrix_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.random.ranf([3, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias" ) ```
default_no_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"]) a = np.random.ranf([2, 10]).astype(np.float32) b = np.random.ranf([10, 3]).astype(np.float32) y = gemm_reference_implementation(a, b) expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias") ```
default_scalar_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 3]).astype(np.float32) b = np.random.ranf([3, 4]).astype(np.float32) c = np.array(3.14).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias" ) ```
default_single_elem_vector_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 7]).astype(np.float32) b = np.random.ranf([7, 3]).astype(np.float32) c = np.random.ranf([1]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_single_elem_vector_bias", ) ```
default_vector_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias" ) ```
default_zero_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias") ```
transposeA ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1 ) a = np.random.ranf([6, 3]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transA=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA") ```
transposeB ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1 ) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([4, 6]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transB=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB") ```
### **GlobalAveragePool** GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
globalaveragepool ```python node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool") ```
globalaveragepool_precomputed ```python node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed") ```
### **GlobalLpPool** GlobalLpPool consumes an input tensor X and applies lp pool pooling across the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 2 #### Attributes
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **GlobalMaxPool** GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
globalmaxpool ```python node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool") ```
globalmaxpool_precomputed ```python node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[9]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool_precomputed") ```
### **Greater** Returns the tensor resulted from performing the `greater` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 7, 9 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
greater ```python node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64") ```
greater ```python node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64") ```
greater_broadcast ```python node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast") ```
greater_broadcast ```python node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast") ```
### **GreaterOrEqual** Returns the tensor resulted from performing the `greater_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. Other versions of this operator: 12 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **GridSample** Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from the `grid`. For spatial input `X` with shape (N, C, H, W), the `grid` will have shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). For volumetric input `X` with shape (N, C, D, H, W), the `grid` will have shape (N, D_out, H_out, W_out, 3), the output `Y` will have shape (N, C, D_out, H_out, W_out). More generally, for an input `X` of rank r+2 with shape (N, C, d1, d2, ..., dr), the `grid` will have shape (N, D1_out, D2_out, ..., Dr_out, r), the output `Y` will have shape (N, C, D1_out, D2_out, ..., Dr_out). The tensor `X` contains values at centers of square pixels (voxels, etc) locations such as (n, c, d1_in, d2_in, ..., dr_in). The (n, d1_out, d2_out, ..., dr_out, :) values from the tensor `grid` are the normalized positions for interpolating the values at the (n, c, d1_out, d2_out, ..., dr_out) locations from the output tensor `Y` using a specified interpolation method (the mode) and a padding mode (for `grid` positions falling outside the 2-dimensional image). For example, the values in `grid[n, h_out, w_out, :]` are size-2 vectors specifying normalized positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[n, c, h_out, w_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 16, 20 #### Attributes
align_corners : int (default is 0)
If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.
mode : string (default is linear)
Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.
padding_mode : string (default is zeros)
Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.
#### Inputs
X (differentiable) : T1
Input tensor of rank r+2 that has shape (N, C, D1, D2, ..., Dr), where N is the batch size, C is the number of channels, D1, D2, ..., Dr are the spatial dimensions.
grid (non-differentiable) : T2
Input offset of shape (N, D1_out, D2_out, ..., Dr_out, r), where D1_out, D2_out, ..., Dr_out are the spatial dimensions of the grid and output, and r is the number of spatial dimensions. Grid specifies the sampling locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If the grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode. Following computer vision convention, the coordinates in the length-r location vector are listed from the innermost tensor dimension to the outermost, the opposite of regular tensor indexing.
#### Outputs
Y (differentiable) : T1
Output tensor of rank r+2 that has shape (N, C, D1_out, D2_out, ..., Dr_out) of the sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input `X` and output `Y` types to all tensor types.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain grid types to float tensors.
#### Examples
gridsample ```python node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", padding_mode="zeros", align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] X = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ] ] ], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.6000, -1.0000], [-0.2000, -1.0000], [0.2000, -1.0000], [0.6000, -1.0000], [1.0000, -1.0000], ], [ [-1.0000, -0.6000], [-0.6000, -0.6000], [-0.2000, -0.6000], [0.2000, -0.6000], [0.6000, -0.6000], [1.0000, -0.6000], ], [ [-1.0000, -0.2000], [-0.6000, -0.2000], [-0.2000, -0.2000], [0.2000, -0.2000], [0.6000, -0.2000], [1.0000, -0.2000], ], [ [-1.0000, 0.2000], [-0.6000, 0.2000], [-0.2000, 0.2000], [0.2000, 0.2000], [0.6000, 0.2000], [1.0000, 0.2000], ], [ [-1.0000, 0.6000], [-0.6000, 0.6000], [-0.2000, 0.6000], [0.2000, 0.6000], [0.6000, 0.6000], [1.0000, 0.6000], ], [ [-1.0000, 1.0000], [-0.6000, 1.0000], [-0.2000, 1.0000], [0.2000, 1.0000], [0.6000, 1.0000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] Y = np.array( [ [ [ [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500], [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000], [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000], [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000], [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000], [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500], ] ] ], dtype=np.float32, ) expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample") ```
gridsample_mode_aligncorners ```python # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.5000, -0.5000], [-0.2000, -0.2000], [0.0000, 0.0000], ], [ [0.0000, 0.0000], [-0.2000, -0.2000], [0.5000, 0.5000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # setting mode = 'bilinear', default align_corners = 0 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear", ) # setting mode = 'bilinear', align_corners = 1 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_align_corners = np.array( [[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_align_corners], name="test_gridsample_aligncorners_true", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest" ) # setting mode = 'bicubic' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic" ) # ============================================================================ # Additional tests # The reference output tensors were generated using PyTorch 2.0. Grid = np.array( [ [ [[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]], [[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_0_additional_1", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_1_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_1_additional_1", ) # These two new bicubic tests produces slightly higher error ~5e-5 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [-0.173250, 0.284265, 1.923106, 2.568000], [5.170375, 2.284414, 4.744844, 1.046875], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [0.304001, 1.128750, 2.266270, 3.144844], [4.531500, 2.455360, 4.599819, 4.000000], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_1_additional_1", ) ```
gridsample_paddingmode ```python # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-10.0000, -10.0000], [-5.0000, -5.0000], [-0.2000, -0.2000], [10.0000, 10.0000], ], [ [10.0000, 10.0000], [-0.2000, -0.2000], [5.0000, 5.0000], [10.0000, 10.0000], ], ] ], dtype=np.float32, ) # setting padding_mode = 'zeros' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="zeros", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_zeros = np.array( [[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_zeros], name="test_gridsample_zeros_padding", ) # setting padding_mode = 'border' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="border", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_border = np.array( [[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_border], name="test_gridsample_border_padding", ) # setting padding_mode = 'reflection' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="reflection", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_reflection = np.array( [[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_reflection], name="test_gridsample_reflection_padding", ) ```
volumeetric_gridsample_mode_aligncorners ```python X = np.array( [ [ [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], [[9.0, 10.0], [11.0, 12.0]], ] ] ], dtype=np.float32, ) Grid = np.array( [ [ [ [[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]], [[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]], [[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]], [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]], [[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]], [[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]], [[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]], ], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [0.1250, 3.4000], [2.0000, 0.4500], [4.7000, 10.9000], [6.5000, 3.0000], ], [ [6.5000, 1.7500], [4.7000, 3.3000], [11.0000, 2.5200], [1.5000, 5.4900], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [1.0000, 6.7000], [3.7500, 2.4000], [5.4000, 9.3000], [6.5000, 6.0000], ], [ [6.5000, 7.0000], [5.4000, 6.6000], [9.2500, 8.4000], [12.0000, 6.1000], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_1", ) ```
### **GroupNormalization** A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494 This operator transforms input according to ``` y = scale * (x - mean) / sqrt(variance + epsilon) + bias, ``` where the mean and variance are computed per instance per group of channels, and `scale` and `bias` should be specified for each channel. The number of groups `num_groups` should be divisible by the number of channels so that there are an equal number of channels per group. The overall computation has two stages: the first stage normalizes the elements to have zero mean and unit variance for each instance in each group, and the second stage scales and shifts the results of the first stage. The floating-point precision used in the first stage is determined by the `stash_type` attribute. For example, if `stash_type` is 1, the operator casts all input variables to 32-bit float, performs the computation, and finally casts the normalized results back to the original type of `X`. The second stage does not depend on `stash_type`. When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. Other versions of this operator: 18 #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
num_groups : int (required)
The number of groups of channels. It should be a divisor of the number of channels `C`.
stash_type : int (default is 1)
The floating-point precision used in stage one of the computation.
#### Inputs
X (differentiable) : T
Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.
scale (differentiable) : T
Scale tensor of shape `(C)`.
bias (differentiable) : T
Bias tensor of shape `(C)`.
#### Outputs
Y (differentiable) : T
The output tensor of the same shape as `X`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
epsilon ```python c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) epsilon = 1e-2 y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], epsilon=epsilon, num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_epsilon", ) ```
groupnormalization ```python c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) y = _group_normalization(x, num_groups, scale, bias).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_example", ) ```
### **HammingWindow** Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Hamming window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
#### Examples
hammingwindow ```python # Test periodic window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow", ) # Test symmetric window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow_symmetric", ) ```
### **HannWindow** Generates a Hann window as described in the paper https://ieeexplore.ieee.org/document/1455106. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
periodic : int (default is 1)
If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
#### Inputs
size (non-differentiable) : T1
A scalar value indicating the length of the window.
#### Outputs
output (non-differentiable) : T2
A Hann window with length: size. The output has the shape: [size].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain the input size to int64_t.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output types to numeric tensors.
#### Examples
hannwindow ```python # Test periodic window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow" ) # Test symmetric window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow_symmetric", ) ```
### **HardSigmoid** HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Attributes
alpha : float (default is 0.2)
Value of alpha.
beta : float (default is 0.5)
Value of beta.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
hardsigmoid ```python node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6 ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.] expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid") ```
hardsigmoid_default ```python default_alpha = 0.2 default_beta = 0.5 node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * default_alpha + default_beta, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default") ```
### **HardSwish** HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 14 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
hardswish ```python node = onnx.helper.make_node( "HardSwish", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = hardswish(x) expect(node, inputs=[x], outputs=[y], name="test_hardswish") ```
### **Hardmax** The operator computes the hardmax values for the given input: Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise The "axis" attribute indicates the dimension along which Hardmax will be performed. The output tensor has the same shape and contains the Hardmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
axis : int (default is -1)
Describes the dimension Hardmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
hardmax ```python node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype( np.float32 ) # expect result: # [[1. 0. 0. 0.] # [0. 1. 0. 0.] # [0. 0. 1. 0.] # [0. 0. 0. 1.]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_example") # For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output x = np.array([[3, 3, 3, 1]]).astype(np.float32) # expect result: # [[1, 0, 0, 0]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot") ```
hardmax_axis ```python x = np.random.randn(3, 4, 5).astype(np.float32) node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=0, ) y = hardmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=1, ) y = hardmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=2, ) y = hardmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=-1, ) y = hardmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis") ```
### **Identity** Identity operator #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 13, 14, 16, 19, 21, 23, 24 #### Inputs
input (differentiable) : V
Input tensor
#### Outputs
output (differentiable) : V
Tensor to copy input into.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain input and output types to all tensor, sequence, and optional types.
#### Examples
identity ```python node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) expect(node, inputs=[data], outputs=[data], name="test_identity") ```
identity_opt ```python ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) identity_node = onnx.helper.make_node( "Identity", inputs=["opt_in"], outputs=["opt_out"] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] expect( identity_node, inputs=[x], outputs=[x], name="test_identity_opt", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[opt_in_tp], output_type_protos=[opt_in_tp], ) ```
sequence ```python node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = [ np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ), np.array( [ [ [ [2, 3], [1, 5], ] ] ], dtype=np.float32, ), ] expect(node, inputs=[data], outputs=[data], name="test_identity_sequence") ```
### **If** If conditional #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 11, 13, 16, 19, 21, 23, 24 #### Attributes
else_branch : graph (required)
Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required)
Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
#### Inputs
cond : B
Condition for the if. The tensor must contain a single element.
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : V
Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), seq(tensor(uint2)), seq(tensor(int2)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0)), optional(tensor(uint2)), optional(tensor(int2))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv13.
B : tensor(bool)
Only bool
#### Examples
if ```python # Given a bool scalar input cond. # return constant tensor x if cond is True, otherwise return constant tensor y. then_out = onnx.helper.make_tensor_value_info( "then_out", onnx.TensorProto.FLOAT, [5] ) else_out = onnx.helper.make_tensor_value_info( "else_out", onnx.TensorProto.FLOAT, [5] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([5, 4, 3, 2, 1]).astype(np.float32) then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["then_out"], value=onnx.numpy_helper.from_array(x), ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["else_out"], value=onnx.numpy_helper.from_array(y), ) then_body = onnx.helper.make_graph( [then_const_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if", opset_imports=[onnx.helper.make_opsetid("", 11)], ) ```
if_optional ```python # Given a bool scalar input cond, return an empty optional sequence of # tensor if True, return an optional sequence with value x # (the input optional sequence) otherwise. ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) then_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp) then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp) then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp) else_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp) else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp) else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] cond = np.array(0).astype(bool) res = compute_if_outputs(x, cond) opt_empty_in = onnx.helper.make_node( "Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp ) then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out]) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["else_seq"] ) else_optional_seq_node = onnx.helper.make_node( "Optional", inputs=["else_seq"], outputs=["else_opt"] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node, else_optional_seq_node], "else_body", [], [else_out], ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) expect( if_node, inputs=[cond], outputs=[res], name="test_if_opt", output_type_protos=[else_out_opt_tp], opset_imports=[onnx.helper.make_opsetid("", 16)], ) ```
if_seq ```python # Given a bool scalar input cond. # return constant sequence x if cond is True, otherwise return constant sequence y. then_out = onnx.helper.make_tensor_sequence_value_info( "then_out", onnx.TensorProto.FLOAT, shape=[5] ) else_out = onnx.helper.make_tensor_sequence_value_info( "else_out", onnx.TensorProto.FLOAT, shape=[5] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)] then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) then_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["then_out"] ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["y"], value=onnx.numpy_helper.from_array(y[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["y"], outputs=["else_out"] ) then_body = onnx.helper.make_graph( [then_const_node, then_seq_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
### **ImageDecoder** Loads and decodes and image from a file. If it can't decode for any reason (e.g. corrupted encoded stream, invalid format, it will return an empty matrix). The following image formats are supported: * BMP * JPEG (note: Lossless JPEG support is optional) * JPEG2000 * TIFF * PNG * WebP * Portable image format (PBM, PGM, PPM, PXM, PNM) Decoded images follow a channel-last layout: (Height, Width, Channels). **JPEG chroma upsampling method:** When upsampling the chroma components by a factor of 2, the pixels are linearly interpolated so that the centers of the output pixels are 1/4 and 3/4 of the way between input pixel centers. When rounding, 0.5 is rounded down and up at alternative pixels locations to prevent bias towards larger values (ordered dither pattern). Considering adjacent input pixels A, B, and C, B is upsampled to pixels B0 and B1 so that ``` B0 = round_half_down((1/4) * A + (3/4) * B) B1 = round_half_up((3/4) * B + (1/4) * C) ``` This method, is the default chroma upsampling method in the well-established libjpeg-turbo library, also referred as "smooth" or "fancy" upsampling. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
pixel_format : string (default is RGB)
Pixel format. Can be one of "RGB", "BGR", or "Grayscale".
#### Inputs
encoded_stream (non-differentiable) : T1
Encoded stream
#### Outputs
image (non-differentiable) : T2
Decoded image
#### Type Constraints
T1 : tensor(uint8)
Constrain input types to 8-bit unsigned integer tensor.
T2 : tensor(uint8)
Constrain output types to 8-bit unsigned integer tensor.
#### Examples
image_decoder_decode_bmp_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "bmp", _image_decoder_data.image_decoder_decode_bmp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_bmp_rgb", ) ```
image_decoder_decode_jpeg2k_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg2000", _image_decoder_data.image_decoder_decode_jpeg2k_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg2k_rgb", ) ```
image_decoder_decode_jpeg_bgr ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="BGR", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_bgr, "BGR" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_bgr", ) ```
image_decoder_decode_jpeg_grayscale ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="Grayscale", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_grayscale, "Grayscale" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_grayscale", ) ```
image_decoder_decode_jpeg_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_rgb", ) ```
image_decoder_decode_png_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "png", _image_decoder_data.image_decoder_decode_png_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_png_rgb", ) ```
image_decoder_decode_pnm_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "ppm", _image_decoder_data.image_decoder_decode_pnm_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_pnm_rgb", ) ```
image_decoder_decode_tiff_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "tiff", _image_decoder_data.image_decoder_decode_tiff_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_tiff_rgb", ) ```
image_decoder_decode_webp_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "webp", _image_decoder_data.image_decoder_decode_webp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_webp_rgb", ) ```
### **InstanceNormalization** Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Attributes
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
#### Inputs
input (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale (differentiable) : T
The input 1-dimensional scale tensor of size C.
B (differentiable) : T
The input 1-dimensional bias tensor of size C.
#### Outputs
output (differentiable) : T
The output tensor of the same shape as input.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
instancenormalization ```python def _instancenorm_test_mode(x, s, bias, epsilon=1e-5): # type: ignore dims_x = len(x.shape) axis = tuple(range(2, dims_x)) mean = np.mean(x, axis=axis, keepdims=True) var = np.var(x, axis=axis, keepdims=True) dim_ones = (1,) * (dims_x - 2) s = s.reshape(-1, *dim_ones) bias = bias.reshape(-1, *dim_ones) return s * (x - mean) / np.sqrt(var + epsilon) + bias # input size: (1, 2, 1, 3) x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32) s = np.array([1.0, 1.5]).astype(np.float32) bias = np.array([0, 1]).astype(np.float32) y = _instancenorm_test_mode(x, s, bias).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], ) # output size: (1, 2, 1, 3) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example") # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) epsilon = 1e-2 y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon") ```
### **IsInf** Map infinity to true and other values to false. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. Other versions of this operator: 10 #### Attributes
detect_negative : int (default is 1)
(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.
detect_positive : int (default is 1)
(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.
#### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
#### Examples
infinity ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf") ```
infinity_float16 ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_float16") ```
negative_infinity_only ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_positive=0 ) x = np.array([-1.7, np.nan, np.inf, -3.6, -np.inf, np.inf], dtype=np.float32) y = np.isneginf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_negative") ```
positive_infinity_only ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_negative=0 ) x = np.array([-1.7, np.nan, np.inf, 3.6, -np.inf, np.inf], dtype=np.float32) y = np.isposinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_positive") ```
### **IsNaN** Returns which elements of the input are NaN. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. Other versions of this operator: 9, 13 #### Inputs
X (non-differentiable) : T1
input
#### Outputs
Y (non-differentiable) : T2
output
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
Constrain input types to float tensors.
T2 : tensor(bool)
Constrain output types to boolean tensors.
#### Examples
float16 ```python node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan_float16") ```
isnan ```python node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan") ```
### **LRN** Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element `X[n, c, d1, ..., dk]` in a tensor of shape `(N x C x D1 x D2, ..., Dk)`, its region is `{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}`. `square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)`, where `max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))`. `Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
alpha : float (default is 0.0001)
Scaling parameter.
beta : float (default is 0.75)
The exponent.
bias : float (default is 1.0)
size : int (required)
The number of channels to sum over
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs
Y (differentiable) : T
Output tensor, which has the shape and type as input tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
default ```python alpha = 0.0001 beta = 0.75 bias = 1.0 nsize = 3 node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn_default") ```
lrn ```python alpha = 0.0002 beta = 0.5 bias = 2.0 nsize = 3 node = onnx.helper.make_node( "LRN", inputs=["x"], outputs=["y"], alpha=alpha, beta=beta, bias=bias, size=nsize, ) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn") ```
### **LSTM** Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `o` - output gate * `f` - forget gate * `c` - cell gate * `t` - time step (t-1 means previous time step) * `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates * `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates * `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates * `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates * `P[iof]` - P peephole weight vector for input, output, and forget gates * `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates * `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates * `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates * `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates * `PB[iof]` - P peephole weight vector for backward input, output, and forget gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): * it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) * ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) * ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) * Ct = ft (.) Ct-1 + it (.) ct * ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) * Ht = ot (.) h(Ct) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 7, 14 #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
input_forget : int (default is 0)
Couple the input and forget gates if 1.
layout : int (default is 0)
The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 8)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional, non-differentiable) : T
Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional, differentiable) : T
The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
#### Outputs (0 - 3)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional, differentiable) : T
The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
#### Examples
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 7 weight_scale = 0.3 number_of_gates = 4 layout = 1 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_lstm_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R) _, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_lstm_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 4 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), 1) lstm = LSTMHelper(X=input, W=W, R=R, B=B) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_initial_bias", ) ```
peepholes ```python input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype( np.float32 ) input_size = 4 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 number_of_peepholes = 3 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"], outputs=["", "Y_h"], hidden_size=hidden_size, ) # Initializing Inputs W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32) seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32) init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype( np.float32 ) lstm = LSTMHelper( X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h ) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_peepholes", ) ```
### **LayerNormalization** This is layer normalization defined in ONNX as function. The overall computation can be split into two stages. The first stage is standardization, which makes the normalized elements have zero mean and unit variances. The computation required by standardization can be described by the following equations. ``` Mean = ReduceMean(X) D = Sub(X, Mean) DD = Mul(D, D) Var = ReduceMean(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(D, InvStdDev) ``` where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `Var` and `StdDev` stand for variance and standard deviation, respectively. The second output is `Mean` and the last one is `InvStdDev`. Depending on `stash_type` attribute, the actual computation must happen in different floating-point precision. For example, if `stash_type` is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast `Normalized` back to the original type of `X`. The second stage then scales and shifts the outcome of the first stage using ``` NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B) ``` The second stage doesn't depends on `stash_type`. All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md). The same variable (i.e., input, output, and attribute) uses the same name in the equations above and this operator's definition. Let `d[i]` indicate the i-th dimension of `X`. If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`, the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`. `Y` and `X` have the same shape. This operator supports unidirectional broadcasting (tensors `Scale` and `B` should be unidirectional broadcastable to tensor `X`); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
stash_type : int (default is 1)
Type of Mean and InvStdDev. This also specifies stage one's computation precision.
#### Inputs (2 - 3)
X : T
Tensor to be normalized.
Scale : T
Scale tensor.
B (optional) : T
Bias tensor.
#### Outputs (1 - 3)
Y : T
Normalized tensor.
Mean (optional) : U
Saved mean used during training to speed up gradient computation
InvStdDev (optional) : U
Saved inverse standard deviation used during training to speed up gradient computation.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types and output Y type to float tensors.
U : tensor(float), tensor(bfloat16)
Type of Mean and InvStdDev tensors.
#### Examples
d ```python X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_2d_axis_negative_{-axis}" else: name = f"test_layer_normalization_2d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
d_epsilon ```python epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_layer_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
default_axis ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in LayerNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y, mean, inv_std_dev = _layer_normalization(X, W, B) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], ) expect( node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name="test_layer_normalization_default_axis", ) ```
layernormalization ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_4d_axis_negative_{-axis}" else: name = f"test_layer_normalization_4d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
### **LeakyRelu** LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor elementwise. #### Version This version of the operator has been available since version 16 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Attributes
alpha : float (default is 0.01)
Coefficient of leakage.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
leakyrelu ```python node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], alpha=0.1 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.1, 0., 1.] y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu") ```
leakyrelu_default ```python default_alpha = 0.01 node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default") ```
### **Less** Returns the tensor resulted from performing the `less` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 7, 9 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
less ```python node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64") ```
less ```python node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64") ```
less_broadcast ```python node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast") ```
less_broadcast ```python node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast") ```
### **LessOrEqual** Returns the tensor resulted from performing the `less_equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. Other versions of this operator: 12 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input types to all numeric tensors.
T1 : tensor(bool)
Constrain output to boolean tensor.
### **Log** Calculates the natural log of the given input tensor, element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The natural log of the input tensor computed element-wise
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
log ```python node = onnx.helper.make_node( "Log", inputs=["x"], outputs=["y"], ) x = np.array([1, 10]).astype(np.float32) y = np.log(x) # expected output [0., 2.30258512] expect(node, inputs=[x], outputs=[y], name="test_log_example") x = np.exp(np.random.randn(3, 4, 5).astype(np.float32)) y = np.log(x) expect(node, inputs=[x], outputs=[y], name="test_log") ```
### **LogSoftmax** The operator computes the log of softmax values for the given input: LogSoftmax(input, axis) = Log(Softmax(input, axis=axis)) The "axis" attribute indicates the dimension along which LogSoftmax will be performed. The output tensor has the same shape and contains the LogSoftmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
axis : int (default is -1)
Describes the dimension LogSoftmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
logsoftmax ```python node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output # [[-2.4076061 -1.407606 -0.407606 ]] y = logsoftmax(x) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1") ```
logsoftmax_axis ```python x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[-3.4401896 -2.4401896 -1.4401896 -0.44018966] # [-3.4401896 -2.4401896 -1.4401896 -0.44018966]] y = logsoftmax(x) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=0, ) y = logsoftmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=1, ) y = logsoftmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=2, ) y = logsoftmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=-1, ) y = logsoftmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis") ```
### **Loop** Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 11, 13, 16, 19, 21, 23, 24 #### Attributes
body : graph (required)
The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
#### Inputs (2 - ∞)
M (optional) : I
A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B
A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V
The initial values of any loop-carried dependencies (values that change across loop iterations)
#### Outputs (1 - ∞)
v_final_and_scan_outputs (variadic, heterogeneous) : V
Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(uint4)), seq(tensor(int4)), seq(tensor(float4e2m1)), seq(tensor(float8e8m0)), seq(tensor(uint2)), seq(tensor(int2)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), optional(tensor(float8e4m3fn)), optional(tensor(float8e4m3fnuz)), optional(tensor(float8e5m2)), optional(tensor(float8e5m2fnuz)), optional(tensor(uint4)), optional(tensor(int4)), optional(tensor(float4e2m1)), optional(tensor(float8e8m0)), optional(tensor(uint2)), optional(tensor(int2))
All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv13.
I : tensor(int64)
tensor of int64, which should be a scalar.
B : tensor(bool)
tensor of bool, which should be a scalar.
#### Examples
loop_11 ```python # Given a tensor x of values [x1, ..., xN], and initial tensor y # sum up its elements using a scan # returning the final state (y+x1+x2+...+xN) as well the scan_output # [y+x1, y+x1+x2, ..., y+x1+x2+...+xN] y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1]) y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1]) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [1] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([-2]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) i_add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) start_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) y_add_node = onnx.helper.make_node( "Add", inputs=["y_in", "slice_out"], outputs=["y_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) scan_identity_node = onnx.helper.make_node( "Identity", inputs=["y_out"], outputs=["scan_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, i_add_node, start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node, scan_identity_node, ], "loop_body", [iter_count, cond_in, y_in], [cond_out, y_out, scan_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "y"], outputs=["res_y", "res_scan"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) res_y = np.array([13]).astype(np.float32) cond = np.array(1).astype(bool) res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1)) expect( node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan], name="test_loop11", opset_imports=[onnx.helper.make_opsetid("", 11)], ) ```
loop_13 ```python # Given a tensor x of values [x1, ..., xN], # Return a sequence of tensors of # [[x1], [x1, x2], ..., [x1, ..., xN]] seq_in = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, None ) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, None ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, seq_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "seq_empty"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) seq_empty: list[Any] = [] seq_res = [x[: int(i)] for i in x] cond = np.array(1).astype(bool) expect( node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res], name="test_loop13_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) ), ], ) ```
loop_16_none ```python # Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0], # Return a concatenated sequence of tensors of # [x0, [x1], [x1, x2], ..., [x1, ..., xN]] ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, [] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x0 = np.array(0).astype(np.float32) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) optional_has_elem_node = onnx.helper.make_node( "OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"] ) optional_is_none = onnx.helper.make_node( "Not", inputs=["optional_has_elem"], outputs=["optional_is_none"] ) optional_get_elem = onnx.helper.make_node( "OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"] ) constant_in = onnx.helper.make_node( "Constant", inputs=[], outputs=["constant_in"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0] ), ) seq_const_in = onnx.helper.make_node( "SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"] ) then_seq_out = onnx.helper.make_tensor_sequence_value_info( "init_seq_in", onnx.TensorProto.FLOAT, [] ) then_body = onnx.helper.make_graph( [constant_in, seq_const_in], "then_body", [], [then_seq_out] ) else_seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, [] ) else_body = onnx.helper.make_graph( [optional_get_elem], "else_body", [], [else_seq_out] ) if_node = onnx.helper.make_node( "If", inputs=["optional_is_none"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, opt_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "opt_seq"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) cond = np.array(1).astype(bool) seq_res = compute_loop_outputs(x, [x0], trip_count) opt_seq_in: list[Any] = [x0] expect( node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res], name="test_loop16_seq_none", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), opt_in_tp, ], ) ```
### **LpNormalization** Given a matrix, apply Lp-normalization along the provided axis. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
axis : int (default is -1)
The axis on which to apply normalization, -1 mean last axis.
p : int (default is 2)
The order of the normalization, only 1 or 2 are supported.
#### Inputs
input (differentiable) : T
Input matrix
#### Outputs
output (differentiable) : T
Matrix after normalization
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
default ```python node = onnx.helper.make_node("LpNormalization", inputs=["x"], outputs=["y"]) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) lp_norm_default = np.sqrt(np.sum(x**2, axis=-1, keepdims=True)) y = x / lp_norm_default expect(node, inputs=[x], outputs=[y], name="test_lpnormalization_default") ```
l1normalization_axis_0 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=1 ) x = np.array([3.0, 4.0], dtype=np.float32) l1_norm_axis_0 = np.sum(abs(x), axis=0, keepdims=True) y = x / l1_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_0") ```
l1normalization_axis_1 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=1 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l1_norm_axis_1 = np.sum(abs(x), axis=1, keepdims=True) y = x / l1_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_1") ```
l1normalization_axis_last ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=-1, p=1 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l1_norm_axis_last = np.sum(abs(x), axis=-1, keepdims=True) y = x / l1_norm_axis_last expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_last") ```
l2normalization_axis_0 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=2 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l2_norm_axis_0 = np.sqrt(np.sum(x**2, axis=0, keepdims=True)) y = x / l2_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_0") ```
l2normalization_axis_1 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=2 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l2_norm_axis_1 = np.sqrt(np.sum(x**2, axis=1, keepdims=True)) y = x / l2_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_1") ```
### **LpPool** LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 2, 11, 18 #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
p : int (default is 2)
p value of the Lp norm used to pool over the input data.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
#### Outputs
Y (differentiable) : T
Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
lppool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ p = 3 kernel_shape = [2] strides = [1] node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, p=p, ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_1d_default") ```
lppool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_default") ```
lppool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], p=p, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [14.560219778561036, 16.24807680927192], [21.633307652783937, 23.49468024894146], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_dilations") ```
lppool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], p=p, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads_required=extra_pads, pads=pads, p=p, ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads") ```
lppool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower") ```
lppool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper") ```
lppool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_strides") ```
lppool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_3d_default") ```
### **MatMul** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 9 #### Inputs
A (differentiable) : T
N-dimensional matrix A
B (differentiable) : T
N-dimensional matrix B
#### Outputs
Y (differentiable) : T
Matrix multiply results from A * B
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
Constrain input and output types to float/int tensors.
#### Examples
matmul ```python node = onnx.helper.make_node( "MatMul", inputs=["a", "b"], outputs=["c"], ) # 2d a = np.random.randn(3, 4).astype(np.float32) b = np.random.randn(4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d") # 3d a = np.random.randn(2, 3, 4).astype(np.float32) b = np.random.randn(2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d") # 4d a = np.random.randn(1, 2, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d") # broadcasting a = np.random.randn(3, 1, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 2).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_bcast") # 1d + 3d a = np.random.randn(4).astype(np.float32) b = np.random.randn(2, 4, 1).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_3d") # 3d + 1d a = np.random.randn(1, 2, 4, 3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d_1d") # 1d + 1d a = np.random.randn(3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_1d") ```
### **MatMulInteger** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Inputs (2 - 4)
A (non-differentiable) : T1
N-dimensional matrix A
B (non-differentiable) : T2
N-dimensional matrix B
a_zero_point (optional, non-differentiable) : T1
Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1].
b_zero_point (optional, non-differentiable) : T2
Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N].
#### Outputs
Y (non-differentiable) : T3
Matrix multiply results from A * B
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input A data type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain input B data type to 8-bit integer tensor.
T3 : tensor(int32)
Constrain output Y data type as 32-bit integer tensor.
#### Examples
matmulinteger ```python node = onnx.helper.make_node( "MatMulInteger", inputs=["A", "B", "a_zero_point", "b_zero_point"], outputs=["Y"], ) A = np.array( [ [11, 7, 3], [10, 6, 2], [9, 5, 1], [8, 4, 0], ], dtype=np.uint8, ) a_zero_point = np.array([12], dtype=np.uint8) B = np.array( [ [1, 4], [2, 5], [3, 6], ], dtype=np.uint8, ) b_zero_point = np.array([0], dtype=np.uint8) output = np.array( [ [-38, -83], [-44, -98], [-50, -113], [-56, -128], ], dtype=np.int32, ) expect( node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output], name="test_matmulinteger", ) ```
### **Max** Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 8, 12 #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for max.
#### Outputs
max (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
max ```python data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 3]).astype(np.float32) result = np.array([3, 5, 4]).astype(np.float32) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_max_example", ) node = onnx.helper.make_node( "Max", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input") result = np.maximum(data_0, data_1) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs" ) ```
max_all_numeric_types ```python for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([3, 4, 4]).astype(op_dtype) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_max_{np.dtype(op_dtype).name}", ) ```
### **MaxPool** MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is maximum number of elements exclude pad. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 8, 10, 11, 12 #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode : int (default is 0)
Whether to use ceil or floor (default) to compute the output shape.
dilations : list of ints
Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
storage_order : int (default is 0)
The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
#### Outputs (1 - 2)
Y (differentiable) : T
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Indices (optional, non-differentiable) : I
Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)
Constrain input and output types to float and 8 bit tensors.
I : tensor(int64)
Constrain index tensor to int64
#### Examples
maxpool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default") ```
maxpool_2d_ceil ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil") ```
maxpool_2d_ceil_output_size_reduce_by_one ```python """input_shape: [1, 1, 2, 2] output_shape: [1, 1, 1, 1] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[1, 1], strides=[2, 2], ceil_mode=True, ) x = np.array([[[[1, 2], [3, 4]]]]).astype(np.float32) y = np.array([[[[1]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil_output_size_reduce_by_one", ) ```
maxpool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default") ```
maxpool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations") ```
maxpool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads") ```
maxpool_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads") ```
maxpool_2d_precomputed_same_upper ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper" ) ```
maxpool_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2] ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides" ) ```
maxpool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower") ```
maxpool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper") ```
maxpool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3] ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides") ```
maxpool_2d_uint8 ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.uint8) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.uint8) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8") ```
maxpool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default") ```
maxpool_3d_dilations ```python """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[11, 12], [15, 16]], [[11, 12], [15, 16]]]]]).astype( np.float32 ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations") ```
maxpool_3d_dilations_use_ref_impl ```python """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ dilations = [2, 2, 2] kernel_shape = [2, 2, 2] strides = [1, 1, 1] ceil_mode = False node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=dilations, ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) x_shape = x.shape[2:] out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = x y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl" ) ```
maxpool_3d_dilations_use_ref_impl_large ```python x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) ceil_mode = True node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = np.pad( x, ( (0, 0), (0, 0), (pads[0], pads[3]), (pads[1], pads[4]), (pads[2], pads[5]), ), mode="constant", constant_values=0, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl_large", ) ```
maxpool_with_argmax_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) z = np.array( [ [ [ [12, 13, 14, 14, 14], [17, 18, 19, 19, 19], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], ] ] ] ).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_pads", ) ```
maxpool_with_argmax_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[2, 2], strides=[2, 2], storage_order=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_strides", ) ```
### **MaxRoiPool** ROI max pool consumes an input tensor X and region of interests (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
pooled_shape : list of ints (required)
ROI pool output shape (height, width).
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
#### Inputs
X (differentiable) : T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois (non-differentiable) : T
RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].
#### Outputs
Y (differentiable) : T
RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
### **MaxUnpool** MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corresponding pooling op that the unpooling op is trying to invert. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9, 11 #### Attributes
kernel_shape : list of ints (required)
The size of the kernel along each axis.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (2 - 3)
X (differentiable) : T1
Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
I (non-differentiable) : T2
Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).
output_shape (optional, non-differentiable) : T2
The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.
#### Outputs
output (differentiable) : T1
Output data tensor that contains the result of the unpooling.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T2 : tensor(int64)
Constrain index tensor to int64
#### Examples
with_output_shape ```python node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI", "output_shape"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) output_shape = np.array((1, 1, 5, 5), dtype=np.int64) y = np.array( [ [ [ [0, 0, 0, 0, 0], [0, 5, 0, 6, 0], [0, 0, 0, 0, 0], [0, 7, 0, 8, 0], [0, 0, 0, 0, 0], ] ] ], dtype=np.float32, ) expect( node, inputs=[xT, xI, output_shape], outputs=[y], name="test_maxunpool_export_with_output_shape", ) ```
without_output_shape ```python node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) y = np.array( [[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]], dtype=np.float32, ) expect( node, inputs=[xT, xI], outputs=[y], name="test_maxunpool_export_without_output_shape", ) ```
### **Mean** Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 8 #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for mean.
#### Outputs
mean (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
mean ```python data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([2, 3, 4]).astype(np.float32) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_mean_example", ) node = onnx.helper.make_node( "Mean", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input") result = np.divide(np.add(data_0, data_1), 2.0) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs" ) ```
### **MeanVarianceNormalization** A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: `(X-EX)/sqrt(E(X-EX)^2)` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 9 #### Attributes
axes : list of ints (default is ['0', '2', '3'])
A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
meanvariancenormalization ```python node = onnx.helper.make_node( "MeanVarianceNormalization", inputs=["X"], outputs=["Y"] ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1) data_mean_squared = np.power(data_mean, 2) data_squared = np.power(input_data, 2) data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1) std = np.sqrt(data_squared_mean - data_mean_squared) expected_output = (input_data - data_mean) / (std + 1e-9) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn") ```
### **MelWeightMatrix** Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale. This function defines the mel scale in terms of a frequency in hertz according to the following formula: mel(f) = 2595 * log10(1 + f/700) In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0. The returned MelWeightMatrix can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram" M of shape [frames, num_mel_bins]. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
output_datatype : int (default is 1)
The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT.
#### Inputs
num_mel_bins (non-differentiable) : T1
The number of bands in the mel spectrum.
dft_length (non-differentiable) : T1
The size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.
sample_rate (non-differentiable) : T1
Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.
lower_edge_hertz (non-differentiable) : T2
Lower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.
upper_edge_hertz (non-differentiable) : T2
The desired top edge of the highest frequency band.
#### Outputs
output (non-differentiable) : T3
The Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].
#### Type Constraints
T1 : tensor(int32), tensor(int64)
Constrain to integer tensors.
T2 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
Constrain to float tensors
T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain to any numerical types.
#### Examples
melweightmatrix ```python node = onnx.helper.make_node( "MelWeightMatrix", inputs=[ "num_mel_bins", "dft_length", "sample_rate", "lower_edge_hertz", "upper_edge_hertz", ], outputs=["output"], ) num_mel_bins = np.int32(8) dft_length = np.int32(16) sample_rate = np.int32(8192) lower_edge_hertz = np.float32(0) upper_edge_hertz = np.float32(8192 / 2) num_spectrogram_bins = dft_length // 2 + 1 frequency_bins = np.arange(0, num_mel_bins + 2) low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700) high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700) mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0] frequency_bins = frequency_bins * mel_step + low_frequency_mel frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1) frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate frequency_bins = frequency_bins.astype(int) output = np.zeros((num_spectrogram_bins, num_mel_bins)) output.flags.writeable = True for i in range(num_mel_bins): lower_frequency_value = frequency_bins[i] # left center_frequency_point = frequency_bins[i + 1] # center higher_frequency_point = frequency_bins[i + 2] # right low_to_center = center_frequency_point - lower_frequency_value if low_to_center == 0: output[center_frequency_point, i] = 1 else: for j in range(lower_frequency_value, center_frequency_point + 1): output[j, i] = float(j - lower_frequency_value) / float( low_to_center ) center_to_high = higher_frequency_point - center_frequency_point if center_to_high > 0: for j in range(center_frequency_point, higher_frequency_point): output[j, i] = float(higher_frequency_point - j) / float( center_to_high ) # Expected output # 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, output = output.astype(np.float32) expect( node, inputs=[ num_mel_bins, dft_length, sample_rate, lower_edge_hertz, upper_edge_hertz, ], outputs=[output], name="test_melweightmatrix", ) ```
### **Min** Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 8, 12 #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for min.
#### Outputs
min (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
min ```python data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 0]).astype(np.float32) result = np.array([1, 2, 0]).astype(np.float32) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_min_example", ) node = onnx.helper.make_node( "Min", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input") result = np.minimum(data_0, data_1) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs" ) ```
min_all_numeric_types ```python for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([1, 2, 1]).astype(op_dtype) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_min_{np.dtype(op_dtype).name}", ) ```
### **Mish** Mish: A Self Regularized Non-Monotonic Neural Activation Function. Perform the linear unit element-wise on the input tensor X using formula: ``` mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x})) ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 18 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input X and output types to float tensors.
#### Examples
mish ```python node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"]) input_data = np.linspace(-10, 10, 10000, dtype=np.float32) # Calculate expected output data expected_output = input_data * np.tanh(np.log1p(np.exp(input_data))) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish") ```
### **Mod** Performs an element-wise binary modulo operation. The semantics and supported data types depend on the value of the `fmod` attribute which must be `0` (default), or `1`. If the `fmod` attribute is set to `0`, `T` is constrained to integer data types and the semantics follow that of the Python `%`-operator. The sign of the result is that of the divisor. If `fmod` is set to `1`, the behavior of this operator follows that of the `fmod` function in C and `T` is constrained to floating point data types. The result of this operator is the remainder of the division operation `x / y` where `x` and `y` are respective elements of `A` and `B`. The result is exactly the value `x - n * y`, where `n` is `x / y` with its fractional part truncated. The returned value has the same sign as `x` (except if `x` is `-0`) and is less or equal to `|y|` in magnitude. The following special cases apply when `fmod` is set to `1`: - If `x` is `-0` and `y` is greater than zero, either `+0` or `-0` may be returned. - If `x` is `±∞` and `y` is not `NaN`, `NaN` is returned. - If `y` is `±0` and `x` is not `NaN`, `NaN` should be returned. - If `y` is `±∞` and `x` is finite, `x` is returned. - If either argument is `NaN`, `NaN` is returned. This operator supports **multidirectional (i.e., NumPy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 10 #### Attributes
fmod : int (default is 0)
Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment
#### Inputs
A (differentiable) : T
Dividend tensor
B (non-differentiable) : T
Divisor tensor
#### Outputs
C (differentiable) : T
Remainder tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to high-precision numeric tensors.
#### Examples
mod_broadcast ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32) y = np.array([7]).astype(np.int32) z = np.mod(x, y) # array([[[0, 1, 2, 3, 4], # [5, 6, 0, 1, 2]], # [[3, 4, 5, 6, 0], # [1, 2, 3, 4, 5]], # [[6, 0, 1, 2, 3], # [4, 5, 6, 0, 1]]], dtype=int32) expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast") ```
mod_int64_fmod ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.fmod(x, y) # expected output [ 0, 1, 5, 0, -1, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod") ```
mod_mixed_sign_float16 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16) z = np.fmod( x, y ) # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 , 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16") ```
mod_mixed_sign_float32 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32) z = np.fmod( x, y ) # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32") ```
mod_mixed_sign_float64 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64) z = np.fmod(x, y) # expected output [-0.1, 0.4, 5. , 0.1, -0.4, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64") ```
mod_mixed_sign_int16 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16") ```
mod_mixed_sign_int32 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32") ```
mod_mixed_sign_int64 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64") ```
mod_mixed_sign_int8 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8") ```
mod_uint16 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint16) y = np.array([2, 3, 8]).astype(np.uint16) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16") ```
mod_uint32 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint32) y = np.array([2, 3, 8]).astype(np.uint32) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32") ```
mod_uint64 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint64) y = np.array([2, 3, 8]).astype(np.uint64) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64") ```
mod_uint8 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint8) y = np.array([2, 3, 8]).astype(np.uint8) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8") ```
### **Mul** Performs element-wise binary multiplication (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 13 #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
mul ```python node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = x * y # expected output [4., 10., 18.] expect(node, inputs=[x, y], outputs=[z], name="test_mul_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint32") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint64") ```
mul_broadcast ```python node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast") ```
### **Multinomial** Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Attributes
dtype : int (default is 6)
(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.
sample_size : int (default is 1)
Number of times to sample.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.
#### Outputs
output : T2
Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input types to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain output types to integral tensors.
### **Neg** Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
Constrain input and output types to signed numeric tensors.
#### Examples
neg ```python node = onnx.helper.make_node( "Neg", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.negative(x) # expected output [4., -2.], expect(node, inputs=[x], outputs=[y], name="test_neg_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.negative(x) expect(node, inputs=[x], outputs=[y], name="test_neg") ```
### **NegativeLogLikelihoodLoss** A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. ``` When an optional "weight" is provided, the sample loss is calculated as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. ``` loss is zero for the case when target-value equals ignore_index. ``` loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index ``` If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: ``` mean(loss), if "weight" is not provided, ``` or if weight is provided, ``` sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. ``` If "reduction" attribute is set to "sum", the output is a scalar: `sum(loss)`. See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: ``` // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] ``` Example 2: ``` // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 ``` Example 3: ``` // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 12, 13 #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.
#### Inputs (2 - 3)
input (differentiable) : T
Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).
target (non-differentiable) : Tind
Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.
weight (optional, non-differentiable) : T
Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.
#### Outputs
loss (differentiable) : T
The negative log likelihood loss
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input, weight, and output types to floating-point tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
#### Examples
input_shape_is_NC ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N,)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NC", ) ```
input_shape_is_NCd1 ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1", ) ```
input_shape_is_NCd1_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_ii", ) ```
input_shape_is_NCd1_mean_weight_negative_ii ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) input = np.random.rand(N, C, dim1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) target[0][0] = -1 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_mean_weight_negative_ii", ) ```
input_shape_is_NCd1_weight ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight", ) ```
input_shape_is_NCd1_weight_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight_ii", ) ```
input_shape_is_NCd1d2 ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2", ) ```
input_shape_is_NCd1d2_no_weight_reduction_mean_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii", ) ```
input_shape_is_NCd1d2_reduction_mean ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_mean", ) ```
input_shape_is_NCd1d2_reduction_sum ```python reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_sum", ) ```
input_shape_is_NCd1d2_with_weight ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight", ) ```
input_shape_is_NCd1d2_with_weight_reduction_mean ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_mean", ) ```
input_shape_is_NCd1d2_with_weight_reduction_sum ```python reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum", ) ```
input_shape_is_NCd1d2_with_weight_reduction_sum_ii ```python reduction = "sum" ignore_index = np.int64(0) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(0) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) target[0][0][0][0] = -5 negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N)).astype(np.int64) target[0] = 10 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_sum_weight_high_ii", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_mean_weight", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_none_no_weight", ) ```
### **NonMaxSuppression** Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. Other versions of this operator: 10 #### Attributes
center_point_box : int (default is 0)
Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.
#### Inputs (2 - 5)
boxes : tensor(float)
An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.
scores : tensor(float)
An input tensor with shape [num_batches, num_classes, spatial_dimension]
max_output_boxes_per_class (optional) : tensor(int64)
Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.
iou_threshold (optional) : tensor(float)
Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.
score_threshold (optional) : tensor(float)
Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.
#### Outputs
selected_indices : tensor(int64)
selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].
#### Type Constraints #### Examples
nonmaxsuppression_center_point_box_format ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], center_point_box=1, ) boxes = np.array( [ [ [0.5, 0.5, 1.0, 1.0], [0.5, 0.6, 1.0, 1.0], [0.5, 0.4, 1.0, 1.0], [0.5, 10.5, 1.0, 1.0], [0.5, 10.6, 1.0, 1.0], [0.5, 100.5, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_center_point_box_format", ) ```
nonmaxsuppression_flipped_coordinates ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [1.0, 1.0, 0.0, 0.0], [0.0, 0.1, 1.0, 1.1], [0.0, 0.9, 1.0, -0.1], [0.0, 10.0, 1.0, 11.0], [1.0, 10.1, 0.0, 11.1], [1.0, 101.0, 0.0, 100.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_flipped_coordinates", ) ```
nonmaxsuppression_identical_boxes ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]] ).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_identical_boxes", ) ```
nonmaxsuppression_limit_output_size ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_limit_output_size", ) ```
nonmaxsuppression_single_box ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32) scores = np.array([[[0.9]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_single_box", ) ```
nonmaxsuppression_suppress_by_IOU ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU", ) ```
nonmaxsuppression_suppress_by_IOU_and_scores ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.4]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU_and_scores", ) ```
nonmaxsuppression_two_batches ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_batches", ) ```
nonmaxsuppression_two_classes ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_classes", ) ```
### **NonZero** Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
X (non-differentiable) : T
input
#### Outputs
Y (non-differentiable) : tensor(int64)
output
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to all tensor types.
#### Examples
nonzero ```python node = onnx.helper.make_node( "NonZero", inputs=["condition"], outputs=["result"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) result = np.array( np.nonzero(condition), dtype=np.int64 ) # expected output [[0, 1, 1], [0, 0, 1]] expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example") ```
### **Not** Returns the negation of the input tensor element-wise. #### Version This version of the operator has been available since version 1 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bool)
Constrain input/output to boolean tensors.
#### Examples
not ```python node = onnx.helper.make_node( "Not", inputs=["x"], outputs=["not"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d") ```
### **OneHot** Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor. when axis = 0: output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise. when axis = -1: output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. Other versions of this operator: 9 #### Attributes
axis : int (default is -1)
(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).
#### Inputs
indices (non-differentiable) : T1
Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.
depth (non-differentiable) : T2
Scalar or Rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.
values (non-differentiable) : T3
Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor.
#### Outputs
output (non-differentiable) : T3
Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
#### Examples
with_axis ```python axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_axis", ) ```
with_negative_axis ```python axisValue = -2 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_negative_axis", ) ```
with_negative_indices ```python axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([0, -7, -8], dtype=np.int64) # print(y) # [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.] # [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.] # [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]] depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_negative_indices", ) ```
without_axis ```python on_value = 5 off_value = 2 output_type = np.int32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"] ) indices = np.array([0, 7, 8], dtype=np.int64) depth = np.float32(12) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_without_axis", ) ```
### **Optional** Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element. #### Version This version of the operator has been available since version 15 of the default ONNX operator set. #### Attributes
type : type_proto
Type of the element in the optional output
#### Inputs (0 - 1)
input (optional) : V
The input element.
#### Outputs
output : O
The optional output enclosing the input element.
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to all tensor and sequence types.
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
Constrain output type to all optional tensor or optional sequence types.
### **OptionalGetElement** If the input is a tensor or sequence type, it returns the input. If the input is an optional type, it outputs the element in the input. It is an error if the input is an empty optional-type (i.e. does not have an element) and the behavior is undefined in this case. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 15 #### Inputs
input : O
The optional input.
#### Outputs
output : V
Output element in the optional input.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output type to all tensor or sequence types.
### **OptionalHasElement** Returns true if (1) the input is an optional-type and contains an element, or, (2) the input is a tensor or sequence type. If the input is not provided or is an empty optional-type, this op returns false. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 15 #### Inputs (0 - 1)
input (optional) : O
The optional input.
#### Outputs
output : B
A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.
#### Type Constraints
O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input type to optional tensor and optional sequence types.
B : tensor(bool)
Constrain output to a boolean tensor.
#### Examples
empty ```python optional = None tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[] ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_proto in [tensor_type_proto, optional_type_proto]: input_name_options = { "empty": "optional_input", "empty_no_input_name": "", "empty_no_input": None, } for test_name_surfix, input_name in input_name_options.items(): if input_type_proto == tensor_type_proto and input_name: # the input tensor cannot be empty if input name is provided. continue node = onnx.helper.make_node( "OptionalHasElement", inputs=[] if input_name is None else [input_name], outputs=["output"], ) output = optional_has_element_reference_implementation(optional) test_name = ( "test_optional_has_element_" + test_name_surfix + ( "_optional_input" if input_type_proto == optional_type_proto else "_tensor_input" ) ) expect( node, inputs=[optional] if input_name else [], outputs=[output], input_type_protos=[input_type_proto] if input_name else [], name=test_name, ) ```
get_element_sequence ```python optional = [np.array([1, 2, 3, 4]).astype(np.int32)] tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[ 4, ], ) seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto) optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_sequence", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[seq_type_proto], name="test_optional_get_element_sequence", ) ```
get_element_tensor ```python optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_tensor", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[tensor_type_proto], name="test_optional_get_element_tensor", ) ```
optionalhaselement ```python optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_protos in [tensor_type_proto, optional_type_proto]: node = onnx.helper.make_node( "OptionalHasElement", inputs=["optional_input"], outputs=["output"] ) output = optional_has_element_reference_implementation(optional) test_name = "test_optional_has_element_" + ( "optional_input" if input_type_protos == optional_type_proto else "tensor_input" ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name=test_name, ) ```
### **Or** Returns the tensor resulted from performing the `or` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
or ```python node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or4d") ```
or_broadcast ```python node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d") ```
### **PRelu** PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. This operator supports **unidirectional broadcasting** (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 9 #### Inputs
X (differentiable) : T
Input tensor
slope (differentiable) : T
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
#### Outputs
Y (differentiable) : T
Output tensor (same size as X)
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
Constrain input and output types to float/int tensors.
#### Examples
prelu ```python node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example") ```
prelu_broadcast ```python node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast") ```
### **Pad** Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 2, 11, 13, 18, 19, 21, 23, 24 #### Attributes
mode : string (default is constant)
Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
#### Inputs (2 - 4)
data (differentiable) : T
Input tensor.
pads (non-differentiable) : tensor(int64)
Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
constant_value (optional, non-differentiable) : T
(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
#### Outputs
output (differentiable) : T
Tensor after padding.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
constant_pad ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) y = pad_impl(x, pads, "constant", 1.2) expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad") ```
constant_pad_axes ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([1, 3], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [1, 3], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_axes", ) ```
constant_pad_negative_axes ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([-3, -1], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [-3, -1], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_negative_axes", ) ```
reflection_edge_and_wrap_pad ```python for mode in ("edge", "reflect", "wrap"): node = onnx.helper.make_node( "Pad", inputs=["x", "pads"], outputs=["y"], mode=mode ) x = np.random.randn(1, 3, 4, 5).astype(np.int32) pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] y = pad_impl(x, pads, mode) expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad") ```
### **Pow** Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 15 of the default ONNX operator set. Other versions of this operator: 1, 7, 12, 13 #### Inputs
X (differentiable) : T
First operand, base of the exponent.
Y (differentiable) : T1
Second operand, power of the exponent.
#### Outputs
Z (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input X and output types to float/int tensors.
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input Y types to float/int tensors.
#### Examples
pow ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_example") x = np.arange(60).reshape(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow") ```
pow_broadcast ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array(2).astype(np.float32) z = pow(x, y) # expected output [1., 4., 9.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar") node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32) y = np.array([1, 2, 3]).astype(np.float32) # expected output [[1, 4, 27], [4, 25, 216]] z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array") ```
types ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32") ```
### **QLinearConv** The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized filter, its scale and zero point, and output's scale and zero point, and computes the quantized output. Each scale and zero-point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per output channel). Each input or output and its related zero point must have same type. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
auto_pad : string (default is NOTSET)
auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations : list of ints
dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
group : int (default is 1)
number of groups input channels and output channels are divided into. default is 1.
kernel_shape : list of ints
The shape of the convolution kernel. If not present, should be inferred from input 'w'.
pads : list of ints
Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
strides : list of ints
Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
#### Inputs (8 - 9)
x : T1
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
x_scale : tensor(float)
Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
x_zero_point : T1
Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
w : T2
The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
w_scale : tensor(float)
Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
w_zero_point : T2
Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
y_scale : tensor(float)
Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
y_zero_point : T3
Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
B (optional) : T4
Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0
#### Outputs
y : T3
Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
#### Type Constraints
T1 : tensor(int8), tensor(uint8)
Constrain input type to 8-bit integer tensor.
T2 : tensor(int8), tensor(uint8)
Constrain filter type to 8-bit integer tensor.
T3 : tensor(int8), tensor(uint8)
Constrain output type to 8-bit integer tensor.
T4 : tensor(int32)
Constrain bias type to 32-bit integer tensor.
#### Examples
qlinearconv ```python node = onnx.helper.make_node( "QLinearConv", inputs=[ "x", "x_scale", "x_zero_point", "w", "w_scale", "w_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) x = np.array( [ [255, 174, 162, 25, 203, 168, 58], [15, 59, 237, 95, 129, 0, 64], [56, 242, 153, 221, 168, 12, 166], [232, 178, 186, 195, 237, 162, 237], [188, 39, 124, 77, 80, 102, 43], [127, 230, 21, 83, 41, 40, 134], [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) x_scale = np.float32(0.00369204697) x_zero_point = np.uint8(132) w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1)) w_scale = np.array([0.00172794575], dtype=np.float32) w_zero_point = np.array([255], dtype=np.uint8) y_scale = np.float32(0.00162681262) y_zero_point = np.uint8(123) output = np.array( [ [0, 81, 93, 230, 52, 87, 197], [240, 196, 18, 160, 126, 255, 191], [199, 13, 102, 34, 87, 243, 89], [23, 77, 69, 60, 18, 93, 18], [67, 216, 131, 178, 175, 153, 212], [128, 25, 234, 172, 214, 215, 121], [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) expect( node, inputs=[ x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, ], outputs=[output], name="test_qlinearconv", ) ```
### **QLinearMatMul** Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits. #### Version This version of the operator has been available since version 21 of the default ONNX operator set. Other versions of this operator: 10 #### Inputs
a (non-differentiable) : T1
N-dimensional quantized matrix a
a_scale (non-differentiable) : TS
scale of quantized input a
a_zero_point (non-differentiable) : T1
zero point of quantized input a
b (non-differentiable) : T2
N-dimensional quantized matrix b
b_scale (non-differentiable) : TS
scale of quantized input b
b_zero_point (non-differentiable) : T2
zero point of quantized input b
y_scale (non-differentiable) : TS
scale of quantized output y
y_zero_point (non-differentiable) : T3
zero point of quantized output y
#### Outputs
y (non-differentiable) : T3
Quantized matrix multiply results from a * b
#### Type Constraints
TS : tensor(float), tensor(float16), tensor(bfloat16)
Constrain scales.
T1 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of input a and its zeropoint.
T2 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of input b and its zeropoint.
T3 : tensor(int8), tensor(uint8), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
The type of the output and its zeropoint.
#### Examples
int ```python for quant_type_name in ["uint8", "int8"]: quant_type = getattr(np, quant_type_name) for dtype_name in ["float32", "float16"]: dtype = getattr(np, dtype_name) node = onnx.helper.make_node( "QLinearMatMul", inputs=[ "a", "a_scale", "a_zero_point", "b", "b_scale", "b_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) # 2D a = np.array([[208, 236, 0, 238], [3, 214, 255, 29]]) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]] ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array( [114 - 127] if quant_type == np.int8 else [114], dtype=quant_type ) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: output = np.array([[41, -12, -9], [1, -75, 20]]) else: output = np.array([[168, 115, 255], [1, 66, 151]]) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_2D_{quant_type_name}_{dtype_name}", ) # 3D a = np.array( [ [[208, 236, 0, 238], [3, 214, 255, 29]], [[208, 236, 0, 238], [3, 214, 255, 29]], ], ) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [ [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], ], ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array([114], dtype=quant_type) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: if dtype == np.float32: output = np.array( [ [[-86, 117, 120], [115, 39, -121]], [[-86, 117, 120], [115, 39, -121]], ] ) else: output = np.array( [ [[-86, 116, 119], [115, 39, -121]], [[-86, 116, 119], [115, 39, -121]], ] ) else: output = np.array( [ [[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]], ] ) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_3D_{quant_type_name}_{dtype_name}", ) ```
### **QuantizeLinear** The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] - uint2: [0, 3] - int2: [-2, 1] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and `y_scale`, unless the `precision` attribute is specified. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 10, 13, 19, 21, 23, 24 #### Attributes
axis : int (default is 1)
(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
block_size : int (default is 0)
(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`
output_dtype : int (default is 0)
(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T3`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T3`.
precision : int (default is 0)
(Optional) The precision of the division operation between `x` and `y_scale`. If not provided, it will be the same as the type of `y_scale`.
saturate : int (default is 1)
The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
#### Inputs (2 - 3)
x : T1
N-D full precision Input tensor to be quantized.
y_scale : T2
Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.
y_zero_point (optional) : T3
Zero point for doing quantization to get `y`. Shape must match `y_scale`. Default is uint8 with zero point of 0 if it's not specified.
#### Outputs
y : T3
N-D quantized output tensor. It has same shape as input `x`.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32)
The type of the input 'x'.
T2 : tensor(float), tensor(float16), tensor(bfloat16), tensor(int32), tensor(float8e8m0)
The type of the input 'y_scale'.
T3 : tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(uint2), tensor(int2)
The type of the input `y_zero_point` and the output `y`.
#### Examples
axis ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ [ [[-162, 10], [-100, 232], [-20, -50]], [[-76, 0], [0, 252], [32, -44]], [[245, -485], [-960, -270], [-375, -470]], ], ], dtype=np.float32, ) y_scale = np.array([2, 4, 5], dtype=np.float32) y_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype( np.uint8 ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_axis", ) ```
blocked_asymmetric ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [6.0, 12.0, 50.0, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) y_zero_point = np.array( [ [0, 1], [1, 0], [2, 3], ], dtype=np.uint8, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) assert y_scale.shape == y_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_zero_point_elementwise = np.repeat( y_zero_point, repeats=repeats, axis=block_axis ) y = np.rint(x / y_scale_elementwise + y_zero_point_elementwise).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_blocked_asymmetric", ) ```
blocked_symmetric ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale"], outputs=["y"], axis=1, block_size=2, output_dtype=TensorProto.INT16, ) x = np.array( [ [6.0, -8, -10, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_val = np.clip( np.rint(x / y_scale_elementwise), a_min=-32768, a_max=32767 ).astype(np.int16) y = make_tensor( "y", TensorProto.INT16, x.shape, y_val, ) expect( node, inputs=[x, y_scale], outputs=[y], name="test_quantizelinear_blocked_symmetric", ) ```
e4m3fn ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) y = make_tensor("y", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e4m3fn", ) ```
e5m2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E5M2, [1], [0.0]) y = make_tensor("y", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e5m2", ) ```
float4e2m1 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [-0.0, -2.5, -4.8, -8.6], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.FLOAT4E2M1, y_scale.shape, np.zeros_like(y_scale), ) y = make_tensor( "y", TensorProto.FLOAT4E2M1, x.shape, [0, 1, 2, 4, -6, -6, 2, 3, 0, -0.5, -1, -2], ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_float4e2m1", ) ```
int16 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -514.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65022.0, -66046.0, 65023.0, -66047.0, 65024.0, -66048.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.int16(256) y = np.array( [ 256, -1, 258, 254, 257, 255, 258, 254, 32767, -32767, 32767, -32768, 32767, -32768, 32767, -32768, ] ).astype(np.int16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int16", ) ```
int2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-4.0, -3.0, 1.0, 2.0], [-0.0, -2.5, -4.8, -8.6], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.INT2, x.shape, [0, 1, 1, 1, -1, -1, 0, 1, 0, -1, -1, -2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int2", ) ```
int4 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.INT4, x.shape, [1, 2, 3, 5, -8, -6, 3, 4, 4, 5, 5, 7] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int4", ) ```
quantizelinear ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32) y_scale = np.float32(2) y_zero_point = np.uint8(128) y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear", ) ```
uint16 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -128.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65536.0, -65534.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.uint16(32767) y = np.array( [ 32767, 32703, 32769, 32765, 32768, 32766, 32769, 32765, 65535, 0, 65535, 0, ] ).astype(np.uint16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint16", ) ```
uint2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-2.0, -1.0, 1.0, 3.0], [4.0, 5.0, 6.0, 7.0], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT2, x.shape, [0, 1, 2, 3, 0, 0, 0, 1, 1, 1, 2, 2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint2", ) ```
uint4 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT4, x.shape, [1, 2, 3, 5, 0, 0, 3, 4, 4, 5, 5, 11] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint4", ) ```
### **RMSNormalization** This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467. The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions, where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape), the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be described by the following equations. ``` XSquared = Mul(X, X) XSquaredMean = ReduceMean(XSquared) MeanSquareEpsilon = Add(XSquaredMean, epsilon) RMS = Sqrt(MeanSquareEpsilon) Normalized = Div(X, RMS) ``` where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square, Depending on `stash_type` attribute, the actual computation must happen in different floating-point precision. For example, if `stash_type` is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast `Normalized` back to the original type of `X`. The second stage then scales the outcome of the first stage using: ``` Y= Mul(Normalized, Scale) ``` Let `d[i]` indicate the i-th dimension of `X`. If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`, the shape of `RMS` is `[d[0], ..., d[axis-1], 1, ..., 1]`. `Y` and `X` have the same shape. This operator supports unidirectional broadcasting (`Scale` should be unidirectional broadcastable to tensor `X`); for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
axis : int (default is -1)
The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
epsilon : float (default is 1e-05)
The epsilon value to use to avoid division by zero.
stash_type : int (default is 1)
The floating-point precision used in stage one of the computation.
#### Inputs
X : T
The input tensor to be normalized. In general, the shape is (D1, D2, ... , Dn) for n-dimensional data, where the root mean squared norm is taken over the last D dimensions, D is determined by the axis attribute.
scale : V
Scale tensor. Scale tensor shape should be broadcastable to the normalized shape.
#### Outputs
Y : V
Output data tensor. Same shape as X
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input X type to float tensors.
V : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain output Y and scale type to float tensors.
#### Examples
d ```python X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_2d_axis_negative_{-axis}" else: name = f"test_rms_normalization_2d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
d_epsilon ```python epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis, epsilon=epsilon) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_rms_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_rms_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
default_axis ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in RMSNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y = _rms_normalization(X, W) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], ) expect( node, inputs=[X, W], outputs=[Y], name="test_rms_normalization_default_axis", ) ```
rmsnormalization ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_4d_axis_negative_{-axis}" else: name = f"test_rms_normalization_4d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
### **RNN** Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `t` - time step (t-1 means previous time step) * `Wi` - W parameter weight matrix for input gate * `Ri` - R recurrence weight matrix for input gate * `Wbi` - W parameter bias vector for input gate * `Rbi` - R parameter bias vector for input gate * `WBi` - W parameter weight matrix for backward input gate * `RBi` - R recurrence weight matrix for backward input gate * `WBbi` - WR bias vectors for backward input gate * `RBbi` - RR bias vectors for backward input gate * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): * Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 7, 14 #### Attributes
activation_alpha : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats
Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh'])
One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float
Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward)
Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int
Number of neurons in the hidden layer
layout : int (default is 0)
The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
#### Inputs (3 - 6)
X (differentiable) : T
The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W (differentiable) : T
The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R (differentiable) : T
The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional, differentiable) : T
The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional, non-differentiable) : T1
Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional, non-differentiable) : T
Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
#### Outputs (0 - 2)
Y (optional, differentiable) : T
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
Y_h (optional, differentiable) : T
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
T1 : tensor(int32)
Constrain seq_lens to integer tensor.
#### Examples
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.5 layout = 1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_simple_rnn_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R) _, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 5 custom_bias = 0.1 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32) R_B = np.zeros((1, hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_with_initial_bias", ) ```
seq_length ```python input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, hidden_size, input_size).astype(np.float32) R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32) # Adding custom bias W_B = np.random.randn(1, hidden_size).astype(np.float32) R_B = np.random.randn(1, hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_rnn_seq_length", ) ```
### **RandomNormal** Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. Default is TensorProto::FLOAT.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from normal distribution
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomNormalLike** Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by `mean` and `scale`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message, and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
mean : float (default is 0.0)
The mean of the normal distribution.
scale : float (default is 1.0)
The standard deviation of the normal distribution.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from normal distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniform** Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the `shape` argument and the range by `low` and `high`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
dtype : int (default is 1)
The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required)
The shape of the output tensor.
#### Inputs #### Outputs
output : T
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **RandomUniformLike** Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by `low` and `high`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
dtype : int
(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
high : float (default is 1.0)
Upper boundary of the output values.
low : float (default is 0.0)
Lower boundary of the output values.
seed : float
(Optional) Seed to the random generator, if not specified we will auto generate one.
#### Inputs
input : T1
Input tensor to copy shape and optionally type information from.
#### Outputs
output : T2
Output tensor of random values drawn from uniform distribution
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain output types to float tensors.
### **Range** Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta` up to `limit` (exclusive). The number of elements in the output of range is computed as below: ``` number_of_elements = max( ceil( (limit - start) / delta ) , 0 ) ``` The pseudocode determining the contents of the output is shown below: ``` for(int i=0; i
start : T
Scalar. First entry for the range of output values.
limit : T
Scalar. Exclusive upper limit for the range of output values.
delta : T
Scalar. Value to step by.
#### Outputs
output : T
A 1-D tensor with same type as the inputs containing generated range of values.
#### Type Constraints
T : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)
Constrain input types to common numeric type tensors.
#### Examples
range_float_type_positive_delta ```python node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.float32(1) limit = np.float32(5) delta = np.float32(2) output = np.arange( start, limit, delta, dtype=np.float32 ) # expected output [1.0, 3.0] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_float_type_positive_delta", ) ```
range_int32_type_negative_delta ```python node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.int32(10) limit = np.int32(6) delta = np.int32(-3) output = np.arange( start, limit, delta, dtype=np.int32 ) # expected output [10, 7] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_int32_type_negative_delta", ) ```
### **Reciprocal** Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
reciprocal ```python node = onnx.helper.make_node( "Reciprocal", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.reciprocal(x) # expected output [-0.25, 0.5], expect(node, inputs=[x], outputs=[y], name="test_reciprocal_example") x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5 y = np.reciprocal(x) expect(node, inputs=[x], outputs=[y], name="test_reciprocal") ```
### **ReduceL1** Computes the L1 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 7.], [11., 15.], [19., 23.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_random", ) ```
### **ReduceL2** Computes the L2 norm of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[25.49509757]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[2.23606798, 5.], # [7.81024968, 10.63014581], # [13.45362405, 16.2788206]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_random", ) ```
### **ReduceLogSum** Computes the log sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_empty_set", ) ```
keepdims ```python node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, keepdims=True)) axes = np.array([], dtype=np.int64) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_default", ) ```
negative_axes_keepdims ```python axes = np.array([-2], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True)) # print(reduced) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_negative_axes", ) ```
nokeepdims ```python shape = [3, 4, 5] axes = np.array([2, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_desc_axes", ) axes = np.array([0, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_asc_axes", ) ```
### **ReduceLogSumExp** Computes the log sum exponent of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[60.00671387]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[20., 2.31326175] # [40.00004578, 2.31326175] # [60.00671387, 2.31326175]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log( np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_random", ) ```
### **ReduceMax** Computes the max of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. Other versions of this operator: 1, 11, 12, 13, 18 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8), tensor(bool)
Constrain input and output types to numeric and Boolean tensors.
#### Examples
bool_inputs ```python axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[True], # [True], # [True], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_bool_inputs", ) ```
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdim_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[20., 2.] # [40., 2.] # [60., 2.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = -(one / zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
### **ReduceMean** Computes the mean of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields undefined. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[18.25]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[12.5, 1.5] # [35., 1.5] # [57.5, 1.5]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_random", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_random", ) ```
### **ReduceMin** Computes the min of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. Other versions of this operator: 1, 11, 12, 13, 18 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8), tensor(bool)
Constrain input and output types to numeric and Boolean tensors.
#### Examples
bool_inputs ```python axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[ True], # [False], # [False], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_bool_inputs", ) ```
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[1.]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[5., 1.] # [30., 1.] # [55., 1.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = one / zero # inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
### **ReduceProd** Computes the product of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 1. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[4.790016e+08]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 8.] # [35., 48.] # [99., 120.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.ones(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_random", ) ```
### **ReduceSum** Computes the sum of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[4., 6.] # [12., 14.] # [20., 22.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_random", ) ```
empty_axes_input_noop ```python shape = [3, 2, 2] keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, noop_with_empty_axes=True, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) axes = np.array([], dtype=np.int64) reduced = np.array(data) # print(reduced) # [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.array(data) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop", ) ```
empty_set ```python """Test case with the reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_random", ) ```
non_reduced_axis_zero ```python """Test case with the non-reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 0, 1] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([2], dtype=np.int64) reduced = np.array([], dtype=np.float32).reshape(reduced_shape) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set_non_reduced_axis_zero", ) ```
### **ReduceSumSquare** Computes the sum square of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 11, 13 #### Attributes
keepdims : int (default is 1)
Keep the reduced dimension or not, default 1 means keep reduced dimension.
noop_with_empty_axes : int (default is 0)
Defines behavior when axes is not provided or is empty. If false (default), reduction happens over all axes (similar to the case when `axis=None` in numpy). If true, reduction happens over an empty set of axes (similar to the case when `axis=()` in numpy). Note that reduction over an empty set of axes means that the reduction step behaves like a no-op (identity function), but composite-reduction operators will still perform the non-reduction steps as needed. Thus, ReduceLogSum returns the Log of input tensor, and ReduceSumSquare returns the Square of the input tensor, in this case.
#### Inputs (1 - 2)
data (differentiable) : T
An input tensor.
axes (optional, non-differentiable) : tensor(int64)
Optional input list of integers, along which to reduce. The default is to reduce over empty axes. When axes is empty (either not provided or explicitly empty), behavior depends on 'noop_with_empty_axes': reduction over all axes if 'noop_with_empty_axes' is false, and reduction over the empty set of axes when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
reduced (differentiable) : T
Reduced output tensor.
#### Type Constraints
T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
#### Examples
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[650.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[10., 20.] # [74., 100.] # [202., 244.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.s]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_random", ) ```
### **RegexFullMatch** RegexFullMatch performs a full regex match on each element of the input tensor. If an element fully matches the regex pattern specified as an attribute, the corresponding element in the output is True and it is False otherwise. [RE2](https://github.com/google/re2/wiki/Syntax) regex syntax is used. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
pattern : string
Regex pattern to match on. This must be valid RE2 syntax.
#### Inputs
X (non-differentiable) : T1
Tensor with strings to match on.
#### Outputs
Y (non-differentiable) : T2
Tensor of bools indicating if each input string fully matches the regex pattern specified.
#### Type Constraints
T1 : tensor(string)
Inputs must be UTF-8 strings
T2 : tensor(bool)
Outputs are bools and are True where there is a full regex match and False otherwise.
#### Examples
basic ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"www\.[\w.-]+\.\bcom\b", ) x = np.array(["www.google.com", "www.facebook.com", "www.bbc.co.uk"]).astype( object ) result = np.array([True, True, False]) expect(node, inputs=[x], outputs=[result], name="test_regex_full_match_basic") ```
match_email_domain ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array( [ ["account@gmail.com", "account@hotmail.com"], ["not email", "account2@yahoo.com"], ] ).astype(object) result = np.array([[True, False], [False, True]]) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_email_domain", ) ```
match_empty ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array([[], []]).astype(object) result = np.array([[], []]).astype(bool) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_empty", ) ```
### **Relu** Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 1, 6, 13 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
Constrain input and output types to signed numeric tensors.
#### Examples
relu ```python node = onnx.helper.make_node( "Relu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) expect(node, inputs=[x], outputs=[y], name="test_relu") ```
### **Reshape** Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 5, 13, 14, 19, 21, 23, 24 #### Attributes
allowzero : int (default is 0)
(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
#### Inputs
data (differentiable) : T
An input tensor.
shape (non-differentiable) : tensor(int64)
Specified shape for output.
#### Outputs
reshaped (differentiable) : T
Reshaped data.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
#### Examples
allowzero ```python original_shape = [0, 3, 4] test_cases = { "allowzero_reordered": np.array([3, 4, 0], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], allowzero=1, # if allowzero=1, final shape = (3, 4, 0) # if allowzero=0, final shape = (3, 4, 4) ) reshaped = reshape_reference_implementation(data, shape, allowzero=1) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) ```
reshape ```python original_shape = [2, 3, 4] test_cases = { "reordered_all_dims": np.array([4, 2, 3], dtype=np.int64), "reordered_last_dims": np.array([2, 4, 3], dtype=np.int64), "reduced_dims": np.array([2, 12], dtype=np.int64), "extended_dims": np.array([2, 3, 2, 2], dtype=np.int64), "one_dim": np.array([24], dtype=np.int64), "negative_dim": np.array([2, -1, 2], dtype=np.int64), "negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64), "zero_dim": np.array([2, 0, 4, 1], dtype=np.int64), "zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], ) reshaped = reshape_reference_implementation(data, shape) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) ```
### **Resize** Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: ``` output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) ``` if input \"sizes\" is not specified. #### Version This version of the operator has been available since version 19 of the default ONNX operator set. Other versions of this operator: 10, 11, 13, 18 #### Attributes
antialias : int (default is 0)
If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.
axes : list of ints
If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
coordinate_transformation_mode : string (default is half_pixel)
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .
cubic_coeff_a : float (default is -0.75)
The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".
exclude_outside : int (default is 0)
If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
extrapolation_value : float (default is 0.0)
When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
keep_aspect_ratio_policy : string (default is stretch)
This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.
mode : string (default is nearest)
Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
nearest_mode : string (default is round_prefer_floor)
Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
#### Inputs (1 - 4)
X (differentiable) : T1
N-D tensor
roi (optional, non-differentiable) : T2
1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
scales (optional, non-differentiable) : tensor(float)
The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.
sizes (optional, non-differentiable) : tensor(int64)
Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified.
#### Outputs
Y (differentiable) : T1
N-D tensor after resizing
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input 'X' and output 'Y' to all tensor types.
T2 : tensor(float16), tensor(float), tensor(double)
Constrain roi type to float or double.
#### Examples
resize_downsample_scales_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.47119141 2.78125 4.08251953] # [ 6.71142578 8.02148438 9.32275391] # [11.91650391 13.2265625 14.52783203]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic", ) ```
resize_downsample_scales_cubic_A_n0p5_exclude_outside ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.36812675 2.6695014 4.0133367 ] # [ 6.57362535 7.875 9.2188353 ] # [11.94896657 13.25034122 14.59417652]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside", ) ```
resize_downsample_scales_cubic_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1. 2.39519159 3.79038317] # [ 6.58076634 7.97595793 9.37114951] # [12.16153268 13.55672427 14.95191585]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_align_corners", ) ```
resize_downsample_scales_cubic_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.5180721 4.2858863] # [ 9.589329 11.357142 ]]]] output = interpolate_nd( data, cubic_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_antialias", ) ```
resize_downsample_scales_linear ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[2.6666665 4.3333331]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear", ) ```
resize_downsample_scales_linear_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.142857]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_align_corners", ) ```
resize_downsample_scales_linear_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.875 4.5 ] # [ 9.375 11. ]]]] output = interpolate_nd( data, linear_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_antialias", ) ```
resize_downsample_scales_linear_half_pixel_symmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32) # [[[[1.6666667, 3.3333333]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_half_pixel_symmetric", ) ```
resize_downsample_scales_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_nearest", ) ```
resize_downsample_sizes_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.63078704 3.00462963 4.37847222] # [ 7.12615741 8.5 9.87384259] # [12.62152778 13.99537037 15.36921296]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic", ) ```
resize_downsample_sizes_cubic_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.7750092 3.1200073 4.4650054] # [ 7.1550016 8.5 9.844998 ] # [12.534994 13.8799925 15.224991 ]]]] output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype( np.float32 ) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic_antialias", ) ```
resize_downsample_sizes_linear_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 2.3636363 3.590909 4.818182 ] # [ 7.2727275 8.5 9.727273 ] # [12.181818 13.409091 14.636364 ]]]] output = interpolate_nd( data, linear_coeffs_antialias, output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_antialias", ) ```
resize_downsample_sizes_linear_pytorch_half_pixel ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="pytorch_half_pixel", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 1], dtype=np.int64) # [[[[ 1.6666666] # [ 7. ] # [12.333333 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, coordinate_transformation_mode="pytorch_half_pixel", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_pytorch_half_pixel", ) ```
resize_downsample_sizes_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 1, 3], dtype=np.int64) # [[[[1. 2. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest", ) ```
resize_downsample_sizes_nearest_not_larger ```python keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2 # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_larger", ) ```
resize_downsample_sizes_nearest_not_smaller ```python keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3 # [[[[1. 2. 4.] # [5. 6. 8.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_smaller", ) ```
resize_tf_crop_and_resize ```python node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize", ) ```
resize_tf_crop_and_resize_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_2_3", ) ```
resize_tf_crop_and_resize_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_3_2", ) ```
resize_tf_crop_and_resize_extrapolation_value ```python node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 10. 10. ] # [12.400001 10. 10. ] # [10. 10. 10. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_extrapolation_value", ) ```
resize_upsample_scales_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125 # 2.91015625 3.38671875 3.68359375] # [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875 # 4.09765625 4.57421875 4.87109375] # [ 3.56640625 3.86328125 4.33984375 4.96875 5.375 # 6.00390625 6.48046875 6.77734375] # [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625 # 8.51953125 8.99609375 9.29296875] # [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625 # 10.14453125 10.62109375 10.91796875] # [10.22265625 10.51953125 10.99609375 11.625 12.03125 # 12.66015625 13.13671875 13.43359375] # [12.12890625 12.42578125 12.90234375 13.53125 13.9375 # 14.56640625 15.04296875 15.33984375] # [13.31640625 13.61328125 14.08984375 14.71875 15.125 # 15.75390625 16.23046875 16.52734375]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic", ) ```
resize_upsample_scales_cubic_A_n0p5_exclude_outside ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882 # 2.93713516 3.47917561 3.73529412] # [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285 # 3.96160918 4.50364964 4.75976814] # [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466 # 6.12977099 6.67181144 6.92792995] # [ 5.91176471 6.16788321 6.70992366 7.25 7.75 # 8.29007634 8.83211679 9.08823529] # [ 7.91176471 8.16788321 8.70992366 9.25 9.75 # 10.29007634 10.83211679 11.08823529] # [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534 # 12.45038168 12.99242213 13.24854064] # [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715 # 14.61854349 15.16058394 15.41670245] # [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118 # 15.64301751 16.18505796 16.44117647]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside", ) ```
resize_upsample_scales_cubic_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394 # 3.19970845 3.65889213 4. ] # [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542 # 4.56413994 5.02332362 5.36443149] # [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012 # 6.40087464 6.86005831 7.20116618] # [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819 # 8.51749271 8.97667638 9.31778426] # [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968 # 9.8819242 10.34110787 10.68221574] # [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776 # 11.99854227 12.45772595 12.79883382] # [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245 # 13.83527697 14.29446064 14.63556851] # [13. 13.34110787 13.80029155 14.32944606 14.67055394 # 15.19970845 15.65889213 16. ]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_align_corners", ) ```
resize_upsample_scales_cubic_asymmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="asymmetric", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4. # 4.09375] # [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625 # 5.71875] # [ 5. 5.40625 6. 6.5 7. 7.59375 8. # 8.09375] # [ 7. 7.40625 8. 8.5 9. 9.59375 10. # 10.09375] # [ 9. 9.40625 10. 10.5 11. 11.59375 12. # 12.09375] # [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375 # 14.46875] # [13. 13.40625 14. 14.5 15. 15.59375 16. # 16.09375] # [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375 # 16.46875]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.75), scale_factors=scales, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_asymmetric", ) ```
resize_upsample_scales_linear ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.25 1.75 2. ] # [1.5 1.75 2.25 2.5 ] # [2.5 2.75 3.25 3.5 ] # [3. 3.25 3.75 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear", ) ```
resize_upsample_scales_linear_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.33333333 1.66666667 2. ] # [1.66666667 2. 2.33333333 2.66666667] # [2.33333333 2.66666667 3. 3.33333333] # [3. 3.33333333 3.66666667 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_align_corners", ) ```
resize_upsample_scales_linear_half_pixel_symmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32) # [[[[1. , 1.15986395, 1.5 , 1.84013605, 2. ], # [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738], # [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262], # [3. , 3.15986395, 3.5 , 3.84013605, 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_half_pixel_symmetric", ) ```
resize_upsample_scales_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest", ) ```
resize_upsample_scales_nearest_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_2_3", ) ```
resize_upsample_scales_nearest_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([3.0, 2.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_3_2", ) ```
resize_upsample_sizes_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 9, 10], dtype=np.int64) # [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922 # 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922] # [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963 # 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963] # [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693 # 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693] # [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069 # 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069] # [ 6.88975 7.07525 7.40625 7.85725 8.342 # 8.658 9.14275 9.59375 9.92475 10.11025 ] # [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931 # 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931] # [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307 # 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307] # [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037 # 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037] # [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078 # 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_cubic", ) ```
resize_upsample_sizes_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest", ) ```
resize_upsample_sizes_nearest_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_2_3", ) ```
resize_upsample_sizes_nearest_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([8, 7], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_3_2", ) ```
resize_upsample_sizes_nearest_ceil_half_pixel ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="half_pixel", nearest_mode="ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_ceil_half_pixel", ) ```
resize_upsample_sizes_nearest_floor_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="align_corners", nearest_mode="floor", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [13. 13. 13. 14. 14. 15. 15. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="floor"), output_size=sizes, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_floor_align_corners", ) ```
resize_upsample_sizes_nearest_not_larger ```python keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7 # [[[[1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_larger", ) ```
resize_upsample_sizes_nearest_not_smaller ```python keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8 # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_smaller", ) ```
resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="round_prefer_ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"), output_size=sizes, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", ) ```
### **ReverseSequence** Reverse batch of sequences having different lengths specified by `sequence_lens`. For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements. Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1 output = [[3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0]] Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0 output = [[0.0, 1.0, 2.0, 3.0 ], [5.0, 4.0, 6.0, 7.0 ], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0]] #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
batch_axis : int (default is 1)
(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.
time_axis : int (default is 0)
(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.
#### Inputs
input : T
Tensor of rank r >= 2.
sequence_lens : tensor(int64)
Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.
#### Outputs
Y : T
Tensor with same shape of input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
#### Examples
reversesequence_batch ```python node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=1, batch_axis=0, ) x = np.array( [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64) y = np.array( [ [0.0, 1.0, 2.0, 3.0], [5.0, 4.0, 6.0, 7.0], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_batch", ) ```
reversesequence_time ```python node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=0, batch_axis=1, ) x = np.array( [ [0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64) y = np.array( [ [3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_time", ) ```
### **RoiAlign** Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 10, 16 #### Attributes
coordinate_transformation_mode : string (default is half_pixel)
Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).
mode : string (default is avg)
The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.
output_height : int (default is 1)
default 1; Pooled output Y's height.
output_width : int (default is 1)
default 1; Pooled output Y's width.
sampling_ratio : int (default is 0)
Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.
spatial_scale : float (default is 1.0)
Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f.
#### Inputs
X : T1
Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois : T1
RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
batch_indices : T2
1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
#### Outputs
Y : T1
RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].
#### Type Constraints
T1 : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain types to float tensors.
T2 : tensor(int64)
Constrain types to int tensors.
#### Examples
roialign_aligned_false ```python node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.4664, 0.4466, 0.3405, 0.5688, 0.6068], [0.3714, 0.4296, 0.3835, 0.5562, 0.3510], [0.2768, 0.4883, 0.5222, 0.5528, 0.4171], [0.4713, 0.4844, 0.6904, 0.4920, 0.8774], [0.6239, 0.7125, 0.6289, 0.3355, 0.3495], ] ], [ [ [0.3022, 0.4305, 0.4696, 0.3978, 0.5423], [0.3656, 0.7050, 0.5165, 0.3172, 0.7015], [0.2912, 0.5059, 0.6476, 0.6235, 0.8299], [0.5916, 0.7389, 0.7048, 0.8372, 0.8893], [0.6227, 0.6153, 0.7097, 0.6154, 0.4585], ] ], [ [ [0.2384, 0.3379, 0.3717, 0.6100, 0.7601], [0.3767, 0.3785, 0.7147, 0.9243, 0.9727], [0.5749, 0.5826, 0.5709, 0.7619, 0.8770], [0.5355, 0.2566, 0.2141, 0.2796, 0.3600], [0.4365, 0.3504, 0.2887, 0.3661, 0.2349], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false", ) ```
roialign_aligned_true ```python node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.5178, 0.3434, 0.3229, 0.4474, 0.6344], [0.4031, 0.5366, 0.4428, 0.4861, 0.4023], [0.2512, 0.4002, 0.5155, 0.6954, 0.3465], [0.3350, 0.4601, 0.5881, 0.3439, 0.6849], [0.4932, 0.7141, 0.8217, 0.4719, 0.4039], ] ], [ [ [0.3070, 0.2187, 0.3337, 0.4880, 0.4870], [0.1871, 0.4914, 0.5561, 0.4192, 0.3686], [0.1433, 0.4608, 0.5971, 0.5310, 0.4982], [0.2788, 0.4386, 0.6022, 0.7000, 0.7524], [0.5774, 0.7024, 0.7251, 0.7338, 0.8163], ] ], [ [ [0.2393, 0.4075, 0.3379, 0.2525, 0.4743], [0.3671, 0.2702, 0.4105, 0.6419, 0.8308], [0.5556, 0.4543, 0.5564, 0.7502, 0.9300], [0.6626, 0.5617, 0.4813, 0.4954, 0.6663], [0.6636, 0.3721, 0.2056, 0.1928, 0.2478], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true", ) ```
roialign_mode_max ```python X = np.array( [ [ [ [ 0.2764, 0.715, 0.1958, 0.3416, 0.4638, 0.0259, 0.2963, 0.6518, 0.4856, 0.725, ], [ 0.9637, 0.0895, 0.2919, 0.6753, 0.0234, 0.6132, 0.8085, 0.5324, 0.8992, 0.4467, ], [ 0.3265, 0.8479, 0.9698, 0.2471, 0.9336, 0.1878, 0.4766, 0.4308, 0.34, 0.2162, ], [ 0.0206, 0.172, 0.2155, 0.4394, 0.0653, 0.3406, 0.7724, 0.3921, 0.2541, 0.5799, ], [ 0.4062, 0.2194, 0.4473, 0.4687, 0.7109, 0.9327, 0.9815, 0.632, 0.1728, 0.6119, ], [ 0.3097, 0.1283, 0.4984, 0.5068, 0.4279, 0.0173, 0.4388, 0.043, 0.4671, 0.7119, ], [ 0.1011, 0.8477, 0.4726, 0.1777, 0.9923, 0.4042, 0.1869, 0.7795, 0.9946, 0.9689, ], [ 0.1366, 0.3671, 0.7011, 0.6234, 0.9867, 0.5585, 0.6985, 0.5609, 0.8788, 0.9928, ], [ 0.5697, 0.8511, 0.6711, 0.9406, 0.8751, 0.7496, 0.165, 0.1049, 0.1559, 0.2514, ], [ 0.7012, 0.4056, 0.7879, 0.3461, 0.0415, 0.2998, 0.5094, 0.3727, 0.5482, 0.0502, ], ] ] ], dtype=np.float32, ) rois = np.array( [[0.0, 0.0, 9.0, 9.0], [0.0, 5.0, 4.0, 9.0], [5.0, 5.0, 9.0, 9.0]], dtype=np.float32, ) batch_indices = np.array([0, 0, 0], dtype=np.int64) Y = np.array( [ [ [ [0.3445228, 0.37310338, 0.37865096, 0.446696, 0.37991184], [0.4133513, 0.5455125, 0.6651902, 0.55805874, 0.27110294], [0.21223956, 0.40924096, 0.8417618, 0.792561, 0.37196714], [0.46835402, 0.39741728, 0.8012819, 0.4969306, 0.5495158], [0.3595896, 0.5196813, 0.5403741, 0.23814403, 0.19992709], ] ], [ [ [0.30517197, 0.5086199, 0.3189761, 0.4054401, 0.47630402], [0.50862, 0.8477, 0.37808004, 0.24936005, 0.79384017], [0.17620805, 0.29368007, 0.44870415, 0.4987201, 0.63148826], [0.51066005, 0.8511, 0.5368801, 0.9406, 0.70008016], [0.4487681, 0.51066035, 0.5042561, 0.5643603, 0.42004836], ] ], [ [ [0.21062402, 0.3510401, 0.37416005, 0.5967599, 0.46507207], [0.32336006, 0.31180006, 0.6236001, 0.9946, 0.7751202], [0.35744014, 0.5588001, 0.35897616, 0.7030401, 0.6353923], [0.5996801, 0.27940005, 0.17948808, 0.35152006, 0.31769615], [0.3598083, 0.40752012, 0.2385281, 0.43856013, 0.26313624], ] ], ], dtype=np.float32, ) node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], mode="max", outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_mode_max", ) ```
### **RotaryEmbedding** RotaryEmbedding is the implementation of rotary positional embeddings (RoPE) based on the paper https://arxiv.org/pdf/2104.09864. The key advantage of RoPE is that it allows the model to understand both the absolute position of a token and the relative distances between tokens. This is achieved through a rotational mechanism where the extent of rotation is computed based on the token's absolute position (position_ids). The rotational mechanism is defined by sine and cosine functions that are used to represent the rotation angles. For each token in the sequence, its positional embedding is computed by rotating its embedding vector. This is done by splitting the embedding vector either into two halves or interleaving every alternate token and applying the rotation matrix to each half of the embedding vector. The rotation matrix is parameterized by the token's position in the sequence. The rotated halves of the embedding vector are concatenated to form the final positional embedding for each token. The rotated positional embeddings are used in the self-attention mechanism. The rotation ensures that the model captures both absolute and relative positional information. Rotary embeddings are defined using the following algorithm: ```python def rotary_embedding( input: np.ndarray, cos_cache: np.ndarray, sin_cache: np.ndarray, position_ids: np.ndarray | None = None, interleaved=None, rotary_embedding_dim=None, num_heads=None, ) -> np.ndarray: original_input_shape = input.shape # First ensure input to be processed has shape [batch_size, seq_len, num_heads, head_size] if len(input.shape) == 4: input = np.transpose(input, (0, 2, 1, 3)) batch_size = input.shape[0] sequence_length = input.shape[1] if len(input.shape) == 3: hidden_size = input.shape[2] assert num_heads != 0 head_size = int(hidden_size / num_heads) new_shape = [batch_size, sequence_length, num_heads, head_size] input = np.reshape(input, new_shape) assert len(input.shape) == 4 head_size = input.shape[3] # Fully or partially perform rotation on input based on rotary_embedding_dim attribute if rotary_embedding_dim is None or rotary_embedding_dim == 0: # If rotary_embedding_dim not provided, perform full rotation by using head_size rotary_embedding_dim = head_size x_rotate = input[:, :, :, :rotary_embedding_dim] x_not_rotate = input[:, :, :, rotary_embedding_dim:] rotary_embedding_dim_half = int(rotary_embedding_dim / 2) # Retrieve sin and cos caches using position ids if position_ids is not None: cos_cache = cos_cache[ position_ids ] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] sin_cache = sin_cache[ position_ids ] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] # Shape: [batch_size, sequence_length, rotary_embedding_dim/2] if cos_cache.shape[-1] != rotary_embedding_dim_half: raise ValueError( f"Last dimension of cos cache ({cos_cache.shape[-1]}) does not match rotary_embedding_dim/2 ({rotary_embedding_dim_half})." ) if sin_cache.shape[-1] != rotary_embedding_dim_half: raise ValueError( f"Last dimension of sin cache ({sin_cache.shape[-1]}) does not match rotary_embedding_dim/2 ({rotary_embedding_dim_half})." ) cos_cache = np.expand_dims( cos_cache, axis=2 ) # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2] sin_cache = np.expand_dims( sin_cache, axis=2 ) # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2] # Either divide the input in halves or interleave (based on interleaved attribute) if interleaved: x1 = x_rotate[:, :, :, 0::2] x2 = x_rotate[:, :, :, 1::2] else: x1, x2 = np.split(x_rotate, 2, axis=-1) # Calculate real and imaginary values real = (cos_cache * x1) - (sin_cache * x2) imag = (sin_cache * x1) + (cos_cache * x2) # Inserted rotated embeddings back to the original input if interleaved: # x_rotate[:, :, :, 0::2] = real # x_rotate[:, :, :, 1::2] = imag real = np.expand_dims(real, axis=-1) imag = np.expand_dims(imag, axis=-1) x_rotate_concat = np.concatenate((real, imag), axis=-1) x_rotate = np.reshape(x_rotate_concat, x_rotate.shape) else: x_rotate = np.concatenate((real, imag), axis=-1) output = np.concatenate((x_rotate, x_not_rotate), axis=-1) if len(original_input_shape) == 3: output = np.reshape(output, original_input_shape) else: output = np.transpose(output, (0, 2, 1, 3)) return output ``` #### Version This version of the operator has been available since version 23 of the default ONNX operator set. #### Attributes
interleaved : int (default is 0)
Rotate using interleaved pattern. Default value is 0 (False).
num_heads : int
Number of attention heads. Must be provided when input is a 3D tensor.
rotary_embedding_dim : int (default is 0)
Rotary embedding dimension used to apply partial rotary embeddings.
#### Inputs (3 - 4)
X : T
The input tensor representing the token embeddings. 4D tensor with shape `(batch_size, num_heads, sequence_length, head_size)` or 3D tensor with shape `(batch_size, sequence_length, hidden_size)`. For cases with a 4D input tensor, `head_size` has to be even. For cases with a 3D input tensor, `num_heads` attribute must be provided and `hidden_size` must be an even multiple of `num_heads` where `hidden_size = num_heads * head_size`
cos_cache : T
The cosine values for the rotation. 2D tensor with shape `(max_position_id_plus_1, head_size / 2)` for full rotation or `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are provided. 3D tensor with shape `(batch_size, sequence_length, head_size / 2)` for full rotation or `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are not provided. `max_position_id_plus_1` is a parameter to the model.
sin_cache : T
The sine values for the rotation. 2D tensor with shape `(max_position_id_plus_1, head_size / 2)` for full rotation or `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are provided. 3D tensor with shape `(batch_size, sequence_length, head_size / 2)` for full rotation or `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial rotation when `position_ids` are not provided. `max_position_id_plus_1` is a parameter to the model.
position_ids (optional) : M
The position indices for the tokens. 2D tensor with shape `(batch_size, sequence_length)`
#### Outputs
Y : T
Tensor with same shape as input.
#### Type Constraints
T : tensor(float), tensor(float16), tensor(bfloat16)
Constrain input and output types to float tensors.
M : tensor(int64)
Constrain input and output types to integer tensors.
#### Examples
rotary_embedding ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding", ) ```
rotary_embedding_3d_input ```python num_heads = 4 node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], num_heads=num_heads, ) input_data = np.random.rand(2, 3, 32).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, num_heads=num_heads, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_3d_input", ) ```
rotary_embedding_interleaved ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_interleaved", ) ```
rotary_embedding_no_position_ids ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding(input_data, cos_cache_data, sin_cache_data) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids", ) ```
rotary_embedding_no_position_ids_interleaved ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_interleaved", ) ```
rotary_embedding_no_position_ids_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 2).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_rotary_dim", ) ```
rotary_embedding_with_interleaved_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_interleaved_rotary_dim", ) ```
rotary_embedding_with_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_rotary_dim", ) ```
### **Round** Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halves, the rule is to round them to the nearest even integer. If input x is integral, +0, -0, NaN, or infinite, x itself is returned. The output tensor has the same shape and type as the input. Examples: ``` round([0.9]) = [1.0] round([2.5]) = [2.0] round([2.3]) = [2.0] round([1.5]) = [2.0] round([-4.5]) = [-4.0] ``` #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 11 #### Inputs
X (non-differentiable) : T
Input tensor
#### Outputs
Y (non-differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
round ```python node = onnx.helper.make_node( "Round", inputs=["x"], outputs=["y"], ) x = np.array( [ 0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8, ] ).astype(np.float32) # expected output y = np.array( [ 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, -1.0, -2.0, -2.0, -2.0, -2.0, -3.0, ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_round") ```
### **STFT** Computes the Short-time Fourier Transform of the signal. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
onesided : int (default is 1)
If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.
#### Inputs (2 - 4)
signal (non-differentiable) : T1
Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.
frame_step (non-differentiable) : T2
The number of samples to step between successive DFTs.
window (optional, non-differentiable) : T1
A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value.
frame_length (optional, non-differentiable) : T2
A scalar representing the size of the DFT. It's an optional value.
#### Outputs
output (non-differentiable) : T1
The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.
#### Type Constraints
T1 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
Constrain signal and output to float tensors.
T2 : tensor(int32), tensor(int64)
Constrain scalar length types to int64_t.
#### Examples
stft ```python signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1) length = np.array(16).astype(np.int64) onesided_length = (length >> 1) + 1 step = np.array(8).astype(np.int64) no_window = "" # optional input, not supplied node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", no_window, "frame_length"], outputs=["output"], ) nstfts = ((signal.shape[1] - length) // step) + 1 # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) output = output.astype(signal.dtype) expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft") node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", "window"], outputs=["output"], ) # Test with window a0 = 0.5 a1 = 0.5 window = a0 + a1 * np.cos( 2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length ) nstfts = 1 + (signal.shape[1] - window.shape[0]) // step # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[ 0:onesided_length ] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) window = window.astype(signal.dtype) output = output.astype(signal.dtype) expect( node, inputs=[signal, step, window], outputs=[output], name="test_stft_with_window", ) ```
### **Scan** Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 8, 9, 11, 16, 19, 21, 23, 24 #### Attributes
body : graph (required)
The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
num_scan_inputs : int (required)
An attribute specifying the number of scan_inputs M.
scan_input_axes : list of ints
An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
scan_input_directions : list of ints
An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
scan_output_axes : list of ints
An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
scan_output_directions : list of ints
An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
#### Inputs (1 - ∞)
initial_state_and_scan_inputs (variadic, heterogeneous) : V
Initial values of the loop's N state variables followed by M scan_inputs
#### Outputs (1 - ∞)
final_state_and_scan_outputs (variadic, heterogeneous) : V
Final values of the loop's N state variables followed by K scan_outputs
#### Type Constraints
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
All Tensor types up to IRv13.
#### Examples
scan_8 ```python # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node no_sequence_lens = "" # optional input, not supplied node = onnx.helper.make_node( "Scan", inputs=[no_sequence_lens, "initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for batch-size 1, sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((1, 2)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((1, 2)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan_sum", opset_imports=[onnx.helper.make_opsetid("", 8)], ) ```
scan_9 ```python # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node node = onnx.helper.make_node( "Scan", inputs=["initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((2,)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((2,)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan9_sum", opset_imports=[onnx.helper.make_opsetid("", 9)], ) ```
### **Scatter** (deprecated) This operator is deprecated. Please use ScatterElements, which provides the same functionality. Scatter takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been deprecated since version 11 of the default ONNX operator set. Other versions of this operator: 9 #### Examples
scatter_with_axis ```python axis = 1 node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter(data, indices, updates, axis=axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_with_axis", opset_imports=[helper.make_opsetid("", 10)], ) ```
scatter_without_axis ```python node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_without_axis", opset_imports=[helper.make_opsetid("", 10)], ) ```
### **ScatterElements** ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to some reduction function `f`, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0, output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1, ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 11, 13, 16 #### Attributes
axis : int (default is 0)
Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : Tind
Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
updates (differentiable) : T
Tensor of rank r >=1 (same rank and shape as indices)
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1 (same rank as input).
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input and output types can be of any tensor type.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
scatter_elements_with_axis ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_axis", ) ```
scatter_elements_with_duplicate_indices ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="add", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="add") # print(y) produces # [[1.0, 5.2, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_duplicate_indices", ) ```
scatter_elements_with_negative_indices ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, -3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 2.1, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_negative_indices", ) ```
scatter_elements_with_reduction_max ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="max", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="max") # print(y) produces # [[1.0, 2.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_max", ) ```
scatter_elements_with_reduction_min ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="min", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="min") # print(y) produces # [[1.0, 1.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_min", ) ```
scatter_elements_without_axis ```python node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter_elements(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_without_axis", ) ```
### **ScatterND** ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = updates[idx] ``` The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to some reduction function `f`, `output` is calculated as follows: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[tuple(indices[idx])] = f(output[tuple(indices[idx])], updates[idx]) ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherND. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 11, 13, 16 #### Attributes
reduction : string (default is none)
Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.
#### Inputs
data (differentiable) : T
Tensor of rank r >= 1.
indices (non-differentiable) : tensor(int64)
Tensor of rank q >= 1.
updates (differentiable) : T
Tensor of rank q + r - indices_shape[-1] - 1.
#### Outputs
output (differentiable) : T
Tensor of rank r >= 1.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to any tensor type.
#### Examples
scatternd ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [2]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates) expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd", ) ```
scatternd_add ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="add", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="add") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_add", ) ```
scatternd_max ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="max", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="max") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_max", ) ```
scatternd_min ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="min", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="min") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_min", ) ```
scatternd_multiply ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="mul", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="mul") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_multiply", ) ```
### **Selu** Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Attributes
alpha : float (default is 1.67326)
Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).
gamma : float (default is 1.0507)
Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
selu ```python node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-3.79272318, 0., 3.] y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu") ```
selu_default ```python default_alpha = 1.67326319217681884765625 default_gamma = 1.05070102214813232421875 node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * default_gamma + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma ) expect(node, inputs=[x], outputs=[y], name="test_selu_default") ```
### **SequenceAt** Outputs a tensor copy from the tensor at 'position' in 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
input_sequence : S
Input sequence.
position : I
Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
tensor : T
Output tensor at the specified position in the input sequence.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceConstruct** Construct a tensor sequence containing 'inputs' tensors. All tensors in 'inputs' must have the same data type. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (1 - ∞)
inputs (variadic) : T
Tensors.
#### Outputs
output_sequence : S
Sequence enclosing the input tensors.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input types to any tensor type.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to any tensor type.
### **SequenceEmpty** Construct an empty tensor sequence, with given data type. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
dtype : int
(Optional) The data type of the tensors in the output sequence. The default type is 'float'.
#### Inputs #### Outputs
output : S
Empty sequence.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to any tensor type.
### **SequenceErase** Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it erases the last tensor from 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (1 - 2)
input_sequence : S
Input sequence.
position (optional) : I
Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
output_sequence : S
Output sequence that has the tensor at the specified position removed.
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceInsert** Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'. 'tensor' must have the same data type as 'input_sequence'. Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs (2 - 3)
input_sequence : S
Input sequence.
tensor : T
Input tensor to be inserted into the input sequence.
position (optional) : I
Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
#### Outputs
output_sequence : S
Output sequence that contains the inserted tensor at given position.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain to any tensor type.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int32), tensor(int64)
Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
#### Examples
sequenceinsert ```python test_cases = { "at_back": [np.array([10, 11, 12]).astype(np.int64)], "at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)], } sequence = [ np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64), ] for test_name, test_inputs in test_cases.items(): tensor = test_inputs[0].astype(np.int64) if len(test_inputs) > 1: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor", "position"], outputs=["output_sequence"], ) position = test_inputs[1] inserted = sequence_insert_reference_implementation( sequence, tensor, position ) expect( node, inputs=[sequence, tensor, position], outputs=[inserted], name="test_sequence_insert_" + test_name, ) else: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor"], outputs=["output_sequence"], ) inserted = sequence_insert_reference_implementation(sequence, tensor) expect( node, inputs=[sequence, tensor], outputs=[inserted], name="test_sequence_insert_" + test_name, ) ```
### **SequenceLength** Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'. #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Inputs
input_sequence : S
Input sequence.
#### Outputs
length : I
Length of input sequence. It must be a scalar(tensor of empty shape).
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor type.
I : tensor(int64)
Constrain output to integral tensor. It must be a scalar(tensor of empty shape).
### **SequenceMap** Applies a sub-graph to each sample in the input sequence(s). Inputs can be either tensors or sequences, with the exception of the first input which must be a sequence. The length of the first input sequence will determine the number of samples in the outputs. Any other sequence inputs should have the same number of samples. The number of inputs and outputs, should match the one of the subgraph. For each i-th element in the output, a sample will be extracted from the input sequence(s) at the i-th position and the sub-graph will be applied to it. The outputs will contain the outputs of the sub-graph for each sample, in the same order as in the input. This operator assumes that processing each sample is independent and could executed in parallel or in any order. Users cannot expect any specific ordering in which each subgraph is computed. #### Version This version of the operator has been available since version 17 of the default ONNX operator set. #### Attributes
body : graph (required)
The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.
#### Inputs (1 - ∞)
input_sequence : S
Input sequence.
additional_inputs (variadic, heterogeneous) : V
Additional inputs to the graph
#### Outputs (1 - ∞)
out_sequence (variadic, heterogeneous) : S
Output sequence(s)
#### Type Constraints
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain input types to any sequence type.
V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain to any tensor or sequence type.
#### Examples
sequence_map_add_1_sequence_1_tensor ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)] x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y0 = [x0[i] + x1 for i in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_1_sequence_1_tensor", ) ```
sequence_map_add_2_sequences ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) N = [np.random.randint(1, 10) for _ in range(3)] x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] y0 = [x0[k] + x1[k] for k in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_2_sequences", ) ```
sequence_map_extract_shapes ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Shape", ["x"], ["shape"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "x", onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ], [onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body ) shapes = [ np.array([40, 30, 3], dtype=np.int64), np.array([20, 10, 3], dtype=np.int64), np.array([10, 5, 3], dtype=np.int64), ] x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3]) ), ] expect( node, inputs=[x0], outputs=[shapes], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_extract_shapes", ) ```
sequence_map_identity_1_sequence ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Identity", ["in0"], ["out0"])], "seq_map_body", [onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x"], outputs=["y"], body=body ) x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)] y = x input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x], outputs=[y], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence", ) ```
sequence_map_identity_1_sequence_1_tensor ```python body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) y0 = x0 y1 = [x1 for _ in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence_1_tensor", ) ```
sequence_map_identity_2_sequences ```python body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] y0 = x0 y1 = x1 input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_2_sequences", ) ```
### **Shape** Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 13, 15, 19, 21, 23, 24 #### Attributes
end : int
(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
start : int (default is 0)
(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
#### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
shape (non-differentiable) : T1
Shape of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor.
#### Examples
shape ```python x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) test_shape("_example", x) # preserve names of original test cases x = np.random.randn(3, 4, 5).astype(np.float32) test_shape("", x) # preserve names of original test cases test_shape("_start_1", x, start=1) test_shape("_end_1", x, end=1) test_shape("_start_negative_1", x, start=-1) test_shape("_end_negative_1", x, end=-1) test_shape("_start_1_end_negative_1", x, start=1, end=-1) test_shape("_start_1_end_2", x, start=1, end=2) test_shape("_clip_start", x, start=-10) test_shape("_clip_end", x, end=10) test_shape("_start_greater_than_end", x, start=2, end=1) ```
### **Shrink** Shrink takes one input data (Tensor) and produces one Tensor output, having same datatype and shape with input. It has two attributes, lambd and bias. The formula of this operator is: If x < -lambd, y = x + bias; If x > lambd, y = x - bias; Otherwise, y = 0. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
bias : float (default is 0.0)
The bias value added to output. Default is 0.
lambd : float (default is 0.5)
The lambd value for the Shrink formulation. Default is 0.5.
#### Inputs
input (differentiable) : T
The input data as Tensor.
#### Outputs
output (differentiable) : T
The output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
Constrain input to only numeric types.
#### Examples
hard_shrink ```python node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard") ```
soft_shrink ```python node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, bias=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft") ```
### **Sigmoid** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
sigmoid ```python node = onnx.helper.make_node( "Sigmoid", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = 1.0 / ( 1.0 + np.exp(np.negative(x)) ) # expected output [0.26894143, 0.5, 0.7310586] expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = 1.0 / (1.0 + np.exp(np.negative(x))) expect(node, inputs=[x], outputs=[y], name="test_sigmoid") ```
### **Sign** Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (non-differentiable) : T
Input tensor
#### Outputs
output (non-differentiable) : T
The sign of the input tensor computed element-wise. It has the same shape and type of the input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
sign ```python node = onnx.helper.make_node( "Sign", inputs=["x"], outputs=["y"], ) x = np.array(range(-5, 6)).astype(np.float32) y = np.sign(x) expect(node, inputs=[x], outputs=[y], name="test_sign") ```
### **Sin** Calculates the sine of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The sine of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
sin ```python node = onnx.helper.make_node( "Sin", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin") ```
### **Sinh** Calculates the hyperbolic sine of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic sine values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
sinh ```python node = onnx.helper.make_node( "Sinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sinh(x) # expected output [-1.17520118, 0., 1.17520118] expect(node, inputs=[x], outputs=[y], name="test_sinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sinh(x) expect(node, inputs=[x], outputs=[y], name="test_sinh") ```
### **Size** Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 13, 19, 21, 23, 24 #### Inputs
data (non-differentiable) : T
An input tensor.
#### Outputs
size (non-differentiable) : T1
Total number of elements of the input tensor
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Input tensor can be of arbitrary type.
T1 : tensor(int64)
Constrain output to int64 tensor, which should be a scalar though.
#### Examples
size ```python node = onnx.helper.make_node( "Size", inputs=["x"], outputs=["y"], ) x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) y = np.array(6).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.array(x.size).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size") ```
### **Slice** Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor of its input `data` tensor. An effective `starts[i]`, `ends[i]`, and `steps[i]` must be computed for each `i` in `[0, ... r-1]` where `r = rank(input)` as follows: If `axes` are omitted, they are set to `[0, ..., r-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` The effective values are initialized as `start[i] = 0`, `ends[i] = dims[i]` where `dims` are the dimensions of `input` and `steps[i] = 1`. All negative elements of `axes` are made non-negative by adding `r` to them, where `r =rank(input)`. All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them, where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted `starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping and `[0, dims[axes[i]]-1]` for negative stepping. The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping `ends[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it is clamped to `[-1, dims[axes[i]]-1]`. Finally, `steps[axes[i]] = steps[i]`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward. Example 1: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] ``` Example 2: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] ``` #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 10, 11 #### Inputs (3 - 5)
data (differentiable) : T
Tensor of data to extract slices from.
starts (non-differentiable) : Tind
1-D tensor of starting indices of corresponding axis in `axes`
ends (non-differentiable) : Tind
1-D tensor of ending indices (exclusive) of corresponding axis in `axes`
axes (optional, non-differentiable) : Tind
1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.
steps (optional, non-differentiable) : Tind
1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.
#### Outputs
output (differentiable) : T
Sliced data tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
Tind : tensor(int32), tensor(int64)
Constrain indices to integer types
#### Examples
slice ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) y = x[0:3, 0:10] starts = np.array([0, 0], dtype=np.int64) ends = np.array([3, 10], dtype=np.int64) axes = np.array([0, 1], dtype=np.int64) steps = np.array([1, 1], dtype=np.int64) expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice" ) ```
slice_default_axes ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes" ) ```
slice_default_steps ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_default_steps", ) ```
slice_end_out_of_bounds ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_end_out_of_bounds", ) ```
slice_neg ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0], dtype=np.int64) ends = np.array([-1], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 0:-1] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg", ) ```
slice_neg_steps ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([20, 10, 4], dtype=np.int64) ends = np.array([0, 0, 1], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) steps = np.array([-1, -3, -2]).astype(np.int64) y = x[20:0:-1, 10:0:-3, 4:1:-2] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg_steps", ) ```
slice_negative_axes ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, -2, -1], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_negative_axes", ) ```
slice_start_out_of_bounds ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1000], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1000:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_start_out_of_bounds", ) ```
### **Softmax** The operator computes the normalized exponential values for the given input: Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) The "axis" attribute indicates the dimension along which Softmax will be performed. The output tensor has the same shape and contains the Softmax values of the corresponding input. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 11 #### Attributes
axis : int (default is -1)
Describes the dimension Softmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
#### Inputs
input (differentiable) : T
The input tensor of rank >= axis.
#### Outputs
output (differentiable) : T
The output values with the same shape as the input tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
softmax ```python node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output [[0.09003058, 0.24472848, 0.66524094]] y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_example") ```
softmax_axis ```python x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[0.032058604 0.08714432 0.23688284 0.6439143 ] # [0.032058604 0.08714432 0.23688284 0.6439143 ]] y = softmax(x) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=0, ) y = softmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=1, ) y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=2, ) y = softmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=-1, ) y = softmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis") ```
### **SoftmaxCrossEntropyLoss** Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. * shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. * shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can calculated as follows: ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. ``` or ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. ``` loss is zero for the case when label-value equals ignore_index. ``` l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index ``` where: ``` p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] ``` Finally, L is optionally reduced: * If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). * If reduction = 'sum', the output is scalar: Sum(L). * If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: `ReduceSum(L) / ReduceSum(W)`, where tensor W is of shape `(N, D1, D2, ..., Dk)` and `W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]`. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 12 #### Attributes
ignore_index : int
Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
reduction : string (default is mean)
Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.
#### Inputs (2 - 3)
scores (differentiable) : T
The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.
labels (non-differentiable) : Tind
The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.
weights (optional, non-differentiable) : T
A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.
#### Outputs (1 - 2)
output (differentiable) : T
Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.
log_prob (optional, differentiable) : T
Log probability tensor. If the output of softmax is prob, its value is log(prob).
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
Tind : tensor(int32), tensor(int64)
Constrain target to integer types
#### Examples
input_shape_is_NCd1_mean_weight_negative_ii ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1_mean_weight_negative_ii", ) ```
input_shape_is_NCd1_mean_weight_negative_ii_log_prob ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1_mean_weight_negative_ii_log_prob", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3_none_no_weight_negative_ii", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3_sum_weight_high_ii", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight ```python reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3d4d5_mean_weight", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob ```python reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight ```python reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) sce = softmaxcrossentropy(x, labels, reduction=reduction) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3d4d5_none_no_weight", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob ```python reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob", ) ```
softmaxcrossentropy_mean ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels) # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean") ```
softmaxcrossentropy_mean_3d ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, y) # Check results expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d") ```
softmaxcrossentropy_mean_3d_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True) # Check results expect( node, inputs=[x, y], outputs=[loss, log_prob], name="test_sce_mean_3d_log_prob", ) ```
softmaxcrossentropy_mean_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii" ) ```
softmaxcrossentropy_mean_no_weights_ii_3d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_3d", ) ```
softmaxcrossentropy_mean_no_weights_ii_3d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_3d_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii_4d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_4d", ) ```
softmaxcrossentropy_mean_no_weights_ii_4d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_4d_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_log_prob", ) ```
softmaxcrossentropy_mean_weights ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight", ) ```
softmaxcrossentropy_mean_weights_ii ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii", ) ```
softmaxcrossentropy_mean_weights_ii_3d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_3d", ) ```
softmaxcrossentropy_mean_weights_ii_3d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_3d_log_prob", ) ```
softmaxcrossentropy_mean_weights_ii_4d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_4d", ) ```
softmaxcrossentropy_mean_weights_ii_4d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True, ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_4d_log_prob", ) ```
softmaxcrossentropy_mean_weights_ii_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_log_prob", ) ```
softmaxcrossentropy_mean_weights_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_log_prob", ) ```
softmaxcrossentropy_none ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="none") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none") ```
softmaxcrossentropy_none_log_prob ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_none_log_prob", ) ```
softmaxcrossentropy_none_weights ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none") # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_none_weights", ) ```
softmaxcrossentropy_none_weights_log_prob ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_none_weights_log_prob", ) ```
softmaxcrossentropy_sum ```python # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="sum") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum") ```
softmaxcrossentropy_sum_log_prob ```python # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="sum", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_sum_log_prob", ) ```
### **Softplus** Softplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
softplus ```python node = onnx.helper.make_node( "Softplus", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.log( np.exp(x) + 1 ) # expected output [0.31326166, 0.69314718, 1.31326163] expect(node, inputs=[x], outputs=[y], name="test_softplus_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.log(np.exp(x) + 1) expect(node, inputs=[x], outputs=[y], name="test_softplus") ```
### **Softsign** Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The softsign (x/(1+|x|)) values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
softsign ```python node = onnx.helper.make_node( "Softsign", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-0.5, 0, 0.5]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_softsign_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = x / (1 + np.abs(x)) expect(node, inputs=[x], outputs=[y], name="test_softsign") ```
### **SpaceToDepth** SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1 #### Attributes
blocksize : int (required)
Blocks of [blocksize, blocksize] are moved.
#### Inputs
input (differentiable) : T
Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
#### Outputs
output (differentiable) : T
Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
#### Examples
example ```python node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=2, ) # (1, 1, 4, 6) input tensor x = np.array( [ [ [ [0, 6, 1, 7, 2, 8], [12, 18, 13, 19, 14, 20], [3, 9, 4, 10, 5, 11], [15, 21, 16, 22, 17, 23], ] ] ] ).astype(np.float32) # (1, 4, 2, 3) output tensor y = np.array( [ [ [[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]], [[12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23]], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example") ```
spacetodepth ```python b, c, h, w = shape = (2, 2, 6, 6) blocksize = 2 node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=blocksize, ) x = np.random.random_sample(shape).astype(np.float32) tmp = np.reshape( x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize] ) tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4]) y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize]) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth") ```
### **Split** Split a tensor into a list of tensors, along the specified 'axis'. Either input 'split' or the attribute 'num_outputs' should be specified, but not both. If the attribute 'num_outputs' is specified, then the tensor is split into equal sized parts. If the tensor is not evenly splittable into `num_outputs`, the last chunk will be smaller. If the input 'split' is specified, it indicates the sizes of each output in the split. #### Version This version of the operator has been available since version 18 of the default ONNX operator set. Other versions of this operator: 1, 2, 11, 13 #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
num_outputs : int
Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.
#### Inputs (1 - 2)
input (differentiable) : T
The tensor to split
split (optional, non-differentiable) : tensor(int64)
Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.
#### Outputs (1 - ∞)
outputs (variadic, differentiable) : T
One or more outputs forming list of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
#### Examples
1d_opset13 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
1d_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset18", ) ```
1d_uneven_split_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3", "output_4"], num_outputs=4, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), np.array([7.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_1d_uneven_split_opset18", ) ```
2d_opset13 ```python node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1 ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
2d_opset18 ```python node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1, num_outputs=2, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset18", ) ```
2d_uneven_split_opset18 ```python node_input = np.array( [ [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0], ] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=1, num_outputs=3, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32), np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_2d_uneven_split_opset18", ) ```
default_values_opset13 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
default_values_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset18", ) ```
zero_size_splits_opset13 ```python # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
zero_size_splits_opset18 ```python # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset18", ) ```
### **SplitToSequence** Split a tensor into a sequence of tensors, along the specified 'axis'. Lengths of the parts can be specified using the optional argument 'split'. If the argument `split' is not specified, a default scalar value of 1 is used as the value of `split'. 'split' must contain only positive numbers. 'split' is either a scalar (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input' will be split into chunks all of size 'split' if possible. The last chunk alone may be smaller than 'split' if the 'input' size along the given axis 'axis' is not divisible by 'split'. If 'split' is a 1-dimensional tensor, the input tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis' specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the dimension size of input tensor on 'axis'. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. Other versions of this operator: 11 #### Attributes
axis : int (default is 0)
Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].
keepdims : int (default is 1)
Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.
#### Inputs (1 - 2)
input : T
The tensor to split
split (optional) : I
Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0.
#### Outputs
output_sequence : S
One or more outputs forming a sequence of tensors after splitting
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input types to all tensor types.
I : tensor(int32), tensor(int64)
Constrain split size to integral tensor.
S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
Constrain output types to all tensor types.
#### Examples
nokeepdims ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) node = onnx.helper.make_node( "SplitToSequence", ["data"], ["seq"], axis=1, keepdims=0, ) expected_outputs = [[data[:, i] for i in range(data.shape[1])]] expect( node, inputs=[data], outputs=expected_outputs, name="test_split_to_sequence_nokeepdims", ) ```
with_split_1 ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array(2, dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=1 ) expected_outputs = [ [ np.array([[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]], dtype=np.float32), np.array([[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]], dtype=np.float32), np.array([[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]], dtype=np.float32), ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_1", ) ```
with_split_2 ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array([1, 2], dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=0 ) expected_outputs = [ [ data[:1], data[1:], ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_2", ) ```
### **Sqrt** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
sqrt ```python node = onnx.helper.make_node( "Sqrt", inputs=["x"], outputs=["y"], ) x = np.array([1, 4, 9]).astype(np.float32) y = np.sqrt(x) # expected output [1., 2., 3.] expect(node, inputs=[x], outputs=[y], name="test_sqrt_example") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) y = np.sqrt(x) expect(node, inputs=[x], outputs=[y], name="test_sqrt") ```
### **Squeeze** Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 11, 13, 21, 23, 24 #### Inputs (1 - 2)
data (differentiable) : T
Tensors with at least max(dims) dimensions.
axes (optional, non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
#### Outputs
squeezed (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
#### Examples
squeeze ```python node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) axes = np.array([0], dtype=np.int64) y = np.squeeze(x, axis=0) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze") ```
squeeze_negative_axes ```python node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2], dtype=np.int64) y = np.squeeze(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes") ```
### **StringConcat** StringConcat concatenates string tensors elementwise (with NumPy-style broadcasting support) #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Inputs
X (non-differentiable) : T
Tensor to prepend in concatenation
Y (non-differentiable) : T
Tensor to append in concatenation
#### Outputs
Z (non-differentiable) : T
Concatenated string tensor
#### Type Constraints
T : tensor(string)
Inputs and outputs must be UTF-8 strings
#### Examples
stringconcat ```python node = onnx.helper.make_node( "StringConcat", inputs=["x", "y"], outputs=["result"], ) x = np.array(["abc", "def"]).astype("object") y = np.array([".com", ".net"]).astype("object") result = np.array(["abc.com", "def.net"]).astype("object") expect(node, inputs=[x, y], outputs=[result], name="test_string_concat") x = np.array(["cat", "dog", "snake"]).astype("object") y = np.array(["s"]).astype("object") result = np.array(["cats", "dogs", "snakes"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_broadcasting", ) x = np.array("cat").astype("object") y = np.array("s").astype("object") result = np.array("cats").astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_zero_dimensional", ) x = np.array(["abc", ""]).astype("object") y = np.array(["", "abc"]).astype("object") result = np.array(["abc", "abc"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_empty_string", ) x = np.array(["įš„", "中"]).astype("object") y = np.array(["įš„", "中"]).astype("object") result = np.array(["įš„įš„", "中中"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_utf8", ) ```
### **StringNormalizer** StringNormalization performs string operations for basic cleaning. This operator has only one input (denoted by X) and only one output (denoted by Y). This operator first examines the elements in the X, and removes elements specified in "stopwords" attribute. After removing stop words, the intermediate result can be further lowercased, uppercased, or just returned depending the "case_change_action" attribute. This operator only accepts [C]- and [1, C]-tensor. If all elements in X are dropped, the output will be the empty value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C]. #### Version This version of the operator has been available since version 10 of the default ONNX operator set. #### Attributes
case_change_action : string (default is NONE)
string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"
is_case_sensitive : int (default is 0)
Boolean. Whether the identification of stop words in X is case-sensitive. Default is false
locale : string
Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.
stopwords : list of strings
List of stop words. If not set, no word would be removed from X.
#### Inputs
X : tensor(string)
UTF-8 strings to normalize
#### Outputs
Y : tensor(string)
UTF-8 Normalized strings
#### Type Constraints #### Examples
monday_casesensintive_lower ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="LOWER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_lower", ) ```
monday_casesensintive_nochangecase ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_nochangecase", ) ```
monday_casesensintive_upper ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_upper", ) ```
monday_empty_output ```python input = np.array(["monday", "monday"]).astype(object) output = np.array([""]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_empty_output", ) ```
monday_insensintive_upper_twodim ```python input = ( np.array( ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"] ) .astype(object) .reshape([1, 6]) ) # It does upper case cecedille, accented E # and german umlaut but fails # with german eszett output = ( np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"]) .astype(object) .reshape([1, 4]) ) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_insensintive_upper_twodim", ) ```
nostopwords_nochangecase ```python input = np.array(["monday", "tuesday"]).astype(object) output = input # No stopwords. This is a NOOP node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_nostopwords_nochangecase", ) ```
### **StringSplit** StringSplit splits a string tensor's elements into substrings based on a delimiter attribute and a maxsplit attribute. The first output of this operator is a tensor of strings representing the substrings from splitting each input string on the `delimiter` substring. This tensor has one additional rank compared to the input tensor in order to store the substrings for each input element (where the input tensor is not empty). Note that, in order to ensure the same number of elements are present in the final dimension, this tensor will pad empty strings as illustrated in the examples below. Consecutive delimiters are not grouped together and are deemed to delimit empty strings, except if the `delimiter` is unspecified or is the empty string (""). In the case where the `delimiter` is unspecified or the empty string, consecutive whitespace characters are regarded as a single separator and leading or trailing whitespace is removed in the output. The second output tensor represents the number of substrings generated. `maxsplit` can be used to limit the number of splits performed - after the `maxsplit`th split if the string is not fully split, the trailing suffix of input string after the final split point is also added. For elements where fewer splits are possible than specified in `maxsplit`, it has no effect. #### Version This version of the operator has been available since version 20 of the default ONNX operator set. #### Attributes
delimiter : string
Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.
maxsplit : int
Maximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.
#### Inputs
X (non-differentiable) : T1
Tensor of strings to split.
#### Outputs
Y (non-differentiable) : T2
Tensor of substrings representing the outcome of splitting the strings in the input on the delimiter. Note that to ensure the same number of elements are present in the final rank, this tensor will pad any necessary empty strings.
Z (non-differentiable) : T3
The number of substrings generated for each input element.
#### Type Constraints
T1 : tensor(string)
The input must be a UTF-8 string tensor
T2 : tensor(string)
Tensor of substrings.
T3 : tensor(int64)
The number of substrings generated.
#### Examples
basic ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=".", maxsplit=None, ) x = np.array(["abc.com", "def.net"]).astype(object) substrings = np.array([["abc", "com"], ["def", "net"]]).astype(object) length = np.array([2, 2], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_basic", ) ```
consecutive_delimiters ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter="-", maxsplit=None, ) x = np.array(["o-n-n--x-", "o-n----nx"]).astype(object) substrings = np.array( [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]] ).astype(object) length = np.array([6, 6], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_consecutive_delimiters", ) ```
empty_string_delimiter ```python for delimiter, test_name in ( ("", "test_string_split_empty_string_delimiter"), (None, "test_string_split_no_delimiter"), ): node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=delimiter, maxsplit=None, ) x = np.array( ["hello world !", " hello world !", " hello world ! "] ).astype(object) substrings = np.array( [ ["hello", "world", "!"], ["hello", "world", "!"], ["hello", "world", "!"], ] ).astype(object) length = np.array([3, 3, 3], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name=test_name, ) ```
empty_string_split ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=None, maxsplit=None, ) x = np.array([]).astype(object) substrings = np.array([]).astype(object).reshape(0, 0) length = np.array([], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_empty_tensor", output_type_protos=[ onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, (0, None)), None, ], ) ```
maxsplit ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], maxsplit=2, ) x = np.array( [["hello world", "def.net"], ["o n n x", "the quick brown fox"]] ).astype(object) substrings = np.array( [ [["hello", "world", ""], ["def.net", "", ""]], [["o", "n", "n x"], ["the", "quick", "brown fox"]], ] ).astype(object) length = np.array([[2, 1], [3, 3]], np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_maxsplit", ) ```
### **Sub** Performs element-wise binary subtraction (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. Other versions of this operator: 1, 6, 7, 13 #### Inputs
A (differentiable) : T
First operand.
B (differentiable) : T
Second operand.
#### Outputs
C (differentiable) : T
Result, has same element type as two inputs
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to all numeric tensors.
#### Examples
sub ```python node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([3, 2, 1]).astype(np.float32) z = x - y # expected output [-2., 0., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_sub_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint32") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint64) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint64") ```
sub_broadcast ```python node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_bcast") ```
### **Sum** Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6, 8 #### Inputs (1 - ∞)
data_0 (variadic, differentiable) : T
List of tensors for sum.
#### Outputs
sum (differentiable) : T
Output tensor.
#### Type Constraints
T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to float tensors.
#### Examples
sum ```python data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([6, 9, 12]).astype(np.float32) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_sum_example", ) node = onnx.helper.make_node( "Sum", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_sum_one_input") result = np.add(data_0, data_1) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_sum_two_inputs" ) ```
### **Swish** Swish function takes one input data (Tensor) and produces one output data (Tensor) of the same shape, where $Swish(x) = x * sigmoid(alpha * x)$. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
alpha : float (default is 1.0)
Coefficient to multiply with input before sigmoid.
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(float16), tensor(float), tensor(bfloat16), tensor(double)
Constrain input and output types to float tensors.
#### Examples
swish ```python node = onnx.helper.make_node( "Swish", inputs=["x"], outputs=["y"], alpha=1.0, # pass alpha as attribute ) x = np.array([3, 4, 5], dtype=np.float32) y = swish(x, alpha=1.0) expect( node, inputs=[x], outputs=[y], name="test_swish", opset_imports=[onnx.helper.make_opsetid("", 24)], ) ```
### **Tan** Calculates the tangent of the given input tensor, element-wise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 7 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The tangent of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
tan ```python node = onnx.helper.make_node( "Tan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan") ```
### **Tanh** Calculates the hyperbolic tangent of the given input tensor element-wise. #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
input (differentiable) : T
Input tensor
#### Outputs
output (differentiable) : T
The hyperbolic tangent values of the input tensor computed element-wise
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
tanh ```python node = onnx.helper.make_node( "Tanh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tanh(x) # expected output [-0.76159418, 0., 0.76159418] expect(node, inputs=[x], outputs=[y], name="test_tanh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tanh(x) expect(node, inputs=[x], outputs=[y], name="test_tanh") ```
### **TensorScatter** TensorScatter is a generic tensor update operation, motivated by the requirements for KV cache updates for Attention ops commonly found in LLMs. It is a functional operation that models an in-place update to a KV cache buffer. The past and present cache tensors have the same shape (batch_size, D1, D2, ..., max_sequence_length, ..., Dn), with the sequence dimension (indicated by the `axis` attribute) being max_sequence_length, so the sizes of these tensors do not need to grow between iterations. The `update` tensor's shape only differs from the cache tensors in the sequence dimension: (batch_size, D1, D2, ..., sequence_length, ..., Dn), where sequence_length <= max_sequence_length. The optional `write_indices` input indicates the write index for each sample in the batch, assumed to be zero if not provided. When the `mode` attribute is set to "circular", the write index is modulo max_sequence_length. The operation can be described using the following pseudocode: ``` for prefix_idx in np.ndindex(past_cache.shape[:axis]): batch_idx = prefix_idx[0] for sequence_idx in range(sequence_length): cache_idx = (*prefix_idx, write_indices[batch_idx] + sequence_idx) if mode == "circular": cache_idx = tuple(np.mod(np.asarray(cache_idx), max_sequence_length)) update_idx = (*prefix_idx, sequence_idx) present_cache[cache_idx] = update[update_idx] ``` During the prefill phase of attention, only the first two inputs are needed. During the decode phase, `write_indices` is also needed so that the incoming key or value update can be appended after the last valid token for each sample in the batch. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. #### Attributes
axis : int (default is -2)
Sequence dimension of the `past_cache` and `update` tensors. It cannot be 0 (the batch dimension). Default is -2.
mode : string (default is linear)
Write mode of cache update. Supported modes include `linear` and `circular`. `linear` mode requires write_indices+sequence_length<=max_sequence_length. For `circular` mode, the updates happen in wrap-around fashion, ie, the update index is modulo `max_sequence_length`
#### Inputs (2 - 3)
past_cache (differentiable) : T
Past state cache for key or value with shape `(batch_size, D1, D2, ..., max_sequence_length, ..., Dn)`.
update (differentiable) : T
New update tensor with shape `(batch_size, D1, D2, ..., sequence_length, ..., Dn)`.
write_indices (optional, non-differentiable) : tensor(int64)
Write indices for the incoming update tensor in the cache. Shape is `(batch_size,)`. Assumed to be all zeros if not provided.
#### Outputs
present_cache (differentiable) : T
Updated cache. Same shape as `past_cache`.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0)
Constrain input and output types to any tensor type.
#### Examples
tensorscatter ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="linear", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [[[5, 5, 5, 5, 5]]], [[[1, 1, 1, 1, 1]]], ], dtype=np.float32, ) write_indices = np.array([1, 2], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [1, 1, 1, 1, 1], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter", ) ```
tensorscatter_3d ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], ) past_cache = np.array( [ [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) update = np.array( [ [ [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], ], [ [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], ], ], dtype=np.float32, ) write_indices = np.array([1, 2, 0], dtype=np.int64) present_cache = np.array( [ [ [1, 2, 3, 4, 5], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_3d", ) ```
tensorscatter_circular ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="circular", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [ [ [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], ] ], [ [ [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], ] ], ], dtype=np.float32, ) write_indices = np.array([1, 3], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [4, 3, 2, 1, 0]]], [[[2, 2, 2, 2, 2], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [1, 1, 1, 1, 1]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_circular", ) ```
### **TfIdfVectorizer** This transform extracts n-grams from the input sequence and save them as a vector. Input can be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input. For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row. More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor. In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips. If the number of skips is 2, we should skip two tokens when scanning through the original sequence. Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2. The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]. If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28] indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively. The output vector (denoted by Y) stores the count of each n-gram; Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0], ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17], respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output. Note that we may consider all skips up to S when generating the n-grams. The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF", this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute. Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor. If pool_strings is set, the input must be a string tensor. #### Version This version of the operator has been available since version 9 of the default ONNX operator set. #### Attributes
max_gram_length : int (required)
Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.
max_skip_count : int (required)
Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1
min_gram_length : int (required)
Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.
mode : string (required)
The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)
ngram_counts : list of ints (required)
The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.
ngram_indexes : list of ints (required)
list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.
pool_int64s : list of ints
List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
pool_strings : list of strings
List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
weights : list of floats
list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.
#### Inputs
X (non-differentiable) : T
Input for n-gram extraction
#### Outputs
Y (non-differentiable) : T1
Ngram results
#### Type Constraints
T : tensor(string), tensor(int32), tensor(int64)
Input is ether string UTF-8 or int32/int64
T1 : tensor(float)
1-D tensor of floats
#### Examples
tf_batch_onlybigrams_skip0 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0", ) ```
tf_batch_onlybigrams_skip5 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5", ) ```
tf_batch_uniandbigrams_skip5 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5", ) ```
tf_only_bigrams_skip0 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_only_bigrams_skip0", ) ```
tf_onlybigrams_levelempty ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 0]).astype(np.int64) ngram_indexes = np.array([0, 1, 2]).astype(np.int64) pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype( # unigrams none np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_levelempty", ) ```
tf_onlybigrams_skip5 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_skip5", ) ```
tf_uniandbigrams_skip5 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_uniandbigrams_skip5", ) ```
### **ThresholdedRelu** ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. #### Version This version of the operator has been available since version 22 of the default ONNX operator set. Other versions of this operator: 10 #### Attributes
alpha : float (default is 1.0)
Threshold value
#### Inputs
X (differentiable) : T
Input tensor
#### Outputs
Y (differentiable) : T
Output tensor
#### Type Constraints
T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
default ```python default_alpha = 1.0 node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"]) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, default_alpha, np.inf) y[y == default_alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default") ```
thresholdedrelu ```python alpha = 2.0 node = onnx.helper.make_node( "ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha ) x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32) y = np.clip(x, alpha, np.inf) # expected output [0., 0., 0., 0., 2.2] y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, alpha, np.inf) y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu") ```
### **Tile** Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] #### Version This version of the operator has been available since version 13 of the default ONNX operator set. Other versions of this operator: 1, 6 #### Inputs
input (differentiable) : T
Input tensor of any shape.
repeats (non-differentiable) : T1
1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.
#### Outputs
output (differentiable) : T
Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
T1 : tensor(int64)
Constrain repeat's type to int64 tensors.
#### Examples
tile ```python node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.random.rand(2, 3, 4, 5).astype(np.float32) repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64) z = np.tile(x, repeats) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile") ```
tile_precomputed ```python node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.array([[0, 1], [2, 3]], dtype=np.float32) repeats = np.array([2, 2], dtype=np.int64) z = np.array( [[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32 ) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed") ```
### **TopK** Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: * Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis * Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). * If "largest" is 1 (the default value) then the k largest elements are returned. * If "sorted" is 1 (the default value) then the resulting k elements will be sorted. * If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined. Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. #### Version This version of the operator has been available since version 24 of the default ONNX operator set. Other versions of this operator: 1, 10, 11 #### Attributes
axis : int (default is -1)
Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
largest : int (default is 1)
Whether to return the top-K largest or smallest elements.
sorted : int (default is 1)
Whether to return the elements in sorted order.
#### Inputs
X (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{n-1}]
K (non-differentiable) : tensor(int64)
A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
#### Outputs
Values (differentiable) : T
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
Indices (non-differentiable) : I
Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
Constrain input and output types to numeric tensors.
I : tensor(int64)
Constrain index tensor to int64
#### Examples
top_k ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k" ) ```
top_k_negative_axis ```python axis = -1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_negative_axis", ) ```
top_k_same_values ```python axis = 0 largest = 0 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # (Pdb) print(values_ref) # [0 0 0] # (Pdb) print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values", ) ```
top_k_same_values_2d ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 1, 1]], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[0 0 0] # [1 1 1] # [1 1 2]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [2 3 0]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_2d", ) ```
top_k_same_values_largest ```python axis = 0 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [0 0 0] # print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_largest", ) ```
top_k_smallest ```python axis = 1 largest = 0 sorted_ = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis, largest=largest, sorted=sorted_, ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [11, 10, 9, 8], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 0. 1. 2.] # [ 4. 5. 6.] # [ 8. 9. 10.]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_smallest", ) ```
top_k_uint64 ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.uint64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3 2 1] # [ 7 6 5] # [11 10 9]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_uint64", ) ```
### **Transpose** Returns a transpose of the input tensor. (Similar to `numpy.transpose`). The optional attribute `perm` must be a permutation of the dimensions of the input tensor. Axis `i` of the output tensor corresponds to the axis `perm[i]` of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute `perm` is omitted, its default value is `(n-1, ..., 0)`, where `n` is the rank of the input tensor. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 13, 21, 23, 24 #### Attributes
perm : list of ints
A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.
#### Inputs
data (differentiable) : T
An input tensor.
#### Outputs
transposed (differentiable) : T
Transposed output.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types.
#### Examples
all_permutations ```python shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) permutations = list(itertools.permutations(np.arange(len(shape)))) for i, permutation in enumerate(permutations): node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"], perm=permutation, ) transposed = np.transpose(data, permutation) expect( node, inputs=[data], outputs=[transposed], name=f"test_transpose_all_permutations_{i}", ) ```
default ```python shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"] ) transposed = np.transpose(data) expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default") ```
### **Trilu** Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute "upper" determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the "upper" attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it. #### Version This version of the operator has been available since version 14 of the default ONNX operator set. #### Attributes
upper : int (default is 1)
Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.
#### Inputs (1 - 2)
input (differentiable) : T
Input tensor of rank 2 or higher.
k (optional, non-differentiable) : tensor(int64)
A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.
#### Outputs
output (differentiable) : T
Output tensor of the same type and shape as the input tensor.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types.
#### Examples
tril ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 0, 0, 0, 0], # [1, 2, 0, 0, 0], # [9, 4, 1, 0, 0], # [4, 3, 4, 2, 0]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril") ```
tril_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [1, 0, 0, 0, 0], # [9, 4, 0, 0, 0], # [4, 3, 4, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg") ```
tril_one_row ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) # X: # [[[6, 2, 4, 1, 6]], # # [[8, 3, 8, 7, 0]], # # [[2, 2, 9, 5, 9]]] # expect result: # [[[6, 0, 0, 0, 0]], # # [[8, 0, 0, 0, 0]], # # [[2, 0, 0, 0, 0]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg") ```
tril_out_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg") ```
tril_out_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos") ```
tril_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 0, 0], # [1, 2, 8, 6, 0], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos") ```
tril_square ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 5]], # # [[2, 0, 0], # [2, 6, 0], # [2, 6, 5]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_square") ```
tril_square_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 0]], # # [[0, 0, 0], # [2, 0, 0], # [2, 6, 0]]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg") ```
tril_zero ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero") ```
triu ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [0, 2, 8, 6, 9], # [0, 0, 0, 8, 7], # [0, 0, 0, 2, 4]] y = triu_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_triu") ```
triu_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [0, 4, 0, 8, 7], # [0, 0, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg") ```
triu_one_row ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) k = np.array(1).astype(np.int64) # X: # [[[1, 4, 9, 7, 1]], # # [[9, 2, 8, 8, 4]], # # [[3, 9, 7, 4, 2]]] # expect result: # [[[0, 4, 9, 7, 1]], # # [[0, 2, 8, 8, 4]], # # [[0, 9, 7, 4, 2]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row") ```
triu_out_neg_out ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out") ```
triu_out_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos") ```
triu_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 3, 7, 9], # [0, 0, 0, 6, 9], # [0, 0, 0, 0, 7], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos") ```
triu_square ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) y = triu_reference_implementation(x) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [0, 5, 4], # [0, 0, 2]], # # [[1, 4, 9], # [0, 6, 3], # [0, 0, 8]]] expect(node, inputs=[x], outputs=[y], name="test_triu_square") ```
triu_square_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [7, 5, 4], # [0, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [0, 9, 8]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg") ```
triu_zero ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero") ```
### **Unique** Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor 'Y' contains all unique values or subtensors of the input. The second optional output tensor 'indices' contains indices of 'Y' elements' first occurrence in 'X'. The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html Example 1: ``` input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] ``` Example 2: ``` input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2] ``` Example 3: ``` input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1] ``` Example 4: ``` input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis = 1 ``` intermediate data are presented below for better understanding: there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)): ``` A: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1], [0, 1]]. ``` there are 3 unique subtensors: ``` [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]]. ``` sorted unique subtensors: ``` B: [[0, 1], [0, 1]], [[1, 1], [1, 1]], [[2, 1], [2, 1]]. ``` output_Y is constructed from B: ``` [[[0. 1.], [1. 1.], [2. 1.]], [[0. 1.], [1. 1.], [2. 1.]]] ``` output_indices is to map from B to A: ``` [1, 0, 2] ``` output_inverse_indices is to map from A to B: ``` [1, 0, 2, 0] ``` output_counts: ``` [2, 1, 1] ``` #### Version This version of the operator has been available since version 11 of the default ONNX operator set. #### Attributes
axis : int
(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
sorted : int (default is 1)
(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).
#### Inputs
X (non-differentiable) : T
A N-D input tensor that is to be processed.
#### Outputs (1 - 4)
Y (non-differentiable) : T
A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'
indices (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing indices of 'Y' elements' first occurrence in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor.
inverse_indices (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'.
counts (optional, non-differentiable) : tensor(int64)
A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Input can be of any tensor type.
#### Examples
length_1 ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, ) x = np.array([0], dtype=np.int64) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [0] # print(indices) # [0] # print(inverse_indices) # [0] # print(counts) # [1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_length_1", ) ```
not_sorted_without_axis ```python node_not_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=0, ) # numpy unique does not retain original order (it sorts the output unique values) # https://github.com/numpy/numpy/issues/8621 # we need to recover unsorted output and indices x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) # prepare index mapping from sorted to unsorted argsorted_indices = np.argsort(indices) inverse_indices_map = dict( zip(argsorted_indices, np.arange(len(argsorted_indices)), strict=True) ) indices = indices[argsorted_indices] y = np.take(x, indices, axis=0) inverse_indices = np.asarray( [inverse_indices_map[i] for i in inverse_indices], dtype=np.int64 ) counts = counts[argsorted_indices] indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # print(y) # [2.0, 1.0, 3.0, 4.0] # print(indices) # [0 1 3 4] # print(inverse_indices) # [0, 1, 1, 2, 3, 2] # print(counts) # [1, 2, 2, 1] expect( node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_not_sorted_without_axis", ) ```
sorted_with_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=0, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[1. 0. 0.] # [2. 3. 4.]] # print(indices) # [0 2] # print(inverse_indices) # [0 0 1] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis", ) ```
sorted_with_axis_3d ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=1, ) x = np.array( [ [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], ], dtype=np.float32, ) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[[0. 1.] # [1. 1.] # [2. 1.]] # [[0. 1.] # [1. 1.] # [2. 1.]]] # print(indices) # [1 0 2] # print(inverse_indices) # [1 0 2 0] # print(counts) # [2 1 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis_3d", ) ```
sorted_with_negative_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=-1, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[0. 1.] # [0. 1.] # [3. 2.]] # print(indices) # [1 0] # print(inverse_indices) # [1 0 0] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_negative_axis", ) ```
sorted_without_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], ) x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_without_axis", ) ```
### **Unsqueeze** Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. #### Version This version of the operator has been available since version 25 of the default ONNX operator set. Other versions of this operator: 1, 11, 13, 21, 23, 24 #### Inputs
data (differentiable) : T
Original tensor
axes (non-differentiable) : tensor(int64)
1D tensor of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
#### Outputs
expanded (differentiable) : T
Reshaped tensor with same data as input.
#### Type Constraints
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(uint4), tensor(int4), tensor(float4e2m1), tensor(float8e8m0), tensor(uint2), tensor(int2)
Constrain input and output types to all tensor types up to IRv13.
#### Examples
unsqueeze_negative_axes ```python node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2]).astype(np.int64) y = np.expand_dims(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes") ```
unsqueeze_one_axis ```python x = np.random.randn(3, 4, 5).astype(np.float32) for i in range(x.ndim): axes = np.array([i]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=i) expect( node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_axis_" + str(i), ) ```
unsqueeze_three_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([2, 4, 5]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes") ```
unsqueeze_two_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([1, 4]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=1) y = np.expand_dims(y, axis=4) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes") ```
unsqueeze_unsorted_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([5, 4, 2]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes") ```
### **Upsample** (deprecated) Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). #### Version This version of the operator has been deprecated since version 10 of the default ONNX operator set. Other versions of this operator: 7, 9 #### Examples
nearest ```python node = onnx.helper.make_node( "Upsample", inputs=["X", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) output = np.array( [ [ [ [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4], [3, 3, 3, 4, 4, 4], ] ] ], dtype=np.float32, ) expect( node, inputs=[data, scales], outputs=[output], name="test_upsample_nearest", opset_imports=[helper.make_opsetid("", 9)], ) ```
### **Where** Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 16 of the default ONNX operator set. Other versions of this operator: 9 #### Inputs
condition (non-differentiable) : B
When True (nonzero), yield X, otherwise yield Y
X (differentiable) : T
values selected at indices where condition is True
Y (differentiable) : T
values selected at indices where condition is False
#### Outputs
output (differentiable) : T
Tensor of shape equal to the broadcasted shape of condition, X, and Y.
#### Type Constraints
B : tensor(bool)
Constrain to boolean tensors.
T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Constrain input and output types to all tensor types (including bfloat).
#### Examples
long ```python node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.int64) y = np.array([[9, 8], [7, 6]], dtype=np.int64) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect( node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example" ) ```
where ```python node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.float32) y = np.array([[9, 8], [7, 6]], dtype=np.float32) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example") ```
### **Xor** Returns the tensor resulted from performing the `xor` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). #### Version This version of the operator has been available since version 7 of the default ONNX operator set. Other versions of this operator: 1 #### Inputs
A (non-differentiable) : T
First input operand for the logical operator.
B (non-differentiable) : T
Second input operand for the logical operator.
#### Outputs
C (non-differentiable) : T1
Result tensor.
#### Type Constraints
T : tensor(bool)
Constrain input to boolean tensor.
T1 : tensor(bool)
Constrain output to boolean tensor.
#### Examples
xor ```python node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor4d") ```
xor_broadcast ```python node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d") ```
## ai.onnx.preview.training ### **ai.onnx.preview.training.Adagrad** Compute one iteration of ADAGRAD, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, ADAGRAD requires some parameters: - The initial learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A learning-rate decay factor "decay_factor". - A small constant "epsilon" to avoid dividing-by-zero. At each ADAGRAD iteration, the optimized tensors are moved along a direction computed based on their estimated gradient and accumulated squared gradient. Assume that only a single tensor "X" is updated by this operator. We need the value of "X", its gradient "G", and its accumulated squared gradient "H". Therefore, variables in this operator's input list are sequentially "R", "T", "X", "G", and "H". Other parameters are given as attributes because they are usually constants. Also, the corresponding output tensors are the new value of "X" (called "X_new"), and then the new accumulated squared gradient (called "H_new"). Those outputs are computed from the given inputs following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Compute a scalar learning-rate factor. At the first update of X, T is generally // 0 (0-based update index) or 1 (1-based update index). r = R / (1 + T * decay_factor); // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G; // Compute new accumulated squared gradient. H_new = H + G_regularized * G_regularized; // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...) // computes element-wise square-root. H_adaptive = Sqrt(H_new) + epsilon // Compute the new value of "X". X_new = X - r * G_regularized / H_adaptive; If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then just reuse the entire pseudo code. Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf. In that reference paper, this operator is a special case of the Figure 1's composite mirror descent update. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
decay_factor : float (default is 0.0)
The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.
epsilon : float (default is 0.0)
Small scalar to avoid dividing by zero.
norm_coefficient : float (default is 0.0)
Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
#### Inputs (3 - ∞)
R : T1
The initial learning rate.
T : T2
The update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
adagrad ```python # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 # Create operator. node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X", "G", "H"], outputs=["X_new", "H_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.0], dtype=np.float32) g = np.array([-1.0], dtype=np.float32) h = np.array([2.0], dtype=np.float32) # Compute expected outputs of Adagrad. x_new, h_new = apply_adagrad( r, t, x, g, h, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x, g, h], outputs=[x_new, h_new], name="test_adagrad", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
adagrad_multiple ```python # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) h1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) h2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Adagrad. x1_new, h1_new = apply_adagrad( r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor ) x2_new, h2_new = apply_adagrad( r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, h1, h2], outputs=[x1_new, x2_new, h1_new, h2_new], name="test_adagrad_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
### **ai.onnx.preview.training.Adam** Compute one iteration of Adam, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. First of all, Adam requires some parameters: - The learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A small constant "epsilon" to avoid dividing-by-zero. - Two coefficients, "alpha" and "beta". At each Adam iteration, the optimized tensors are moved along a direction computed based on their exponentially-averaged historical gradient and exponentially-averaged historical squared gradient. Assume that only a tensor "X" is being optimized. The rest of required information is - the value of "X", - "X"'s gradient (denoted by "G"), - "X"'s exponentially-averaged historical gradient (denoted by "V"), and - "X"'s exponentially-averaged historical squared gradient (denoted by "H"). Some of those parameters are passed into this operator as input tensors and others are stored as this operator's attributes. Specifically, this operator's input tensor list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is the second input, and so on. Other parameters are given as attributes because they are constants. Moreover, the corresponding output tensors are - the new value of "X" (called "X_new"), - the new exponentially-averaged historical gradient (denoted by "V_new"), and - the new exponentially-averaged historical squared gradient (denoted by "H_new"). Those outputs are computed following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G // Update exponentially-averaged historical gradient. V_new = alpha * V + (1 - alpha) * G_regularized // Update exponentially-averaged historical squared gradient. H_new = beta * H + (1 - beta) * G_regularized * G_regularized // Compute the element-wise square-root of H_new. V_new will be element-wisely // divided by H_sqrt for a better update direction. H_sqrt = Sqrt(H_new) + epsilon // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power. R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R // Compute new value of "X". X_new = X - R_adjusted * V_new / H_sqrt // Post-update regularization. X_final = (1 - norm_coefficient_post) * X_new If there are multiple inputs to be optimized, the pseudo code will be applied independently to each of them. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
alpha : float (default is 0.9)
Coefficient of previously accumulated gradient in running average. Default to 0.9.
beta : float (default is 0.999)
Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.
epsilon : float (default is 0.0)
Small scalar to avoid dividing by zero.
norm_coefficient : float (default is 0.0)
Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
norm_coefficient_post : float (default is 0.0)
Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
#### Inputs (3 - ∞)
R : T1
The initial learning rate.
T : T2
The update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input and output types to float tensors.
#### Examples
adam ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 epsilon = 1e-7 # Create operator. node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X", "G", "V", "H"], outputs=["X_new", "V_new", "H_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, epsilon=epsilon, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) h = np.array([0.1, 0.1], dtype=np.float32) # Compute expected outputs of Adam. x_new, v_new, h_new = apply_adam( r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x, g, v, h], outputs=[x_new, v_new, h_new], name="test_adam", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
adam_multiple ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 epsilon = 1e-2 node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) h1 = np.array([0.5], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) h2 = np.array([1.0, 10.0], dtype=np.float32) # Compute expected outputs of Adam. x1_new, v1_new, h1_new = apply_adam( r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon ) x2_new, v2_new, h2_new = apply_adam( r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2], outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new], name="test_adam_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
### **ai.onnx.preview.training.Gradient** Gradient operator computes the partial derivatives of a specific tensor w.r.t. some other tensors. This operator is widely used in gradient-based training algorithms. To illustrate its use, let's consider a computation graph, ``` X -----. | v W --> Conv --> H --> Gemm --> Y ^ | Z ``` , where W and Z are trainable tensors. Note that operators' attributes are omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of Y with respect to W (Z). The user can compute gradient by inserting Gradient operator to form another graph shown below. ``` W --> Conv --> H --> Gemm --> Y | ^ ^ | | | | X Z | | | | | .----------' | | | (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in | | | "xs" followed by "zs") | v v '---> Gradient(xs=["W", "Z"], zs=["X"], y="Y") | | | '-----------------------------------> dY/dW (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` By definition, the tensor "y" is a function of independent variables in "xs" and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H" cannot appear in "xs" and "zs". The reason is that "H" can be determined by tensors "W" and "X" and therefore "H" is not an independent variable. All outputs are optional. If needed, for example, user can assign an empty string to the 1st output name of that Gradient to skip the generation of dY/dW. Note that the concept of optional outputs can also be found in ONNX's RNN, GRU, and LSTM. Gradient operator can compute derivative against intermediate tensors. For example, the gradient of Y with respect to H can be done via ``` W --> Conv --> H --> Gemm --> Y ^ | ^ | | | X | Z .-------' | | .----------' | | (H/Z is the 1st/2nd input of Gradient as shown in "xs") v v Gradient(xs=["H", "Z"], y="Y") | | | '-----------------------------------> dY/dH (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` It is possible to represent high-order differentiation using Gradient operators. For example, given the following linear model: ``` W --> Gemm --> Y --> Loss --> O ^ ^ | | X L ``` To compute the 2nd order derivative of O with respect to W (denoted by d^2O/dW^2), one can do ``` W --> Gemm --> Y --> Loss --> O | ^ ^ | | | | X .------------L | | | | | | | v +------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient) | | | | | | | '---> dO/dW (2nd output of Gradient) | v v '---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of | Gradient) | | '---> d^2O/dW^2 (2nd output of Gradient) ``` The tensors named in attributes "xs", "zs", and "y" define the differentiated computation graph, and the inputs to Gradient node define the values at which the gradient is computed. We can feed different tensors to the identified graph. For example, one can compute the gradient of Y with respect to H at a specific value of H, H_1, by providing that value as an input to the Gradient node. ``` W --> Conv --> H --> Gemm --> Y ^ ^ | | X Z Z_1 (2nd input of Gradient) | v H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1. | '------------------------------> dY/dZ (2nd output of Gradient) ``` When the inputs of Gradient are the tensors named in "xs" and "zs", the computation can be optimized. More specifically, intermediate variables in forward pass can be reused if the gradient is computed via reverse-mode auto-differentiation. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
xs : list of strings (required)
Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
y : string (required)
The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".
zs : list of strings
Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
#### Inputs (1 - ∞)
Inputs (variadic, heterogeneous) : T1
The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".
#### Outputs (1 - ∞)
Outputs (variadic, heterogeneous) : T2
The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".
#### Type Constraints
T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
Allow outputs to be any kind of tensor.
T2 : tensor(float16), tensor(float), tensor(double)
Allow inputs to be any kind of floating-point tensor.
#### Examples
gradient_scalar_add ```python add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dc_da", "dc_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="c", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # dc / da = d(a+b) / da = 1 dc_da = np.array(1).astype(np.float32) # db / db = d(a+b) / db = 1 dc_db = np.array(1).astype(np.float32) graph = onnx.helper.make_graph( nodes=[add_node, gradient_node], name="GradientOfAdd", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add" ) ```
gradient_scalar_add_and_mul ```python add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dd_da", "dd_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="d", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # d = a * c = a * (a + b) d = a * c # dd / da = d(a*a+a*b) / da = 2 * a + b dd_da = (2 * a + b).astype(np.float32) # dd / db = d(a*a+a*b) / db = a dd_db = a graph = onnx.helper.make_graph( nodes=[add_node, mul_node, gradient_node], name="GradientOfTwoOperators", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[d, dd_da, dd_db], name="test_gradient_of_add_and_mul", ) ```
### **ai.onnx.preview.training.Momentum** Compute one iteration of stochastic gradient update with momentum. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, SG with momentum requires several parameters: - The learning-rate "R". - The update count "T". That is, the number of conducted training iterations. It should be zero in the first training iteration. - A L2-norm regularization coefficient "norm_coefficient". - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha". - The scaling coefficient of current gradient "beta". - An attribute to choose either standard momentum or Nesterov's momentum "mode" should be used. For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized. Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new momentum (called "V_new"). This operator supports two different momentum algorithms. Set the attribute "mode" to "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to "standard" to use standard momentum. Computation details are described subsequently. Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting. Pseudo code for SG with standard momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized // Update X. X_new = X - R * V_new Pseudo code for SG with Nesterov's momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G; // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized; // Compute final update direction and then update X. X_new = X - R * (G_regularized + alpha * V_new) If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then our pseudo code becomes applicable. #### Version This version of the operator has been available since version 1 of the 'ai.onnx.preview.training' operator set. #### Attributes
alpha : float (required)
The decay factor of momentum. It should be a scalar.
beta : float (required)
The coefficient of gradient in computing new momentum. It should be a scalar.
mode : string (required)
Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum
norm_coefficient : float (required)
Coefficient of 0.5 * norm_coefficient * ||X||^2.
#### Inputs (3 - ∞)
R : T1
The learning rate.
T : T2
Update count of "X". It should be a scalar.
inputs (variadic, heterogeneous) : T3
It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].
#### Outputs (1 - ∞)
outputs (variadic, heterogeneous) : T3
It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].
#### Type Constraints
T1 : tensor(float), tensor(double)
Constrain input types to float scalars.
T2 : tensor(int64)
Constrain input types to 64-bit integer scalars.
T3 : tensor(float), tensor(double)
Constrain input types to float tensors.
#### Examples
momentum ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
momentum_multiple ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Momentum. x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta) x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2], outputs=[x1_new, x2_new, v1_new, v2_new], name="test_momentum_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
nesterov_momentum ```python # Define operator attributes. norm_coefficient = 0.01 alpha = 0.95 beta = 1.0 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="nesterov", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_nesterov_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
onnx-onnx-bca0315/docs/Overview.md000066400000000000000000000050251511334557700171510ustar00rootroot00000000000000 # Overview Deep learning with neural networks is accomplished through computation over dataflow graphs. Some frameworks (such as CNTK, Caffe2, Theano, and TensorFlow) make use of static graphs, while others (such as PyTorch and Chainer) use dynamic graphs. However, they all provide interfaces that make it simple for developers to construct computation graphs and runtimes that process the graphs in an optimized way. The graph serves as an Intermediate Representation (IR) that captures the specific intent of the developer's source code, and is conducive for optimization and translation to run on specific devices (CPU, GPU, FPGA, etc.). ## Why a common IR? Today, each framework has its own proprietary representation of the graph, though they all provide similar capabilities – meaning each framework is a siloed stack of API, graph, and runtime. Furthermore, frameworks are typically optimized for some characteristic, such as fast training, supporting complicated network architectures, inference on mobile devices, etc. It's up to the developer to select a framework that is optimized for one of these characteristics. Additionally, these optimizations may be better suited for particular stages of development. This leads to significant delays between research and production due to the necessity of conversion. With the goal of democratizing AI, we envision empowering developers to select the framework that works best for their project, at any stage of development or deployment. The Open Neural Network Exchange (ONNX) format is a common IR to help establish this powerful ecosystem. By providing a common representation of the computation graph, ONNX helps developers choose the right framework for their task, allows authors to focus on innovative enhancements, and enables hardware vendors to streamline optimizations for their platforms. ONNX is designed to be an open format. We welcome contributions from the community and encourage everyone to adopt ONNX in their ecosystem. ## Why two variants? The base definition of ONNX includes the necessary support for machine learning algorithms based on neural network technologies. ONNX-ML includes additional types and standard operators commonly used in classical machine learning algorithms. The two variants were created in order to explicitly recognize the desire for some frameworks to go beyond neural network algorithms in a standardized fashion, while allowing other frameworks to support only neural networks. onnx-onnx-bca0315/docs/PythonAPIOverview.md000066400000000000000000000401211511334557700207010ustar00rootroot00000000000000 # Python API Overview The full API is described at [API Reference](https://onnx.ai/onnx/api). ## Loading an ONNX Model ```python import onnx # onnx_model is an in-memory ModelProto onnx_model = onnx.load("path/to/the/model.onnx") ``` Runnable IPython notebooks: - [load_model.ipynb](/examples/load_model.ipynb) ## Loading an ONNX Model with External Data * [Default] If the external data is under the same directory of the model, simply use `onnx.load()` ```python import onnx onnx_model = onnx.load("path/to/the/model.onnx") ``` * If the external data is under another directory, use `load_external_data_for_model()` to specify the directory path and load after using `onnx.load()` ```python import onnx from onnx.external_data_helper import load_external_data_for_model onnx_model = onnx.load("path/to/the/model.onnx", load_external_data=False) load_external_data_for_model(onnx_model, "data/directory/path/") # Then the onnx_model has loaded the external data from the specific directory ``` ## Converting an ONNX Model to External Data ```python from onnx.external_data_helper import convert_model_to_external_data # onnx_model is an in-memory ModelProto onnx_model = ... convert_model_to_external_data(onnx_model, all_tensors_to_one_file=True, location="filename", size_threshold=1024, convert_attribute=False) # Then the onnx_model has converted raw data as external data # Must be followed by save ``` ## Saving an ONNX Model ```python import onnx # onnx_model is an in-memory ModelProto onnx_model = ... # Save the ONNX model onnx.save(onnx_model, "path/to/the/model.onnx") ``` Runnable IPython notebooks: - [save_model.ipynb](/examples/save_model.ipynb) ## Converting and Saving an ONNX Model to External Data ```python import onnx # onnx_model is an in-memory ModelProto onnx_model = ... onnx.save_model(onnx_model, "path/to/save/the/model.onnx", save_as_external_data=True, all_tensors_to_one_file=True, location="filename", size_threshold=1024, convert_attribute=False) # Then the onnx_model has converted raw data as external data and saved to specific directory ``` ## Manipulating TensorProto and Numpy Array ```python import numpy import onnx from onnx import numpy_helper # Preprocessing: create a Numpy array numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float) print(f"Original Numpy array:\n{numpy_array}\n") # Convert the Numpy array to a TensorProto tensor = numpy_helper.from_array(numpy_array) print(f"TensorProto:\n{tensor}") # Convert the TensorProto to a Numpy array new_array = numpy_helper.to_array(tensor) print(f"After round trip, Numpy array:\n{new_array}\n") # Save the TensorProto with open("tensor.pb", "wb") as f: f.write(tensor.SerializeToString()) # Load a TensorProto new_tensor = onnx.TensorProto() with open("tensor.pb", "rb") as f: new_tensor.ParseFromString(f.read()) print(f"After saving and loading, new TensorProto:\n{new_tensor}") from onnx import TensorProto, helper # Conversion utilities for mapping attributes in ONNX IR # The functions below are available after ONNX 1.13 np_dtype = helper.tensor_dtype_to_np_dtype(TensorProto.FLOAT) print(f"The converted numpy dtype for {helper.tensor_dtype_to_string(TensorProto.FLOAT)} is {np_dtype}.") storage_dtype = helper.tensor_dtype_to_storage_tensor_dtype(TensorProto.FLOAT) print(f"The storage dtype for {helper.tensor_dtype_to_string(TensorProto.FLOAT)} is {helper.tensor_dtype_to_string(storage_dtype)}.") field_name = helper.tensor_dtype_to_field(TensorProto.FLOAT) print(f"The field name for {helper.tensor_dtype_to_string(TensorProto.FLOAT)} is {field_name}.") tensor_dtype = helper.np_dtype_to_tensor_dtype(np_dtype) print(f"The tensor data type for numpy dtype: {np_dtype} is {helper.tensor_dtype_to_string(tensor_dtype)}.") for tensor_dtype in helper.get_all_tensor_dtypes(): print(helper.tensor_dtype_to_string(tensor_dtype)) ``` Runnable IPython notebooks: - [np_array_tensorproto.ipynb](/examples/np_array_tensorproto.ipynb) ## Creating an ONNX Model Using Helper Functions ```python import onnx from onnx import helper, AttributeProto, TensorProto, GraphProto # Create inputs and output value info X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2]) pads = helper.make_tensor_value_info("pads", TensorProto.INT64, [8]) # pads is INT64 Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [5, 4]) # Create Pad node with 'value' attribute (not input) node_def = helper.make_node( "Pad", inputs=["X", "pads"], # Inputs: X and pads (INT64) outputs=["Y"], mode="constant", # Attribute for padding mode value=0.0 # Attribute for fill value ) # Build graph and model graph_def = helper.make_graph( [node_def], "test-model", [X, pads], [Y], ) model_def = helper.make_model( graph_def, producer_name="onnx-example", opset_imports=[helper.make_opsetid("", 11)] # OPSET 11 required ) # Validate the model onnx.checker.check_model(model_def) print("Model is valid!") ``` Runnable IPython notebooks: - [make_model.ipynb](/examples/make_model.ipynb) - [Protobufs.ipynb](/examples/Protobufs.ipynb) ## Conversion utilities for mapping attributes in ONNX IR ```python from onnx import TensorProto, helper np_dtype = helper.tensor_dtype_to_np_dtype(TensorProto.FLOAT) print(f"The converted numpy dtype for {helper.tensor_dtype_to_string(TensorProto.FLOAT)} is {np_dtype}.") field_name = helper.tensor_dtype_to_field(TensorProto.FLOAT) print(f"The field name for {helper.tensor_dtype_to_string(TensorProto.FLOAT)} is {field_name}.") # There are other useful conversion utilities. Please checker onnx.helper ``` ## Checking an ONNX Model ```python import onnx # Preprocessing: load the ONNX model model_path = "path/to/the/model.onnx" onnx_model = onnx.load(model_path) print(f"The model is:\n{onnx_model}") # Check the model try: onnx.checker.check_model(onnx_model) except onnx.checker.ValidationError as e: print(f"The model is invalid: {e}") else: print("The model is valid!") ``` Runnable IPython notebooks: - [check_model.ipynb](/examples/check_model.ipynb) ### Checking a Large ONNX Model >2GB Current checker supports checking models with external data, but for those models larger than 2GB, please use the model path for onnx.checker and the external data needs to be under the same directory. ```python import onnx onnx.checker.check_model("path/to/the/model.onnx") # onnx.checker.check_model(loaded_onnx_model) will fail if given >2GB model ``` ## Running Shape Inference on an ONNX Model ```python import onnx from onnx import helper, shape_inference from onnx import TensorProto # Preprocessing: create a model with two nodes, Y"s shape is unknown node1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2]) node2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[1, 0, 2]) graph = helper.make_graph( [node1, node2], "two-transposes", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 4))], ) original_model = helper.make_model(graph, producer_name="onnx-examples") # Check the model and print Y"s shape information onnx.checker.check_model(original_model) print(f"Before shape inference, the shape info of Y is:\n{original_model.graph.value_info}") # Apply shape inference on the model inferred_model = shape_inference.infer_shapes(original_model) # Check the model and print Y"s shape information onnx.checker.check_model(inferred_model) print(f"After shape inference, the shape info of Y is:\n{inferred_model.graph.value_info}") ``` Runnable IPython notebooks: - [shape_inference.ipynb](/examples/shape_inference.ipynb) ### Shape inference a Large ONNX Model >2GB Current shape_inference supports models with external data, but for those models larger than 2GB, please use the model path for onnx.shape_inference.infer_shapes_path and the external data needs to be under the same directory. You can specify the output path for saving the inferred model; otherwise, the default output path is same as the original model path. ```python import onnx # output the inferred model to the original model path onnx.shape_inference.infer_shapes_path("path/to/the/model.onnx") # output the inferred model to the specified model path onnx.shape_inference.infer_shapes_path("path/to/the/model.onnx", "output/inferred/model.onnx") # inferred_model = onnx.shape_inference.infer_shapes(loaded_onnx_model) will fail if given >2GB model ``` ## Running Type Inference on an ONNX Function ```python import onnx import onnx.helper import onnx.parser import onnx.shape_inference function_text = """ CastTo (x) => (y) { y = Cast (x) } """ function = onnx.parser.parse_function(function_text) # The function above has one input-parameter x, and one attribute-parameter dtype. # To apply type-and-shape-inference to this function, we must supply the type of # input-parameter and an attribute value for the attribute-parameter as below: float_type_ = onnx.helper.make_tensor_type_proto(1, None) dtype_6 = onnx.helper.make_attribute("dtype", 6) result = onnx.shape_inference.infer_function_output_types( function, [float_type_], [dtype_6] ) print(result) # a list containing the (single) output type ``` ## Converting Version of an ONNX Model within Default Domain (""/"ai.onnx") ```python import onnx from onnx import version_converter, helper # Preprocessing: load the model to be converted. model_path = "path/to/the/model.onnx" original_model = onnx.load(model_path) print(f"The model before conversion:\n{original_model}") # A full list of supported adapters can be found here: # https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21 # Apply the version conversion on the original model converted_model = version_converter.convert_version(original_model, ) print(f"The model after conversion:\n{converted_model}") ``` ## Utility Functions ### Extracting Sub-model with Inputs Outputs Tensor Names Function `extract_model()` extracts sub-model from an ONNX model. The sub-model is defined by the names of the input and output tensors *exactly*. ```python import onnx input_path = "path/to/the/original/model.onnx" output_path = "path/to/save/the/extracted/model.onnx" input_names = ["input_0", "input_1", "input_2"] output_names = ["output_0", "output_1"] onnx.utils.extract_model(input_path, output_path, input_names, output_names) ``` Note: For control-flow operators, e.g. If and Loop, the *boundary of sub-model*, which is defined by the input and output tensors, should not *cut through* the subgraph that is connected to the *main graph* as attributes of these operators. ### ONNX Compose `onnx.compose` module provides tools to create combined models. `onnx.compose.merge_models` can be used to merge two models, by connecting some of the outputs from the first model with inputs from the second model. By default, inputs/outputs not present in the `io_map` argument will remain as inputs/outputs of the combined model. In this example we merge two models by connecting each output of the first model to an input in the second. The resulting model will have the same inputs as the first model and the same outputs as the second: ```python import onnx model1 = onnx.load("path/to/model1.onnx") # agraph (float[N] A, float[N] B) => (float[N] C, float[N] D) # { # C = Add(A, B) # D = Sub(A, B) # } model2 = onnx.load("path/to/model2.onnx") # agraph (float[N] X, float[N] Y) => (float[N] Z) # { # Z = Mul(X, Y) # } combined_model = onnx.compose.merge_models( model1, model2, io_map=[("C", "X"), ("D", "Y")] ) ``` Additionally, a user can specify a list of `inputs`/`outputs` to be included in the combined model, effectively dropping the part of the graph that does't contribute to the combined model outputs. In the following example, we are connecting only one of the two outputs in the first model to both inputs in the second. By specifying the outputs of the combined model explicitly, we are dropping the output not consumed from the first model, and the relevant part of the graph: ```python import onnx # Default case. Include all outputs in the combined model combined_model = onnx.compose.merge_models( model1, model2, io_map=[("C", "X"), ("C", "Y")], ) # outputs: "D", "Z" # Explicit outputs. "Y" output and the Sub node are not present in the combined model combined_model = onnx.compose.merge_models( model1, model2, io_map=[("C", "X"), ("C", "Y")], outputs=["Z"], ) # outputs: "Z" ``` `onnx.compose.add_prefix` allows you to add a prefix to names in the model, to avoid a name collision when merging them. By default, it renames all names in the graph: inputs, outputs, edges, nodes, initializers, sparse initializers and value infos. ```python import onnx model = onnx.load("path/to/the/model.onnx") # model - outputs: ["out0", "out1"], inputs: ["in0", "in1"] new_model = onnx.compose.add_prefix(model, prefix="m1/") # new_model - outputs: ["m1/out0", "m1/out1"], inputs: ["m1/in0", "m1/in1"] # Can also be run in-place onnx.compose.add_prefix(model, prefix="m1/", inplace=True) ``` `onnx.compose.expand_out_dim` can be used to connect models that expect a different number of dimensions by inserting dimensions with extent one. This can be useful, when combining a model producing samples with a model that works with batches of samples. ```python import onnx # outputs: "out0", shape=[200, 200, 3] model1 = onnx.load("path/to/the/model1.onnx") # outputs: "in0", shape=[N, 200, 200, 3] model2 = onnx.load("path/to/the/model2.onnx") # outputs: "out0", shape=[1, 200, 200, 3] new_model1 = onnx.compose.expand_out_dims(model1, dim_idx=0) # Models can now be merged combined_model = onnx.compose.merge_models( new_model1, model2, io_map=[("out0", "in0")] ) # Can also be run in-place onnx.compose.expand_out_dims(model1, dim_idx=0, inplace=True) ``` ## Tools ### Updating Model"s Inputs Outputs Dimension Sizes with Variable Length Function `update_inputs_outputs_dims` updates the dimension of the inputs and outputs of the model, to the provided values in the parameter. You could provide both static and dynamic dimension size, by using dim_param. For more information on static and dynamic dimension size, checkout [Tensor Shapes](IR.md#tensor-shapes). The function runs model checker after the input/output sizes are updated. ```python import onnx from onnx.tools import update_model_dims model = onnx.load("path/to/the/model.onnx") # Here both "seq", "batch" and -1 are dynamic using dim_param. variable_length_model = update_model_dims.update_inputs_outputs_dims(model, {"input_name": ["seq", "batch", 3, -1]}, {"output_name": ["seq", "batch", 1, -1]}) ``` ## ONNX Parser Functions `onnx.parser.parse_model` and `onnx.parser.parse_graph` can be used to create an ONNX model or graph from a textual representation as shown below. See [Language Syntax](Syntax.md) for more details about the language syntax. ```python input = """ agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C) { T = MatMul(X, W) S = Add(T, B) C = Softmax(S) } """ graph = onnx.parser.parse_graph(input) input = """ < ir_version: 7, opset_import: ["" : 10] > agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C) { T = MatMul(X, W) S = Add(T, B) C = Softmax(S) } """ model = onnx.parser.parse_model(input) ``` ## ONNX Inliner Functions `onnx.inliner.inline_local_functions` and `inline_selected_functions` can be used to inline model-local functions in an ONNX model. In particular, `inline_local_functions` can be used to produce a function-free model (suitable for backends that do not handle or support functions). On the other hand, `inline_selected_functions` can be used to inline selected functions. There is no support yet for inlining ONNX standard ops that are functions (also known as schema-defined functions). ```python import onnx import onnx.inliner model = onnx.load("path/to/the/model.onnx") inlined = onnx.inliner.inline_local_functions(model) onnx.save("path/to/the/inlinedmodel.onnx") ``` onnx-onnx-bca0315/docs/Relicensing.md000066400000000000000000000007101511334557700176010ustar00rootroot00000000000000 # Relicensing MIT to Apache-2.0 The following copyright holders agree that all of their contributions originally submitted to this project under the MIT license are hereby relicensed to Apache-2.0, and are submitted pursuant to the Developer Certificate of Origin, version 1.1: Intel Corporation Microsoft Corporation NVIDIA Corporation IBM Corporation Facebook Inc. onnx-onnx-bca0315/docs/ShapeInference.md000066400000000000000000000133561511334557700202300ustar00rootroot00000000000000 # ONNX Shape Inference ONNX provides an optional implementation of shape inference on ONNX graphs. This implementation covers each of the core operators, as well as provides an interface for extensibility. Therefore, you may choose to invoke the existing shape inference functionality on your graphs, or to define shape inference implementations to go along with your custom operators (or both!). Shape inference functions are stored as a member of the OpSchema objects. In ONNX 1.10 release, symbol generation and propagation along with shape data propagation was added to ONNX graph level shape inference. Detailed proposal is [here](proposals/SymbolicShapeInfProposal.md) ## Background Please see this [section](IR.md#static-tensor-shapes) of IR.md for a review of static tensor shapes. In particular, a static tensor shape (represented by a `TensorShapeProto`) is distinct from a runtime tensor shape. This feature is commonly used when the exact runtime tensor shape is not known statically (that is, at compile time). * A `Tensor` with an undefined `shape` field is used to represent a tensor of unknown rank. * A `Tensor` with a defined `shape` represents a tensor of known rank. * Each `Dimension` of a `TensorShapeProto` can have a known integer value (represented by the `dim_value` field) or it can have an unknown value represented by a symbolic identified (the `dim_param` field) or it may have neither field defined (in which case it represents an anonymous unknown value). ## Invoking Shape Inference Shape inference can be invoked either via C++ or Python. The Python API is described, with example, [here](PythonAPIOverview.md#running-shape-inference-on-an-onnx-model). The C++ API consists of a single function ```cpp shape_inference::InferShapes( ModelProto& m, const ISchemaRegistry* schema_registry); ``` The first argument is a `ModelProto` to perform shape inference on, which is annotated in-place with shape information. The second argument is optional. ## Limitations Shape inference is not guaranteed to be complete. In particular, some dynamic behaviors block the flow of shape inference, for example a Reshape to a dynamically-provide shape. Also, all operators are not required to have a shape inference implementation. Shape inference works only with constants and simple variables. It does not support arithmetic expressions containing variables. For example, `Concat` on tensors of shapes `(5, 2)` and `(7, 2)` can be inferred to produce a result of shape `(12, 2)`, but `Concat` on tensors of shapes `(5, 2)` and `(N, 2)` will simply produce `(M, 2)`, rather than containing a representation of `N+5`. Note that differing unknown symbolic values will be propagated, so the `M` here represents an unknown quantity that is the same as other occurrences of `M`. These limitations are a property of the current implementation, not fundamental constraints - if you are in need of something more advanced, do let us know! ## Implementing Shape Inference For Operators You can add a shape inference function to your operator's Schema with ```cpp OpSchema& Opschema::TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction); ``` `InferenceFunction` is defined in [shape_inference.h](/onnx/defs/shape_inference.h), along with the core interface struct `InferenceContext` and an assortment of helper methods. `InferenceContext` is the core struct which is provided to your inference function. It allows accessing information about the operator's inputs, and also allows writing out inferred information. To see numerous examples, search for occurrences of `TypeAndShapeInferenceFunction` in the codebase. One that is relatively involved is the implementation for `Concat`, in onnx/defs/tensor/defs.cc. Please note the following points when implementing the shape-inference method for operators to avoid common errors: * Before accessing the `shape` of any input, the code must check that the shape is available. If unavailable, it should be treated as a dynamic tensor whose rank is unknown and handled appropriately. Usually, the shape-inference logic is guarded by a call to `hasInputShape` or `hasNInputShapes`. * Before accessing the `dim_value` or `dim_param` of any dimension, the code must check if these fields have a value. In particular, the code must handle the possibility that the dimension may not have a statically known value. There are several utility functions in [shape_inference.h](/onnx/defs/shape_inference.h) to handle various common situations. * Use `checkInputRank` for inputs that must have a fixed rank. (See the inference for `RoiAlign` as an example.) * `unifyInputDim` and `unifyDim` and `updateOutputShape` can be used when multiple input dims are expected to be the same, and when input dimensions are propagated to specific output dimensions. (See the inference for `RoiAlign` for an example.) * Overloaded operators `*` and `/` can be used on symbolic dimensions when output dimensions are computed from input dimensions using arithmetic. (See the inference for `SpaceToDepth` for an example.) These utilities handle missing shapes and dimensions safely. _Example_: Consider a simple matrix-multiplication op that expects inputs of shape `[M,K]` and `[K,N]` and returns an output of shape `[M,N]`. This can be coded up as below: ```cpp // Check that input 0 has rank 2 (if its rank is known). checkInputRank(ctx, 0, 2); // Check that input 1 has rank 2 (if its rank is known). checkInputRank(ctx, 1, 2); Dim M, K, N; // Check various dimensions, handling missing dimensions/shapes safely. unifyInputDim(ctx, 0, 0, M); unifyInputDim(ctx, 0, 1, K); unifyInputDim(ctx, 1, 0, K); unifyInputDim(ctx, 1, 1, N); updateOutputShape(ctx, 0, {M. N}); ``` onnx-onnx-bca0315/docs/Syntax.md000066400000000000000000000076101511334557700166330ustar00rootroot00000000000000 # ONNX Textual Syntax ## Overview This document describes a textual syntax for ONNX models, which is currently an experimental feature. The syntax enables a compact and readable representation of ONNX models. It is motivated by a couple of use-cases. One is to enable compact description of test-cases and its use in CI (both in the ONNX repo as well as in other dependent repos such as ONNX-MLIR). The second is to help simplify the definition of ONNX functions. Several of the existing function-definitions are verbose, and the use of this syntax will lead to more compact, readable, and easier-to-maintain function definitions. Efficient representation and efficient parsing of very large tensor-constants is *not* a goal. Alternative methods should be used for that. ## The API The key parser methods are the ```OnnxParser::Parse``` methods, used as below. ```cpp const char* code = R"ONNX( < ir_version: 7, opset_import: [ "" : 10 ] > agraph (float[N, 128] X, float[128, 10] W, float[10] B) => (float[N, 10] C) { T = MatMul(X, W) S = Add(T, B) C = Softmax(S) } )ONNX"; ModelProto model; OnnxParser::Parse(model, code); checker::check_model(model); ``` See the [test-cases](../onnx/test/cpp/parser_test.cc) for more examples illustrating the API and syntax. ## The Syntax The grammar below describes the syntax: ```bnf id-list ::= id (',' id)* quotable-id-list ::= quotable-id (',' quotable-id)* tensor-dim ::= '?' | id | int-constant tensor-dims ::= tensor-dim (',' tensor-dim)* tensor-type ::= prim-type | prim-type '[' ']' | prim-type '[' tensor-dims ']' type ::= tensor-type | 'seq' '(' type ')' | 'map' '(' prim-type ',' type ')' | 'optional' '(' type ')' | 'sparse_tensor' '(' tensor-type ')' value-info ::= type quotable-id value-infos ::= value-info (',' value-info)* value-info-list ::= '(' value-infos? ') id-or-value-info ::= type? quotable-id id-or-value-infos ::= id-or-value-info (',' id-or-value-info)* quoted-str :== '"' ([^"])* '"' quotable-id :== id | quoted-str str-str :== quoted-str ':' quoted-str str-str-list :== '[' str-str (',' str-str)* ']' internal-data ::= '{' prim-constants '}' external-data ::= str-str-list constant-data ::= internal-data | external-data value-info-or-initializer ::= type quotable-id [ '=' constant-data ] value-info-or-initializers ::= value-info-or-initializer (',' value-info-or-initializer)* input-list ::= '(' value-info-or-initializers? ')' output-list ::= '(' value-infos? ')' initializer-list ::= '<' value-info-or-initializers? '>' prim-constants ::= prim-constant (',' prim-constant)* tensor-constant ::= tensor-type (quotable-id)? ('=')? '{' prim-constants '}' attr-ref ::= '@' id single-attr-value ::= tensor-constant | graph | prim-constant | attr-ref attr-value-list ::= '[' single-attr-value (',' single-attr-value)* ']' attr-value ::= single-attr-value | attr-value-list attr-type ::= ':' id attr ::= id attr-type? '=' attr-value attr-list ::= '<' attr (',' attr)* '>' node-label ::= '[' quotable-id ']' node ::= node-label? quotable-id-list? '=' qualified-id attr-list? '(' quotable-id-list? ')' | node-label? quotable-id-list? '=' qualified-id '(' quotable-id-list? ')' attr-list node-list ::= '{' node* '}' graph ::= quotable-id input-list '=>' output-list initializer-list node-list other-data ::= id ':' value other-data-list ::= '<' other-data (',' other-data)* '>' fun-attr-list ::= '<' id | attr (',' id | attr)* '>' fun-input-list ::= '(' id-or-value-infos ')' fun-output-list ::= '(' id-or-value-infos ')' fun-value-infos ::= ( '<' value-infos '>' )? function ::= other-data-list? id fun-attr-list? quotable-id fun-input-list '=>' fun-output-list fun-value-infos node-list model ::= other-data-list? graph function* ``` onnx-onnx-bca0315/docs/TestCoverage-ml.md000066400000000000000000000167371511334557700203600ustar00rootroot00000000000000 # Test Coverage Report (ONNX-ML Operators) ## Outlines * [Node Test Coverage](#node-test-coverage) * [Model Test Coverage](#model-test-coverage) * [Overall Test Coverage](#overall-test-coverage) # Node Test Coverage ## Summary Node tests have covered 4/19 (21.05%, 0 generators excluded) common operators. Node tests have covered 0/0 (N/A) experimental operators. * [Covered Common Operators](#covered-common-operators) * [No Cover Common Operators](#no-cover-common-operators) * [Covered Experimental Operators](#covered-experimental-operators) * [No Cover Experimental Operators](#no-cover-experimental-operators) ## 💚Covered Common Operators ### ArrayFeatureExtractor There are 1 test cases, listed as following:
arrayfeatureextractor ```python node = onnx.helper.make_node( "ArrayFeatureExtractor", inputs=["x", "y"], outputs=["z"], domain="ai.onnx.ml", ) x = np.arange(12).reshape((3, 4)).astype(np.float32) y = np.array([0, 1], dtype=np.int64) z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T expect( node, inputs=[x, y], outputs=[z], name="test_ai_onnx_ml_array_feature_extractor", ) ```
### Binarizer There are 1 test cases, listed as following:
binarizer ```python threshold = 1.0 node = onnx.helper.make_node( "Binarizer", inputs=["X"], outputs=["Y"], threshold=threshold, domain="ai.onnx.ml", ) x = np.random.randn(3, 4, 5).astype(np.float32) y = compute_binarizer(x, threshold)[0] expect(node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_binarizer") ```
### LabelEncoder There are 2 test cases, listed as following:
string_int_label_encoder ```python node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], default_int64=42, ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, -1, 2, -1]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int_no_default", ) ```
tensor_based_label_encoder ```python tensor_keys = make_tensor( "keys_tensor", onnx.TensorProto.STRING, (3,), ["a", "b", "c"] ) repeated_string_keys = ["a", "b", "c"] x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int16) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_tensor=tensor_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_mapping", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=repeated_string_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_value_only_mapping", ) ```
### TreeEnsemble There are 2 test cases, listed as following:
tree_ensemble_set_membership ```python node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=4, aggregate_function=1, membership_values=make_tensor( "membership_values", onnx.TensorProto.FLOAT, (8,), [1.2, 3.7, 8, 9, np.nan, 12, 7, np.nan], ), nodes_missing_value_tracks_true=None, nodes_hitrates=None, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 6, 6], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.FLOAT, (3,), np.array([11, 232344.0, np.nan], dtype=np.float32), ), nodes_trueleafs=[0, 1, 1], nodes_truenodeids=[1, 0, 1], nodes_falseleafs=[1, 0, 1], nodes_falsenodeids=[2, 2, 3], leaf_targetids=[0, 1, 2, 3], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.FLOAT, (4,), [1, 10, 1000, 100] ), ) x = np.array([1.2, 3.4, -0.12, np.nan, 12, 7], np.float32).reshape(-1, 1) expected = np.array( [ [1, 0, 0, 0], [0, 0, 0, 100], [0, 0, 0, 100], [0, 0, 1000, 0], [0, 0, 1000, 0], [0, 10, 0, 0], ], dtype=np.float32, ) expect( node, inputs=[x], outputs=[expected], name="test_ai_onnx_ml_tree_ensemble_set_membership", ) ```
tree_ensemble_single_tree ```python node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=2, membership_values=None, nodes_missing_value_tracks_true=None, nodes_hitrates=None, aggregate_function=1, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 0, 0], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.DOUBLE, (3,), np.array([3.14, 1.2, 4.2], dtype=np.float64), ), nodes_truenodeids=[1, 0, 1], nodes_trueleafs=[0, 1, 1], nodes_falsenodeids=[2, 2, 3], nodes_falseleafs=[0, 1, 1], leaf_targetids=[0, 1, 0, 1], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.DOUBLE, (4,), np.array([5.23, 12.12, -12.23, 7.21], dtype=np.float64), ), ) x = np.array([1.2, 3.4, -0.12, 1.66, 4.14, 1.77], np.float64).reshape(3, 2) y = np.array([[5.23, 0], [5.23, 0], [0, 12.12]], dtype=np.float64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_tree_ensemble_single_tree", ) ```

## 💔No Cover Common Operators ### CastMap (call for test cases) ### CategoryMapper (call for test cases) ### DictVectorizer (call for test cases) ### FeatureVectorizer (call for test cases) ### Imputer (call for test cases) ### LinearClassifier (call for test cases) ### LinearRegressor (call for test cases) ### Normalizer (call for test cases) ### OneHotEncoder (call for test cases) ### SVMClassifier (call for test cases) ### SVMRegressor (call for test cases) ### Scaler (call for test cases) ### TreeEnsembleClassifier (call for test cases) ### TreeEnsembleRegressor (call for test cases) ### ZipMap (call for test cases)
## 💚Covered Experimental Operators
## 💔No Cover Experimental Operators
# Model Test Coverage No model tests present for selected domain # Overall Test Coverage ## To be filled. onnx-onnx-bca0315/docs/TestCoverage.md000066400000000000000000024152311511334557700177440ustar00rootroot00000000000000 # Test Coverage Report (ONNX Core Operators) ## Outlines * [Node Test Coverage](#node-test-coverage) * [Model Test Coverage](#model-test-coverage) * [Overall Test Coverage](#overall-test-coverage) # Node Test Coverage ## Summary Node tests have covered 185/197 (93.91%, 5 generators excluded) common operators. Node tests have covered 0/0 (N/A) experimental operators. * [Covered Common Operators](#covered-common-operators) * [No Cover Common Operators](#no-cover-common-operators) * [Covered Experimental Operators](#covered-experimental-operators) * [No Cover Experimental Operators](#no-cover-experimental-operators) ## 💚Covered Common Operators ### Abs There are 1 test cases, listed as following:
abs ```python node = onnx.helper.make_node( "Abs", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.abs(x) expect(node, inputs=[x], outputs=[y], name="test_abs") ```
### Acos There are 1 test cases, listed as following:
acos ```python node = onnx.helper.make_node( "Acos", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos") ```
### Acosh There are 1 test cases, listed as following:
acosh ```python node = onnx.helper.make_node( "Acosh", inputs=["x"], outputs=["y"], ) x = np.array([10, np.e, 1]).astype(np.float32) y = np.arccosh(x) # expected output [2.99322295, 1.65745449, 0.] expect(node, inputs=[x], outputs=[y], name="test_acosh_example") x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32) y = np.arccosh(x) expect(node, inputs=[x], outputs=[y], name="test_acosh") ```
### Adagrad There are 2 test cases, listed as following:
adagrad ```python # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 # Create operator. node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X", "G", "H"], outputs=["X_new", "H_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.0], dtype=np.float32) g = np.array([-1.0], dtype=np.float32) h = np.array([2.0], dtype=np.float32) # Compute expected outputs of Adagrad. x_new, h_new = apply_adagrad( r, t, x, g, h, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x, g, h], outputs=[x_new, h_new], name="test_adagrad", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
adagrad_multiple ```python # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) h1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) h2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Adagrad. x1_new, h1_new = apply_adagrad( r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor ) x2_new, h2_new = apply_adagrad( r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, h1, h2], outputs=[x1_new, x2_new, h1_new, h2_new], name="test_adagrad_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
### Adam There are 2 test cases, listed as following:
adam ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 epsilon = 1e-7 # Create operator. node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X", "G", "V", "H"], outputs=["X_new", "V_new", "H_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, epsilon=epsilon, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) h = np.array([0.1, 0.1], dtype=np.float32) # Compute expected outputs of Adam. x_new, v_new, h_new = apply_adam( r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x, g, v, h], outputs=[x_new, v_new, h_new], name="test_adam", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
adam_multiple ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 epsilon = 1e-2 node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) h1 = np.array([0.5], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) h2 = np.array([1.0, 10.0], dtype=np.float32) # Compute expected outputs of Adam. x1_new, v1_new, h1_new = apply_adam( r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon ) x2_new, v2_new, h2_new = apply_adam( r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2], outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new], name="test_adam_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
### Add There are 2 test cases, listed as following:
add ```python node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint64") ```
add_broadcast ```python node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast") ```
### AffineGrid There are 2 test cases, listed as following:
2d_no_reference_evaluator ```python theta_2d = create_theta_2d() N, C, H, W = len(theta_2d), 3, 5, 6 data_size = (H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_2d, original_grid) test_name = "test_affine_grid_2d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_2d, np.array([N, C, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) ```
3d_no_reference_evaluator ```python theta_3d = create_theta_3d() N, C, D, H, W = len(theta_3d), 3, 4, 5, 6 data_size = (D, H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_3d, original_grid) test_name = "test_affine_grid_3d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_3d, np.array([N, C, D, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) ```
### And There are 2 test cases, listed as following:
and ```python node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and4d") ```
and_broadcast ```python node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d") ```
### ArgMax There are 8 test cases, listed as following:
default_axes_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims ) # result: [[1, 1]] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random", ) ```
default_axes_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[1, 1]] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random_select_last_index", ) ```
keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random" ) ```
keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random_select_last_index", ) ```
negative_axis_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random", ) ```
negative_axis_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random_select_last_index", ) ```
no_keepdims ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [0, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random" ) ```
no_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [1, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random_select_last_index", ) ```
### ArgMin There are 8 test cases, listed as following:
default_axes_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims ) # The content of result is : [[0], [0]] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random", ) ```
default_axes_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[0, 0]] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random_select_last_index", ) ```
keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random" ) ```
keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random_select_last_index", ) ```
negative_axis_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random", ) ```
negative_axis_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random_select_last_index", ) ```
no_keepdims ```python data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1, 0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random" ) ```
no_keepdims_select_last_index ```python data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1, 0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random_select_last_index", ) ```
### Asin There are 1 test cases, listed as following:
asin ```python node = onnx.helper.make_node( "Asin", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin") ```
### Asinh There are 1 test cases, listed as following:
asinh ```python node = onnx.helper.make_node( "Asinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arcsinh(x) # expected output [-0.88137358, 0., 0.88137358] expect(node, inputs=[x], outputs=[y], name="test_asinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arcsinh(x) expect(node, inputs=[x], outputs=[y], name="test_asinh") ```
### Atan There are 1 test cases, listed as following:
atan ```python node = onnx.helper.make_node( "Atan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan") ```
### Atanh There are 1 test cases, listed as following:
atanh ```python node = onnx.helper.make_node( "Atanh", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arctanh(x) # expected output [-0.54930615, 0., 0.54930615] expect(node, inputs=[x], outputs=[y], name="test_atanh_example") x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32) y = np.arctanh(x) expect(node, inputs=[x], outputs=[y], name="test_atanh") ```
### Attention There are 62 test cases, listed as following:
attention ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_attn_mask ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_causal ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_attn_mask ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_causal ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_diff_head_sizes_with_past_and_present ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_attn_mask ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_causal ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_softcap ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_gqa_with_past_and_present ```python q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_scaled ```python scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_transpose_verification ```python """Test case to verify correct 3D to 4D transpose behavior. This test verifies that 3D inputs are correctly reshaped and transposed according to the ONNX specification: [batch_size, seq_length, hidden_size] -> [batch_size, seq_length, num_heads, head_size] -> [batch_size, num_heads, seq_length, head_size] """ q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) # Test inputs that will clearly demonstrate the transpose behavior batch_size = 1 q_seq_length = 2 kv_seq_length = 2 head_size = 4 q_hidden_size = q_num_heads * head_size # 3 * 4 = 12 kv_hidden_size = kv_num_heads * head_size # 3 * 4 = 12 # Create structured inputs to verify correct transpose behavior # Q has a pattern where each position in hidden dimension has a specific value Q = np.zeros((batch_size, q_seq_length, q_hidden_size), dtype=np.float32) # Fill Q with pattern: head0=[1,1,1,1], head1=[2,2,2,2], head2=[3,3,3,3] for head in range(q_num_heads): start_idx = head * head_size end_idx = start_idx + head_size Q[0, :, start_idx:end_idx] = float(head + 1) K = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 V = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_transpose_verification", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_bias ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_softcap ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_3d_with_past_and_present_qk_matmul_softmax ```python q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_4d_diff_heads_mask4d_padded_kv ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "", "", "nonpad_kv_seqlen"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 4).astype(np.float32) nonpad_kv_seqlen = np.array([3, 4], dtype=np.int64) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, nonpad_kv_seqlen=nonpad_kv_seqlen, ) expect( node, inputs=[Q, K, V, attn_mask, nonpad_kv_seqlen], outputs=[Y], name="test_attention_4d_diff_heads_mask4d_padded_kv", opset_imports=[onnx.helper.make_opsetid("", 24)], ) ```
attention_attn_3d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_3d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_4d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_4d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask_bool ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_attn_mask_bool_4d ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=2.0, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present_mask3D ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_diff_head_sizes_with_past_and_present_mask4D ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_fp16 ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa ```python node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_attn_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_gqa_with_past_and_present_fp16 ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float16) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_scaled ```python scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_3d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_3d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_4d_mask ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_past_and_present_qk_matmul_bias_4d_mask_causal ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y", "", "", "qk_matmul_output"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_bias ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_softcap ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], softcap=2.0, qk_matmul_output_mode=2, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
attention_with_qk_matmul_softmax ```python node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=3, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) ```
### AveragePool There are 17 test cases, listed as following:
averagepool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default") ```
averagepool_2d_ceil ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil") ```
averagepool_2d_ceil_last_window_starts_on_pad ```python """input_shape: [1, 3, 2, 2] output_shape: [1, 3, 1, 1] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[3, 3], pads=[1, 1, 1, 1], ceil_mode=True, count_include_pad=1, ) x = np.array( [ [ [[0.8580, 0.0786], [0.2692, 0.1537]], [[0.8816, 0.4353], [0.5772, 0.6623]], [[0.9067, 0.9483], [0.5970, 0.7630]], ] ] ).astype(np.float32) y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil_last_window_starts_on_pad", ) ```
averagepool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default") ```
averagepool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4] x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations") ```
averagepool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads") ```
averagepool_2d_pads_count_include_pad ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) dilations = (1, 1) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, count_include_pad=1, ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads_count_include_pad", ) ```
averagepool_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [7, 7.5, 8, 8.5, 9], [9.5, 10, 10.5, 11, 11.5], [12, 12.5, 13, 13.5, 14], [14.5, 15, 15.5, 16, 16.5], [17, 17.5, 18, 18.5, 19], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads" ) ```
averagepool_2d_precomputed_pads_count_include_pad ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [2.5200, 3.6000, 4.8000, 4.0800, 3.2400], [4.5600, 6.4000, 8.4000, 7.0400, 5.5200], [7.2000, 10.0000, 13.0000, 10.8000, 8.4000], [6.9600, 9.6000, 12.4000, 10.2400, 7.9200], [6.1200, 8.4000, 10.8000, 8.8800, 6.8400], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads_count_include_pad", ) ```
averagepool_2d_precomputed_same_upper ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype( np.float32 ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_same_upper", ) ```
averagepool_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_strides", ) ```
averagepool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower") ```
averagepool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper") ```
averagepool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( None, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = x y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=None, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides") ```
averagepool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default") ```
averagepool_3d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4, 4] x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small" ) ```
averagepool_3d_dilations_large ```python x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) count_include_pad = 0 for count_include_pad in (0, 1): for ceil_mode in (True, False): node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, count_include_pad=count_include_pad, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, extra_pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations=dilations, ceil_mode=ceil_mode, ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[3]), (extra_pads[1], extra_pads[4]), (extra_pads[2], extra_pads[5]), ), mode="constant", constant_values=0 if count_include_pad == 1 else np.nan, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=None, dilations=dilations, count_include_pad=count_include_pad, ) test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}" expect(node, inputs=[x], outputs=[y], name=test_name) ```
### BatchNormalization There are 2 test cases, listed as following:
batchnormalization ```python # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_example", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) epsilon = 1e-2 y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_epsilon", ) ```
train ```python # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) # using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'" # working around by using np.byte(1).astype(bool) training_mode = 1 y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_example_training_mode", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) training_mode = 1 momentum = 0.9 epsilon = 1e-2 y, output_mean, output_var = _batchnorm_training_mode( x, s, bias, mean, var, momentum, epsilon ) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], epsilon=epsilon, training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_epsilon_training_mode", ) ```
### Bernoulli There are 3 test cases, listed as following:
bernoulli_with_dtype ```python node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double") ```
bernoulli_with_seed ```python seed = float(0) node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], seed=seed, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, np.float32) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed") ```
bernoulli_without_dtype ```python node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], ) x = np.random.uniform(0.0, 1.0, 10).astype(float) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli") ```
### BitShift There are 8 test cases, listed as following:
left_unit16 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16") ```
left_unit32 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32") ```
left_unit64 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64") ```
left_unit8 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8") ```
right_unit16 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16") ```
right_unit32 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32") ```
right_unit64 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64") ```
right_unit8 ```python node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8") ```
### BitwiseAnd There are 2 test cases, listed as following:
bitwiseand ```python node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d") ```
bitwiseand_broadcast ```python node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_and(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d") ```
### BitwiseNot There are 1 test cases, listed as following:
bitwisenot ```python node = onnx.helper.make_node( "BitwiseNot", inputs=["x"], outputs=["bitwise_not"], ) # 2d x = create_random_int((3, 4), np.int32) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d") # 3d x = create_random_int((3, 4, 5), np.uint16) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d") # 4d x = create_random_int((3, 4, 5, 6), np.uint8) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d") ```
### BitwiseOr There are 2 test cases, listed as following:
bitwiseor ```python node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d") # 4d x = create_random_int((3, 4, 5, 6), np.int8) y = create_random_int((3, 4, 5, 6), np.int8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d") ```
bitwiseor_broadcast ```python node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d") # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d") ```
### BitwiseXor There are 2 test cases, listed as following:
bitwiseor_broadcast ```python node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_xor(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d") ```
bitwisexor ```python node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d") ```
### BlackmanWindow There are 1 test cases, listed as following:
blackmanwindow ```python # Test periodic window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) y += a2 * np.cos(4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow", ) # Test symmetric window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) y += a2 * np.cos( 4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow_symmetric", ) ```
### Cast There are 3 test cases, listed as following:
cast ```python test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in F8_TYPES or to_type in F8_TYPES: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, ) expect( node, inputs=[input], outputs=[output], name="test_cast_" + from_type + "_to_" + to_type, ) ```
e8m0 ```python np_fp32 = np.array( [ "0.0", "0.124", "0.25", "0.5", "1.1", "2.0", "4.0", "8.0", ], dtype=np.float32, ) test_cases = [ ("FLOAT", "FLOAT8E8M0"), ("FLOAT16", "FLOAT8E8M0"), ("FLOAT8E8M0", "FLOAT"), ("FLOAT8E8M0", "FLOAT16"), ] for from_type, to_type in test_cases: if from_type == "FLOAT": input_np = np_fp32 output_np = to_float8e8m0(np_fp32) elif from_type == "FLOAT16": input_np = np_fp32.astype(np.float16) output_np = to_float8e8m0(input_np) elif from_type == "FLOAT8E8M0": input_np = to_float8e8m0(np_fp32) if to_type == "FLOAT": output_np = input_np.astype(np.float32) elif to_type == "FLOAT16": output_np = input_np.astype(np.float16) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) input = make_tensor( "input", getattr(TensorProto, from_type), [2, 4], input_np, raw=True, ) output = make_tensor( "output", getattr(TensorProto, to_type), [2, 4], output_np, raw=True, ) if to_type == "FLOAT8E8M0": node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), saturate=1, round_mode="up", ) else: node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), ) expect( node, inputs=[input], outputs=[output], name="test_cast_e8m0_" + from_type + "_to_" + to_type, ) ```
saturate_false ```python test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, saturate=0, ) expect( node, inputs=[input], outputs=[output], name="test_cast_no_saturate_" + from_type + "_to_" + to_type, ) ```
### CastLike There are 2 test cases, listed as following:
castlike ```python test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] f8_types = {"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"} for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in f8_types or to_type in f8_types: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_" + from_type + "_to_" + to_type, ) ```
saturate_false ```python test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], saturate=0, ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_no_saturate_" + from_type + "_to_" + to_type, ) ```
### Ceil There are 1 test cases, listed as following:
ceil ```python node = onnx.helper.make_node( "Ceil", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2]).astype(np.float32) y = np.ceil(x) # expected output [-1., 2.] expect(node, inputs=[x], outputs=[y], name="test_ceil_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.ceil(x) expect(node, inputs=[x], outputs=[y], name="test_ceil") ```
### Celu There are 1 test cases, listed as following:
celu ```python alpha = 2.0 node = onnx.helper.make_node( "Celu", inputs=["X"], outputs=["Y"], alpha=alpha, ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data positive_input = np.maximum(0, input_data) negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1)) expected_output = positive_input + negative_input expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu") ```
### CenterCropPad There are 6 test cases, listed as following:
center_crop_pad_crop ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(20, 10, 3).astype(np.float32) shape = np.array([10, 7, 3], dtype=np.int64) y = x[5:15, 1:8, :] expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop") ```
center_crop_pad_crop_and_pad ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 10, 3], dtype=np.int64) y = np.zeros([10, 10, 3], dtype=np.float32) y[:, 1:9, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_and_pad", ) ```
center_crop_pad_crop_axes_chw ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[1, 2], ) # Cropping on second dim, padding on third, first stays the same x = np.random.randn(3, 20, 8).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([3, 10, 9], dtype=np.float32) y[:, :, :8] = x[:, 5:15, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_chw", ) ```
center_crop_pad_crop_axes_hwc ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[0, 1], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_hwc", ) ```
center_crop_pad_crop_negative_axes_hwc ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[-3, -2], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_negative_axes_hwc", ) ```
center_crop_pad_pad ```python node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(10, 7, 3).astype(np.float32) shape = np.array([20, 10, 3], dtype=np.int64) y = np.zeros([20, 10, 3], dtype=np.float32) y[5:15, 1:8, :] = x expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad") ```
### Clip There are 3 test cases, listed as following:
clip ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) x = np.array([-2, 0, 2]).astype(np.float32) min_val = np.float32(-1) max_val = np.float32(1) y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.] expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example" ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, max_val) expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip") node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) min_val = np.float32(-5) max_val = np.float32(5) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds" ) x = np.array([-6, 0, 6]).astype(np.float32) y = np.array([-5, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds" ) x = np.array([-1, 0, 6]).astype(np.float32) y = np.array([-1, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_splitbounds", ) x = np.array([-2, 0, 6]).astype(np.float32) y = np.array([1, 1, 1]).astype(np.float32) min_val = np.float32(2) max_val = np.float32(1) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_min_greater_than_max", ) ```
clip_default ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, np.inf) expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min") no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, -np.inf, max_val) expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max") no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds") ```
clip_default_int8 ```python node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, min_val, np.iinfo(np.int8).max) expect( node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min" ) no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, np.iinfo(np.int8).min, max_val) expect( node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max" ) no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.int8) y = np.array([-1, 0, 1]).astype(np.int8) expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds") ```
### Col2Im There are 5 test cases, listed as following:
col2im ```python input = np.array( [ [ [1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5) [2.0, 7.0, 12.0, 17.0, 22.0], [3.0, 8.0, 13.0, 18.0, 23.0], [4.0, 9.0, 14.0, 19.0, 24.0], [5.0, 0.0, 15.0, 20.0, 25.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) output = np.array( [ [ [ [1.0, 2.0, 3.0, 4.0, 5.0], # (1, 1, 5, 5) [6.0, 7.0, 8.0, 9.0, 0.0], [11.0, 12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0, 20.0], [21.0, 22.0, 23.0, 24.0, 25.0], ] ] ] ).astype(np.float32) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im", ) ```
col2im_5d ```python input = np.array( [ [ [1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12) [2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57], [3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58], [4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59], [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60], [61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116], [62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117], [63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118], [64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119], [65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120], ] ] ).astype(np.float32) image_shape = np.array([3, 4, 5]).astype(np.int64) block_shape = np.array([1, 1, 5]).astype(np.int64) output = np.array( [ [ [ [ [1, 2, 3, 4, 5], # (1, 2, 3, 4, 5) [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], ], [ [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35], [36, 37, 38, 39, 40], ], [ [41, 42, 43, 44, 45], [46, 47, 48, 49, 50], [51, 52, 53, 54, 55], [56, 57, 58, 59, 60], ], ], [ [ [61, 62, 63, 64, 65], [66, 67, 68, 69, 70], [71, 72, 73, 74, 75], [76, 77, 78, 79, 80], ], [ [81, 82, 83, 84, 85], [86, 87, 88, 89, 90], [91, 92, 93, 94, 95], [96, 97, 98, 99, 100], ], [ [101, 102, 103, 104, 105], [106, 107, 108, 109, 110], [111, 112, 113, 114, 115], [116, 117, 118, 119, 120], ], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_5d", ) ```
col2im_dilations ```python input = np.array( [ [ [1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5) [2.0, 6.0, 10.0, 14.0, 18], [3.0, 7.0, 11.0, 15.0, 19], [4.0, 8.0, 12.0, 16.0, 20], ] ] ).astype(np.float32) image_shape = np.array([6, 6]).astype(np.int64) block_shape = np.array([2, 2]).astype(np.int64) output = np.array( [ [ [ [1.0, 0.0, 0.0, 0.0, 0.0, 2.0], # (1, 1, 6, 6) [8.0, 0.0, 0.0, 0.0, 0.0, 10.0], [16.0, 0.0, 0.0, 0.0, 0.0, 18.0], [24.0, 0.0, 0.0, 0.0, 0.0, 26.0], [32.0, 0.0, 0.0, 0.0, 0.0, 34.0], [19.0, 0.0, 0.0, 0.0, 0.0, 20.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], dilations=[1, 5], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_dilations", ) ```
col2im_pads ```python input = np.array( [ [ [ 1.0, 6.0, 11.0, 16.0, 21.0, 26, 31, 36, 41, 46, 51, 56, 61, 66, 71, ], # (1, 5, 15) [ 2.0, 7.0, 12.0, 17.0, 22.0, 27, 32, 37, 42, 47, 52, 57, 62, 67, 72, ], [ 3.0, 8.0, 13.0, 18.0, 23.0, 28, 33, 38, 43, 48, 53, 58, 63, 68, 73, ], [ 4.0, 9.0, 14.0, 19.0, 24.0, 29, 34, 39, 44, 49, 54, 59, 64, 69, 74, ], [ 5.0, 10.0, 15.0, 20.0, 25.0, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, ], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) output = np.array( [ [ [ [8.0, 21.0, 24.0, 27.0, 24.0], # (1, 1, 5, 5) [38.0, 66.0, 69.0, 72.0, 54.0], [68.0, 111.0, 114.0, 117.0, 84.0], [98.0, 156.0, 159.0, 162.0, 114.0], [128.0, 201.0, 204.0, 207.0, 144.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], pads=[0, 1, 0, 1], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_pads", ) ```
col2im_strides ```python input = np.array( [ [ [0.0, 0.0, 0.0, 0.0], # (1, 9, 4) [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([3, 3]).astype(np.int64) output = np.array( [ [ [ [0.0, 1.0, 1.0, 1.0, 1.0], # (1, 1, 5, 5) [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 2.0, 1.0, 2.0, 1.0], [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], strides=[2, 2], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_strides", ) ```
### Compress There are 4 test cases, listed as following:
compress_0 ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=0, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 1]) output = np.compress(condition, input, axis=0) # print(output) # [[ 3. 4.] # [ 5. 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_0", ) ```
compress_1 ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_1", ) ```
compress_default_axis ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 0, 0, 1]) output = np.compress(condition, input) # print(output) # [ 2., 5.] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_default_axis", ) ```
compress_negative_axis ```python node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=-1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=-1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_negative_axis", ) ```
### Concat There are 1 test cases, listed as following:
concat ```python test_cases: dict[str, Sequence[Any]] = { "1d": ([1, 2], [3, 4]), "2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]), "3d": ( [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]], ), } for test_case, values_ in test_cases.items(): values = [np.asarray(v, dtype=np.float32) for v in values_] for i in range(len(values[0].shape)): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_" + str(i), ) for i in range(-len(values[0].shape), 0): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)), ) ```
### Constant There are 1 test cases, listed as following:
constant ```python values = np.random.randn(5, 5).astype(np.float32) node = onnx.helper.make_node( "Constant", inputs=[], outputs=["values"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=values.shape, vals=values.flatten().astype(float), ), ) expect(node, inputs=[], outputs=[values], name="test_constant") ```
### ConstantOfShape There are 3 test cases, listed as following:
float_ones ```python x = np.array([4, 3, 2]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.FLOAT, [1], [1] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.ones(x, dtype=np.float32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_float_ones") ```
int32_shape_zero ```python x = np.array( [ 0, ] ).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect( node, inputs=[x], outputs=[y], name="test_constantofshape_int_shape_zero" ) ```
int32_zeros ```python x = np.array([10, 6]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_int_zeros") ```
### Conv There are 3 test cases, listed as following:
conv ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[1, 1, 1, 1], ) y_with_padding = np.array( [ [ [ [12.0, 21.0, 27.0, 33.0, 24.0], # (1, 1, 5, 5) output tensor [33.0, 54.0, 63.0, 72.0, 51.0], [63.0, 99.0, 108.0, 117.0, 81.0], [93.0, 144.0, 153.0, 162.0, 111.0], [72.0, 111.0, 117.0, 123.0, 84.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_basic_conv_with_padding", ) # Convolution without padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[0, 0, 0, 0], ) y_without_padding = np.array( [ [ [ [54.0, 63.0, 72.0], # (1, 1, 3, 3) output tensor [99.0, 108.0, 117.0], [144.0, 153.0, 162.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_basic_conv_without_padding", ) ```
conv_with_autopad_same ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with auto_pad='SAME_LOWER' and strides=2 node = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], auto_pad="SAME_LOWER", kernel_shape=[3, 3], strides=[2, 2], ) y = np.array( [[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same") ```
conv_with_strides ```python x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 7, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with strides=2 and padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_padding = np.array( [ [ [ [12.0, 27.0, 24.0], # (1, 1, 4, 3) output tensor [63.0, 108.0, 81.0], [123.0, 198.0, 141.0], [112.0, 177.0, 124.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_conv_with_strides_padding", ) # Convolution with strides=2 and no padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_without_padding = np.array( [ [ [ [54.0, 72.0], # (1, 1, 3, 2) output tensor [144.0, 162.0], [234.0, 252.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_conv_with_strides_no_padding", ) # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor) node_with_asymmetric_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 0, 1, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_asymmetric_padding = np.array( [ [ [ [21.0, 33.0], # (1, 1, 4, 2) output tensor [99.0, 117.0], [189.0, 207.0], [171.0, 183.0], ] ] ] ).astype(np.float32) expect( node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding], name="test_conv_with_strides_and_asymmetric_padding", ) ```
### ConvInteger There are 2 test cases, listed as following:
with_padding ```python x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w_zero_points = np.array([0, 1], dtype=np.uint8) w = np.array([1, 1, 1, 1, 1, 1, 1, 1]).astype(np.uint8).reshape((2, 1, 2, 2)) y = ( np.array( [ 1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ] ) .astype(np.int32) .reshape((1, 2, 4, 4)) ) # ConvInteger with padding convinteger_node_with_padding = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point", "w_zero_points"], outputs=["y"], pads=[1, 1, 1, 1], ) expect( convinteger_node_with_padding, inputs=[x, w, x_zero_point, w_zero_points], outputs=[y], name="test_convinteger_with_padding", ) ```
without_padding ```python x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2)) y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2) # ConvInteger without padding convinteger_node = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"] ) expect( convinteger_node, inputs=[x, w, x_zero_point], outputs=[y], name="test_convinteger_without_padding", ) ```
### ConvTranspose There are 9 test cases, listed as following:
convtranspose ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], # (1, 2, 5, 5) [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose") ```
convtranspose_1d ```python x = np.array([[[0.0, 1.0, 2.0]]]).astype(np.float32) # (1, 1, 3) W = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]).astype( # (1, 2, 3) np.float32 ) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [[[0.0, 1.0, 3.0, 3.0, 2.0], [0.0, 1.0, 3.0, 3.0, 2.0]]] # (1, 2, 5) ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_1d") ```
convtranspose_3d ```python x = np.array( [ [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 3, 4, 5) [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], ], [ [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], [35.0, 36.0, 37.0, 38.0, 39.0], ], [ [40.0, 41.0, 42.0, 43.0, 44.0], [45.0, 46.0, 47.0, 48.0, 49.0], [50.0, 51.0, 52.0, 53.0, 54.0], [55.0, 56.0, 57.0, 58.0, 59.0], ], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [ [1.0, 1.0, 1.0], # (1, 2, 3, 3, 3) [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], # (1, 2, 5, 6, 7) [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_3d") ```
convtranspose_attributes ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], # (1, 2, 10, 8) [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_shape=[10, 8] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_output_shape") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_padding=[1, 1] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pad") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], name="test", strides=[3, 2], output_shape=[10, 8], kernel_shape=[3, 3], output_padding=[1, 1], ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_kernel_shape") ```
convtranspose_autopad_same ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2] ) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_autopad_same") ```
convtranspose_dilations ```python x = np.array( [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2] ) y = np.array( [ [ [ [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5] [63.0, 35.0, 67.0, 10.0, 14.0], [24.0, 22.0, 76.0, 76.0, 21.0], [9.0, 5.0, 88.0, 45.0, 63.0], [3.0, 2.0, 33.0, 18.0, 54.0], ] ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_dilations") ```
convtranspose_group_2 ```python x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ] ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2") ```
convtranspose_group_2_image_3 ```python x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [18.0, 37.0, 57.0, 39.0, 20.0], [39.0, 80.0, 123.0, 84.0, 43.0], [63.0, 129.0, 198.0, 135.0, 69.0], [45.0, 92.0, 141.0, 96.0, 49.0], [24.0, 49.0, 75.0, 51.0, 26.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], ] ).astype(np.float32) expect( node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2_image_3" ) ```
convtranspose_pads ```python x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], pads=[1, 2, 1, 2] ) y = np.array( [ [ [ [1.0, 1.0, 3.0], # (1, 2, 7, 3) [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], [ [1.0, 1.0, 3.0], [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pads") ```
### Cos There are 1 test cases, listed as following:
cos ```python node = onnx.helper.make_node( "Cos", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos") ```
### Cosh There are 1 test cases, listed as following:
cosh ```python node = onnx.helper.make_node( "Cosh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cosh(x) # expected output [1.54308069, 1., 1.54308069] expect(node, inputs=[x], outputs=[y], name="test_cosh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cosh(x) expect(node, inputs=[x], outputs=[y], name="test_cosh") ```
### CumSum There are 9 test cases, listed as following:
cumsum_1d ```python node = onnx.helper.make_node("CumSum", inputs=["x", "axis"], outputs=["y"]) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([1.0, 3.0, 6.0, 10.0, 15.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d") ```
cumsum_1d_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([0.0, 1.0, 3.0, 6.0, 10.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_exclusive") ```
cumsum_1d_int32_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1, 2, 3, 4, 5]).astype(np.int32) axis = np.int32(0) y = np.array([0, 1, 3, 6, 10]).astype(np.int32) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_int32_exclusive" ) ```
cumsum_1d_reverse ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([15.0, 14.0, 12.0, 9.0, 5.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse") ```
cumsum_1d_reverse_exclusive ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1, exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([14.0, 12.0, 9.0, 5.0, 0.0]).astype(np.float64) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse_exclusive" ) ```
cumsum_2d_axis_0 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(0) y = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 9.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_0") ```
cumsum_2d_axis_1 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_1") ```
cumsum_2d_int32 ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.int32).reshape((2, 3)) axis = np.int32(0) y = np.array([1, 2, 3, 5, 7, 9]).astype(np.int32).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_int32") ```
cumsum_2d_negative_axis ```python node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(-1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis") ```
### DFT There are 2 test cases, listed as following:
dft ```python node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(1, dtype=np.int64) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft") node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(2, dtype=np.int64) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_axis") node = onnx.helper.make_node( "DFT", inputs=["x", "", "axis"], outputs=["y"], inverse=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape(10, 10) axis = np.array(1, dtype=np.int64) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_inverse") ```
opset19 ```python node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_axis_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node( "DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape( 10, 10, ) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_inverse_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) ```
### DeformConv There are 3 test cases, listed as following:
deformconv ```python X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) # Convolution with padding offset_with_padding = np.zeros((1, 8, 4, 4), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_with_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [1, 2] offset_with_padding[0, 5, 1, 2] = -0.1 node_with_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_with_padding"], outputs=["Y_with_padding"], kernel_shape=[2, 2], pads=[1, 1, 1, 1], ) Y_with_padding = np.array( [ [ [ [0.0, 1.0, 3.0, 2.0], # (1, 1, 4, 4) output tensor [3.0, 8.0, 11.9, 7.0], [9.0, 20.0, 24.0, 13.0], [6.0, 13.0, 15.0, 8.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[X, W, offset_with_padding], outputs=[Y_with_padding], name="test_basic_deform_conv_with_padding", ) # Convolution without padding offset_without_padding = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_without_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset_without_padding[0, 5, 0, 1] = -0.1 node_without_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_without_padding"], outputs=["Y_without_padding"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y_without_padding = np.array( [ [ [ [9.5, 11.9], # (1, 1, 2, 2) output tensor [20.0, 24.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[X, W, offset_without_padding], outputs=[Y_without_padding], name="test_basic_deform_conv_without_padding", ) ```
deformconv_with_mask_bias ```python X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) B = np.ones((1,), dtype=np.float32) offset = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset[0, 5, 0, 1] = -0.1 mask = np.ones((1, 4, 2, 2), dtype=np.float32) mask[0, 2, 1, 1] = 0.2 # [1, 0] element of kernel at output position [1, 1] node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset", "B", "mask"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y = np.array( [ [ [ [10.5, 12.9], # (1, 1, 2, 2) output tensor [21.0, 19.4], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset, B, mask], outputs=[Y], name="test_deform_conv_with_mask_bias", ) ```
deformconv_with_multiple_offset_groups ```python X = np.zeros((1, 2, 3, 3), dtype=np.float32) X[0, 0] = np.reshape(np.arange(9).astype(np.float32), (3, 3)) X[0, 1] = np.reshape(np.arange(8, -1, -1).astype(np.float32), (3, 3)) X.shape = (1, 2, 3, 3) W = np.ones((1, 2, 2, 2), dtype=np.float32) offset = np.zeros((1, 16, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel in channel 0, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel in channel 1, at output position [0, 1] offset[0, 13, 0, 1] = -0.1 node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], offset_group=2, ) Y = np.array( [ [ [ [33.5, 32.1], # (1, 1, 2, 2) output tensor [32.0, 32.0], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset], outputs=[Y], name="test_deform_conv_with_multiple_offset_groups", ) ```
### DepthToSpace There are 2 test cases, listed as following:
crd_mode_example ```python node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="CRD" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 9.0, 1.0, 10.0, 2.0, 11.0], [18.0, 27.0, 19.0, 28.0, 20.0, 29.0], [3.0, 12.0, 4.0, 13.0, 5.0, 14.0], [21.0, 30.0, 22.0, 31.0, 23.0, 32.0], ], [ [36.0, 45.0, 37.0, 46.0, 38.0, 47.0], [54.0, 63.0, 55.0, 64.0, 56.0, 65.0], [39.0, 48.0, 40.0, 49.0, 41.0, 50.0], [57.0, 66.0, 58.0, 67.0, 59.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_crd_mode_example") ```
default_mode_example ```python node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="DCR" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 18.0, 1.0, 19.0, 2.0, 20.0], [36.0, 54.0, 37.0, 55.0, 38.0, 56.0], [3.0, 21.0, 4.0, 22.0, 5.0, 23.0], [39.0, 57.0, 40.0, 58.0, 41.0, 59.0], ], [ [9.0, 27.0, 10.0, 28.0, 11.0, 29.0], [45.0, 63.0, 46.0, 64.0, 47.0, 65.0], [12.0, 30.0, 13.0, 31.0, 14.0, 32.0], [48.0, 66.0, 49.0, 67.0, 50.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_example") ```
### DequantizeLinear There are 14 test cases, listed as following:
axis ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # 1-D tensor zero point and scale of size equal to axis 1 of the input tensor x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array([2, 4, 5], dtype=np.float32) x_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = ( x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32) ) * x_scale.reshape(1, 3, 1, 1) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_axis", ) ```
blocked ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[5, 12], [12, 33], [65, 42]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array( [ [ [[3.0, 2.0], [4.0, 1.0], [2.0, 2.0]], [[5.0, 2.0], [4.0, 3.0], [5.0, 2.0]], ], ], dtype=np.float32, ) x_zero_point = np.array( [ [ [[1, 0], [0, 1], [2, 20]], [[3, 2], [4, 3], [15, 2]], ], ], dtype=np.uint8, ) # x.shape = (1, 4, 3, 2) # x_scale.shape = (1, 2, 3, 2) assert x_scale.shape == x_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // x_scale.shape[i] for i in range(len(x.shape))] = (1, 2, 1, 1) assert all( x.shape[i] == x_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % x_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // x_scale.shape[block_axis] # Create element-wise scale and zero point x_scale_elementwise = np.repeat(x_scale, repeats=repeats, axis=block_axis) x_zero_point_elementwise = np.repeat( x_zero_point, repeats=repeats, axis=block_axis ) y = ( x.astype(np.float32) - x_zero_point_elementwise.astype(np.float32) ) * x_scale_elementwise expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_blocked", ) ```
dequantizelinear ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # scalar zero point and scale x = np.array([0, 3, 128, 255]).astype(np.uint8) x_scale = np.float32(2) x_zero_point = np.uint8(128) y = np.array([-256, -250, 0, 254], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear", ) ```
e4m3fn ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn", ) ```
e4m3fn_float16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float16(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float16) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn_float16", ) ```
e4m3fn_zero_point ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) zero_point = make_tensor("zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale, zero_point], outputs=[y], name="test_dequantizelinear_e4m3fn_zero_point", ) ```
e5m2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, -96]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 98304.0, -192.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e5m2", ) ```
float4e2m1 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT4E2M1, [5], [0, 1, -1, 1.5, -4]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.FLOAT4E2M1, (1,), [0]) y = np.array([0, 2, -2, 3, -8], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_float4e2m1", ) ```
int16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([-300, -30, -1025, 1270]).astype(np.int16) x_scale = np.float32(2) x_zero_point = np.int16(-1024) y = np.array([1448.0, 1988.0, -2.0, 4588.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int16", ) ```
int2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT2, [4], [0, 1, -1, -2]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT2, (1,), [1]) y = np.array([-2, 0, -4, -6], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int2", ) ```
int4 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT4, [5], [0, 1, 7, -4, -8]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT4, (1,), [1]) y = np.array([-2, 0, 12, -10, -18], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int4", ) ```
uint16 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([30000, 31000, 32768, 33000]).astype(np.uint16) x_scale = np.float32(2) x_zero_point = np.uint16(32767) y = np.array([-5534.0, -3534.0, 2.0, 466.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint16", ) ```
uint2 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT2, [4], [0, 1, 2, 3]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT2, (1,), [1]) y = np.array([-2, 0, 2, 4], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint2", ) ```
uint4 ```python node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT4, [5], [0, 1, 7, 10, 15]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT4, (1,), [1]) y = np.array([-2, 0, 12, 18, 28], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint4", ) ```
### Det There are 2 test cases, listed as following:
2d ```python node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.arange(4).reshape(2, 2).astype(np.float32) y = np.linalg.det(x) # expect -2 expect(node, inputs=[x], outputs=[y], name="test_det_2d") ```
nd ```python node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype( np.float32 ) y = np.linalg.det(x) # expect array([-2., -3., -8.]) expect(node, inputs=[x], outputs=[y], name="test_det_nd") ```
### Div There are 2 test cases, listed as following:
div ```python node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.array([3, 4]).astype(np.float32) y = np.array([1, 2]).astype(np.float32) z = x / y # expected output [3., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_div_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint64") ```
div_broadcast ```python node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast") ```
### Dropout There are 12 test cases, listed as following:
default ```python seed = np.int64(0) node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x) expect(node, inputs=[x], outputs=[y], name="test_dropout_default") ```
default_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, return_mask=True) expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask") ```
default_mask_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, r, return_mask=True) expect( node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio" ) ```
default_old ```python node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_default_old", opset_imports=[helper.make_opsetid("", 11)], ) ```
default_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x, r) expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio") ```
random_old ```python node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ratio=0.2, ) x = np.random.randn(3, 4, 5).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_random_old", opset_imports=[helper.make_opsetid("", 11)], ) ```
training ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout") ```
training_default ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default" ) ```
training_default_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_default_mask", ) ```
training_default_zero_ratio ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio" ) ```
training_default_zero_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_zero_ratio_mask", ) ```
training_ratio_mask ```python seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask" ) ```
### DynamicQuantizeLinear There are 1 test cases, listed as following:
dynamicquantizelinear ```python node = onnx.helper.make_node( "DynamicQuantizeLinear", inputs=["x"], outputs=["y", "y_scale", "y_zero_point"], ) # expected scale 0.0196078438 and zero point 153 X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear", ) # expected scale 0.0156862754 and zero point 255 X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_max_adjusted", ) X = ( np.array([1, 2.1, 1.3, 2.5, 3.34, 4.0, 1.5, 2.6, 3.9, 4.0, 3.0, 2.345]) .astype(np.float32) .reshape((3, 4)) ) # expected scale 0.0156862754 and zero point 0 x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_min_adjusted", ) ```
### Einsum There are 6 test cases, listed as following:
einsum_batch_diagonal ```python Eqn = "...ii ->...i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 5, 5) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal") ```
einsum_batch_matmul ```python Eqn = "bij, bjk -> bik" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5, 2, 3) Y = np.random.randn(5, 3, 4) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul") ```
einsum_inner_prod ```python Eqn = "i,i" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5) Y = np.random.randn(5) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod") ```
einsum_scalar ```python Eqn = "->" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.array(5.0) # scalar input Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_scalar") ```
einsum_sum ```python Eqn = "ij->i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum") ```
einsum_transpose ```python Eqn = "ij->ji" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Y = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose") ```
### Elu There are 2 test cases, listed as following:
elu ```python node = onnx.helper.make_node("Elu", inputs=["x"], outputs=["y"], alpha=2.0) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-1.2642411, 0., 1.] y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu") ```
elu_default ```python default_alpha = 1.0 node = onnx.helper.make_node( "Elu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_elu_default") ```
### Equal There are 4 test cases, listed as following:
equal ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(3, 4, 5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal") x = (np.random.randn(3, 4, 5) * 10).astype(np.int8) y = (np.random.randn(3, 4, 5) * 10).astype(np.int8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int8") x = (np.random.randn(3, 4, 5) * 10).astype(np.int16) y = (np.random.randn(3, 4, 5) * 10).astype(np.int16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint64") ```
equal_broadcast ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast") ```
equal_string ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1", "string3"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string") ```
equal_string_broadcast ```python node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string_broadcast") ```
### Erf There are 1 test cases, listed as following:
erf ```python node = onnx.helper.make_node( "Erf", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) y = np.vectorize(math.erf)(x).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_erf") ```
### Exp There are 1 test cases, listed as following:
exp ```python node = onnx.helper.make_node( "Exp", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.exp(x) # expected output [0.36787945, 1., 2.71828175] expect(node, inputs=[x], outputs=[y], name="test_exp_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.exp(x) expect(node, inputs=[x], outputs=[y], name="test_exp") ```
### Expand There are 2 test cases, listed as following:
dim_changed ```python node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] new_shape = [2, 1, 6] expanded = data * np.ones(new_shape, dtype=np.float32) # print(expanded) # [[[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]], # # [[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_changed", ) ```
dim_unchanged ```python node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] new_shape = [3, 4] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] expanded = np.tile(data, 4) # print(expanded) # [[1., 1., 1., 1.], # [2., 2., 2., 2.], # [3., 3., 3., 3.]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_unchanged", ) ```
### EyeLike There are 3 test cases, listed as following:
populate_off_main_diagonal ```python shape = (4, 5) off_diagonal_offset = 1 node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], k=off_diagonal_offset, dtype=onnx.TensorProto.FLOAT, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32) expect( node, inputs=[x], outputs=[y], name="test_eyelike_populate_off_main_diagonal", ) ```
with_dtype ```python shape = (3, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.float64) expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype") ```
without_dtype ```python shape = (4, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype") ```
### Flatten There are 3 test cases, listed as following:
flatten ```python shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(len(shape)): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i)) ```
flatten_negative_axis ```python shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(-len(shape), 0): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect( node, inputs=[a], outputs=[b], name="test_flatten_negative_axis" + str(abs(i)), ) ```
flatten_with_default_axis ```python node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], # Default value for axis: axis=1 ) shape = (5, 4, 3, 2) a = np.random.random_sample(shape).astype(np.float32) new_shape = (5, 24) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis") ```
### Floor There are 1 test cases, listed as following:
floor ```python node = onnx.helper.make_node( "Floor", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2, 2]).astype(np.float32) y = np.floor(x) # expected output [-2., 1., 2.] expect(node, inputs=[x], outputs=[y], name="test_floor_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.floor(x) expect(node, inputs=[x], outputs=[y], name="test_floor") ```
### GRU There are 4 test cases, listed as following:
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 6 number_of_gates = 3 weight_scale = 0.2 layout = 1 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_gru_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 5 weight_scale = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R) _, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_gru_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 3 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_with_initial_bias", ) ```
seq_length ```python input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype( np.float32 ) R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype( np.float32 ) # Adding custom bias W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_seq_length", ) ```
### Gather There are 4 test cases, listed as following:
gather_0 ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=0) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_0", ) ```
gather_1 ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_1", ) ```
gather_2d_indices ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(3, 3).astype(np.float32) indices = np.array([[0, 2]]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_2d_indices", ) ```
gather_negative_indices ```python node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.arange(10).astype(np.float32) indices = np.array([0, -9, -10]) y = np.take(data, indices, axis=0) # print(y) # [0. 1. 0.] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_negative_indices", ) ```
### GatherElements There are 3 test cases, listed as following:
gather_elements_0 ```python axis = 1 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2], [3, 4]], dtype=np.float32) indices = np.array([[0, 0], [1, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[1, 1], # [4, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_0", ) ```
gather_elements_1 ```python axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[4, 8, 3], # [7, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_1", ) ```
gather_elements_negative_indices ```python axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[7, 5, 3], # [4, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_negative_indices", ) ```
### GatherND There are 3 test cases, listed as following:
float32 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32) indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_float32", ) ```
int32 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[0, 1], [2, 3]], dtype=np.int32) indices = np.array([[0, 0], [1, 1]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([0, 3], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32", ) ```
int32_batchdim_1 ```python node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], batch_dims=1, ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32) indices = np.array([[1], [0]], dtype=np.int64) output = gather_nd_impl(data, indices, 1) expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32_batch_dim1", ) ```
### Gelu There are 2 test cases, listed as following:
gelu_default ```python node = onnx.helper.make_node("Gelu", inputs=["x"], outputs=["y"]) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.15865526, 0., 0.84134474] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.99595031, 3.99987331, 4.99999857] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_2") ```
gelu_tanh ```python node = onnx.helper.make_node( "Gelu", inputs=["x"], outputs=["y"], approximate="tanh" ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.158808, 0., 0.841192] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.9963627, 3.99993, 4.9999995] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_2") ```
### Gemm There are 11 test cases, listed as following:
all_attributes ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.25, beta=0.35, transA=1, transB=1, ) a = np.random.ranf([4, 3]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.random.ranf([1, 5]).astype(np.float32) y = gemm_reference_implementation( a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35 ) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes") ```
alpha ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5 ) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, alpha=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha") ```
beta ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5 ) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, beta=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta") ```
default_matrix_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.random.ranf([3, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias" ) ```
default_no_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"]) a = np.random.ranf([2, 10]).astype(np.float32) b = np.random.ranf([10, 3]).astype(np.float32) y = gemm_reference_implementation(a, b) expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias") ```
default_scalar_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 3]).astype(np.float32) b = np.random.ranf([3, 4]).astype(np.float32) c = np.array(3.14).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias" ) ```
default_single_elem_vector_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 7]).astype(np.float32) b = np.random.ranf([7, 3]).astype(np.float32) c = np.random.ranf([1]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_single_elem_vector_bias", ) ```
default_vector_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias" ) ```
default_zero_bias ```python node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias") ```
transposeA ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1 ) a = np.random.ranf([6, 3]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transA=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA") ```
transposeB ```python node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1 ) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([4, 6]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transB=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB") ```
### GlobalAveragePool There are 2 test cases, listed as following:
globalaveragepool ```python node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool") ```
globalaveragepool_precomputed ```python node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed") ```
### GlobalMaxPool There are 2 test cases, listed as following:
globalmaxpool ```python node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool") ```
globalmaxpool_precomputed ```python node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[9]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool_precomputed") ```
### Gradient There are 2 test cases, listed as following:
gradient_scalar_add ```python add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dc_da", "dc_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="c", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # dc / da = d(a+b) / da = 1 dc_da = np.array(1).astype(np.float32) # db / db = d(a+b) / db = 1 dc_db = np.array(1).astype(np.float32) graph = onnx.helper.make_graph( nodes=[add_node, gradient_node], name="GradientOfAdd", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add" ) ```
gradient_scalar_add_and_mul ```python add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dd_da", "dd_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="d", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # d = a * c = a * (a + b) d = a * c # dd / da = d(a*a+a*b) / da = 2 * a + b dd_da = (2 * a + b).astype(np.float32) # dd / db = d(a*a+a*b) / db = a dd_db = a graph = onnx.helper.make_graph( nodes=[add_node, mul_node, gradient_node], name="GradientOfTwoOperators", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[d, dd_da, dd_db], name="test_gradient_of_add_and_mul", ) ```
### Greater There are 4 test cases, listed as following:
greater ```python node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64") ```
greater ```python node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64") ```
greater_broadcast ```python node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast") ```
greater_broadcast ```python node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast") ```
### GridSample There are 4 test cases, listed as following:
gridsample ```python node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", padding_mode="zeros", align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] X = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ] ] ], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.6000, -1.0000], [-0.2000, -1.0000], [0.2000, -1.0000], [0.6000, -1.0000], [1.0000, -1.0000], ], [ [-1.0000, -0.6000], [-0.6000, -0.6000], [-0.2000, -0.6000], [0.2000, -0.6000], [0.6000, -0.6000], [1.0000, -0.6000], ], [ [-1.0000, -0.2000], [-0.6000, -0.2000], [-0.2000, -0.2000], [0.2000, -0.2000], [0.6000, -0.2000], [1.0000, -0.2000], ], [ [-1.0000, 0.2000], [-0.6000, 0.2000], [-0.2000, 0.2000], [0.2000, 0.2000], [0.6000, 0.2000], [1.0000, 0.2000], ], [ [-1.0000, 0.6000], [-0.6000, 0.6000], [-0.2000, 0.6000], [0.2000, 0.6000], [0.6000, 0.6000], [1.0000, 0.6000], ], [ [-1.0000, 1.0000], [-0.6000, 1.0000], [-0.2000, 1.0000], [0.2000, 1.0000], [0.6000, 1.0000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] Y = np.array( [ [ [ [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500], [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000], [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000], [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000], [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000], [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500], ] ] ], dtype=np.float32, ) expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample") ```
gridsample_mode_aligncorners ```python # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.5000, -0.5000], [-0.2000, -0.2000], [0.0000, 0.0000], ], [ [0.0000, 0.0000], [-0.2000, -0.2000], [0.5000, 0.5000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # setting mode = 'bilinear', default align_corners = 0 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear", ) # setting mode = 'bilinear', align_corners = 1 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_align_corners = np.array( [[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_align_corners], name="test_gridsample_aligncorners_true", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest" ) # setting mode = 'bicubic' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic" ) # ============================================================================ # Additional tests # The reference output tensors were generated using PyTorch 2.0. Grid = np.array( [ [ [[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]], [[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_0_additional_1", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_1_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_1_additional_1", ) # These two new bicubic tests produces slightly higher error ~5e-5 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [-0.173250, 0.284265, 1.923106, 2.568000], [5.170375, 2.284414, 4.744844, 1.046875], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [0.304001, 1.128750, 2.266270, 3.144844], [4.531500, 2.455360, 4.599819, 4.000000], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_1_additional_1", ) ```
gridsample_paddingmode ```python # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-10.0000, -10.0000], [-5.0000, -5.0000], [-0.2000, -0.2000], [10.0000, 10.0000], ], [ [10.0000, 10.0000], [-0.2000, -0.2000], [5.0000, 5.0000], [10.0000, 10.0000], ], ] ], dtype=np.float32, ) # setting padding_mode = 'zeros' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="zeros", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_zeros = np.array( [[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_zeros], name="test_gridsample_zeros_padding", ) # setting padding_mode = 'border' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="border", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_border = np.array( [[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_border], name="test_gridsample_border_padding", ) # setting padding_mode = 'reflection' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="reflection", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_reflection = np.array( [[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_reflection], name="test_gridsample_reflection_padding", ) ```
volumeetric_gridsample_mode_aligncorners ```python X = np.array( [ [ [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], [[9.0, 10.0], [11.0, 12.0]], ] ] ], dtype=np.float32, ) Grid = np.array( [ [ [ [[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]], [[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]], [[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]], [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]], [[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]], [[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]], [[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]], ], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [0.1250, 3.4000], [2.0000, 0.4500], [4.7000, 10.9000], [6.5000, 3.0000], ], [ [6.5000, 1.7500], [4.7000, 3.3000], [11.0000, 2.5200], [1.5000, 5.4900], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [1.0000, 6.7000], [3.7500, 2.4000], [5.4000, 9.3000], [6.5000, 6.0000], ], [ [6.5000, 7.0000], [5.4000, 6.6000], [9.2500, 8.4000], [12.0000, 6.1000], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_1", ) ```
### GroupNormalization There are 2 test cases, listed as following:
epsilon ```python c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) epsilon = 1e-2 y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], epsilon=epsilon, num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_epsilon", ) ```
groupnormalization ```python c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) y = _group_normalization(x, num_groups, scale, bias).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_example", ) ```
### HammingWindow There are 1 test cases, listed as following:
hammingwindow ```python # Test periodic window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow", ) # Test symmetric window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow_symmetric", ) ```
### HannWindow There are 1 test cases, listed as following:
hannwindow ```python # Test periodic window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow" ) # Test symmetric window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow_symmetric", ) ```
### HardSigmoid There are 2 test cases, listed as following:
hardsigmoid ```python node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6 ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.] expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid") ```
hardsigmoid_default ```python default_alpha = 0.2 default_beta = 0.5 node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * default_alpha + default_beta, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default") ```
### HardSwish There are 1 test cases, listed as following:
hardswish ```python node = onnx.helper.make_node( "HardSwish", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = hardswish(x) expect(node, inputs=[x], outputs=[y], name="test_hardswish") ```
### Hardmax There are 2 test cases, listed as following:
hardmax ```python node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype( np.float32 ) # expect result: # [[1. 0. 0. 0.] # [0. 1. 0. 0.] # [0. 0. 1. 0.] # [0. 0. 0. 1.]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_example") # For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output x = np.array([[3, 3, 3, 1]]).astype(np.float32) # expect result: # [[1, 0, 0, 0]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot") ```
hardmax_axis ```python x = np.random.randn(3, 4, 5).astype(np.float32) node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=0, ) y = hardmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=1, ) y = hardmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=2, ) y = hardmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=-1, ) y = hardmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis") ```
### Identity There are 3 test cases, listed as following:
identity ```python node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) expect(node, inputs=[data], outputs=[data], name="test_identity") ```
identity_opt ```python ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) identity_node = onnx.helper.make_node( "Identity", inputs=["opt_in"], outputs=["opt_out"] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] expect( identity_node, inputs=[x], outputs=[x], name="test_identity_opt", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[opt_in_tp], output_type_protos=[opt_in_tp], ) ```
sequence ```python node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = [ np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ), np.array( [ [ [ [2, 3], [1, 5], ] ] ], dtype=np.float32, ), ] expect(node, inputs=[data], outputs=[data], name="test_identity_sequence") ```
### If There are 3 test cases, listed as following:
if ```python # Given a bool scalar input cond. # return constant tensor x if cond is True, otherwise return constant tensor y. then_out = onnx.helper.make_tensor_value_info( "then_out", onnx.TensorProto.FLOAT, [5] ) else_out = onnx.helper.make_tensor_value_info( "else_out", onnx.TensorProto.FLOAT, [5] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([5, 4, 3, 2, 1]).astype(np.float32) then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["then_out"], value=onnx.numpy_helper.from_array(x), ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["else_out"], value=onnx.numpy_helper.from_array(y), ) then_body = onnx.helper.make_graph( [then_const_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if", opset_imports=[onnx.helper.make_opsetid("", 11)], ) ```
if_optional ```python # Given a bool scalar input cond, return an empty optional sequence of # tensor if True, return an optional sequence with value x # (the input optional sequence) otherwise. ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) then_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp) then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp) then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp) else_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp) else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp) else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] cond = np.array(0).astype(bool) res = compute_if_outputs(x, cond) opt_empty_in = onnx.helper.make_node( "Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp ) then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out]) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["else_seq"] ) else_optional_seq_node = onnx.helper.make_node( "Optional", inputs=["else_seq"], outputs=["else_opt"] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node, else_optional_seq_node], "else_body", [], [else_out], ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) expect( if_node, inputs=[cond], outputs=[res], name="test_if_opt", output_type_protos=[else_out_opt_tp], opset_imports=[onnx.helper.make_opsetid("", 16)], ) ```
if_seq ```python # Given a bool scalar input cond. # return constant sequence x if cond is True, otherwise return constant sequence y. then_out = onnx.helper.make_tensor_sequence_value_info( "then_out", onnx.TensorProto.FLOAT, shape=[5] ) else_out = onnx.helper.make_tensor_sequence_value_info( "else_out", onnx.TensorProto.FLOAT, shape=[5] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)] then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) then_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["then_out"] ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["y"], value=onnx.numpy_helper.from_array(y[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["y"], outputs=["else_out"] ) then_body = onnx.helper.make_graph( [then_const_node, then_seq_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
### ImageDecoder There are 9 test cases, listed as following:
image_decoder_decode_bmp_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "bmp", _image_decoder_data.image_decoder_decode_bmp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_bmp_rgb", ) ```
image_decoder_decode_jpeg2k_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg2000", _image_decoder_data.image_decoder_decode_jpeg2k_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg2k_rgb", ) ```
image_decoder_decode_jpeg_bgr ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="BGR", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_bgr, "BGR" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_bgr", ) ```
image_decoder_decode_jpeg_grayscale ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="Grayscale", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_grayscale, "Grayscale" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_grayscale", ) ```
image_decoder_decode_jpeg_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_rgb", ) ```
image_decoder_decode_png_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "png", _image_decoder_data.image_decoder_decode_png_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_png_rgb", ) ```
image_decoder_decode_pnm_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "ppm", _image_decoder_data.image_decoder_decode_pnm_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_pnm_rgb", ) ```
image_decoder_decode_tiff_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "tiff", _image_decoder_data.image_decoder_decode_tiff_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_tiff_rgb", ) ```
image_decoder_decode_webp_rgb ```python node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "webp", _image_decoder_data.image_decoder_decode_webp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_webp_rgb", ) ```
### InstanceNormalization There are 1 test cases, listed as following:
instancenormalization ```python def _instancenorm_test_mode(x, s, bias, epsilon=1e-5): # type: ignore dims_x = len(x.shape) axis = tuple(range(2, dims_x)) mean = np.mean(x, axis=axis, keepdims=True) var = np.var(x, axis=axis, keepdims=True) dim_ones = (1,) * (dims_x - 2) s = s.reshape(-1, *dim_ones) bias = bias.reshape(-1, *dim_ones) return s * (x - mean) / np.sqrt(var + epsilon) + bias # input size: (1, 2, 1, 3) x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32) s = np.array([1.0, 1.5]).astype(np.float32) bias = np.array([0, 1]).astype(np.float32) y = _instancenorm_test_mode(x, s, bias).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], ) # output size: (1, 2, 1, 3) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example") # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) epsilon = 1e-2 y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon") ```
### IsInf There are 4 test cases, listed as following:
infinity ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf") ```
infinity_float16 ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_float16") ```
negative_infinity_only ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_positive=0 ) x = np.array([-1.7, np.nan, np.inf, -3.6, -np.inf, np.inf], dtype=np.float32) y = np.isneginf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_negative") ```
positive_infinity_only ```python node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_negative=0 ) x = np.array([-1.7, np.nan, np.inf, 3.6, -np.inf, np.inf], dtype=np.float32) y = np.isposinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_positive") ```
### IsNaN There are 2 test cases, listed as following:
float16 ```python node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan_float16") ```
isnan ```python node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan") ```
### LRN There are 2 test cases, listed as following:
default ```python alpha = 0.0001 beta = 0.75 bias = 1.0 nsize = 3 node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn_default") ```
lrn ```python alpha = 0.0002 beta = 0.5 bias = 2.0 nsize = 3 node = onnx.helper.make_node( "LRN", inputs=["x"], outputs=["y"], alpha=alpha, beta=beta, bias=bias, size=nsize, ) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn") ```
### LSTM There are 4 test cases, listed as following:
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 7 weight_scale = 0.3 number_of_gates = 4 layout = 1 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_lstm_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R) _, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_lstm_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 4 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), 1) lstm = LSTMHelper(X=input, W=W, R=R, B=B) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_initial_bias", ) ```
peepholes ```python input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype( np.float32 ) input_size = 4 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 number_of_peepholes = 3 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"], outputs=["", "Y_h"], hidden_size=hidden_size, ) # Initializing Inputs W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32) seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32) init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype( np.float32 ) lstm = LSTMHelper( X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h ) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_peepholes", ) ```
### LayerNormalization There are 4 test cases, listed as following:
d ```python X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_2d_axis_negative_{-axis}" else: name = f"test_layer_normalization_2d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
d_epsilon ```python epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_layer_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
default_axis ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in LayerNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y, mean, inv_std_dev = _layer_normalization(X, W, B) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], ) expect( node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name="test_layer_normalization_default_axis", ) ```
layernormalization ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_4d_axis_negative_{-axis}" else: name = f"test_layer_normalization_4d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
### LeakyRelu There are 2 test cases, listed as following:
leakyrelu ```python node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], alpha=0.1 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.1, 0., 1.] y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu") ```
leakyrelu_default ```python default_alpha = 0.01 node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default") ```
### Less There are 4 test cases, listed as following:
less ```python node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64") ```
less ```python node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64") ```
less_broadcast ```python node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast") ```
less_broadcast ```python node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast") ```
### Log There are 1 test cases, listed as following:
log ```python node = onnx.helper.make_node( "Log", inputs=["x"], outputs=["y"], ) x = np.array([1, 10]).astype(np.float32) y = np.log(x) # expected output [0., 2.30258512] expect(node, inputs=[x], outputs=[y], name="test_log_example") x = np.exp(np.random.randn(3, 4, 5).astype(np.float32)) y = np.log(x) expect(node, inputs=[x], outputs=[y], name="test_log") ```
### LogSoftmax There are 2 test cases, listed as following:
logsoftmax ```python node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output # [[-2.4076061 -1.407606 -0.407606 ]] y = logsoftmax(x) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1") ```
logsoftmax_axis ```python x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[-3.4401896 -2.4401896 -1.4401896 -0.44018966] # [-3.4401896 -2.4401896 -1.4401896 -0.44018966]] y = logsoftmax(x) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=0, ) y = logsoftmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=1, ) y = logsoftmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=2, ) y = logsoftmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=-1, ) y = logsoftmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis") ```
### Loop There are 3 test cases, listed as following:
loop_11 ```python # Given a tensor x of values [x1, ..., xN], and initial tensor y # sum up its elements using a scan # returning the final state (y+x1+x2+...+xN) as well the scan_output # [y+x1, y+x1+x2, ..., y+x1+x2+...+xN] y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1]) y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1]) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [1] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([-2]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) i_add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) start_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) y_add_node = onnx.helper.make_node( "Add", inputs=["y_in", "slice_out"], outputs=["y_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) scan_identity_node = onnx.helper.make_node( "Identity", inputs=["y_out"], outputs=["scan_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, i_add_node, start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node, scan_identity_node, ], "loop_body", [iter_count, cond_in, y_in], [cond_out, y_out, scan_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "y"], outputs=["res_y", "res_scan"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) res_y = np.array([13]).astype(np.float32) cond = np.array(1).astype(bool) res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1)) expect( node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan], name="test_loop11", opset_imports=[onnx.helper.make_opsetid("", 11)], ) ```
loop_13 ```python # Given a tensor x of values [x1, ..., xN], # Return a sequence of tensors of # [[x1], [x1, x2], ..., [x1, ..., xN]] seq_in = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, None ) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, None ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, seq_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "seq_empty"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) seq_empty: list[Any] = [] seq_res = [x[: int(i)] for i in x] cond = np.array(1).astype(bool) expect( node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res], name="test_loop13_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) ), ], ) ```
loop_16_none ```python # Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0], # Return a concatenated sequence of tensors of # [x0, [x1], [x1, x2], ..., [x1, ..., xN]] ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, [] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x0 = np.array(0).astype(np.float32) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) optional_has_elem_node = onnx.helper.make_node( "OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"] ) optional_is_none = onnx.helper.make_node( "Not", inputs=["optional_has_elem"], outputs=["optional_is_none"] ) optional_get_elem = onnx.helper.make_node( "OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"] ) constant_in = onnx.helper.make_node( "Constant", inputs=[], outputs=["constant_in"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0] ), ) seq_const_in = onnx.helper.make_node( "SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"] ) then_seq_out = onnx.helper.make_tensor_sequence_value_info( "init_seq_in", onnx.TensorProto.FLOAT, [] ) then_body = onnx.helper.make_graph( [constant_in, seq_const_in], "then_body", [], [then_seq_out] ) else_seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, [] ) else_body = onnx.helper.make_graph( [optional_get_elem], "else_body", [], [else_seq_out] ) if_node = onnx.helper.make_node( "If", inputs=["optional_is_none"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, opt_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "opt_seq"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) cond = np.array(1).astype(bool) seq_res = compute_loop_outputs(x, [x0], trip_count) opt_seq_in: list[Any] = [x0] expect( node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res], name="test_loop16_seq_none", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), opt_in_tp, ], ) ```
### LpNormalization There are 6 test cases, listed as following:
default ```python node = onnx.helper.make_node("LpNormalization", inputs=["x"], outputs=["y"]) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) lp_norm_default = np.sqrt(np.sum(x**2, axis=-1, keepdims=True)) y = x / lp_norm_default expect(node, inputs=[x], outputs=[y], name="test_lpnormalization_default") ```
l1normalization_axis_0 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=1 ) x = np.array([3.0, 4.0], dtype=np.float32) l1_norm_axis_0 = np.sum(abs(x), axis=0, keepdims=True) y = x / l1_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_0") ```
l1normalization_axis_1 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=1 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l1_norm_axis_1 = np.sum(abs(x), axis=1, keepdims=True) y = x / l1_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_1") ```
l1normalization_axis_last ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=-1, p=1 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l1_norm_axis_last = np.sum(abs(x), axis=-1, keepdims=True) y = x / l1_norm_axis_last expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_last") ```
l2normalization_axis_0 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=2 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l2_norm_axis_0 = np.sqrt(np.sum(x**2, axis=0, keepdims=True)) y = x / l2_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_0") ```
l2normalization_axis_1 ```python node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=2 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l2_norm_axis_1 = np.sqrt(np.sum(x**2, axis=1, keepdims=True)) y = x / l2_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_1") ```
### LpPool There are 8 test cases, listed as following:
lppool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ p = 3 kernel_shape = [2] strides = [1] node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, p=p, ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_1d_default") ```
lppool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_default") ```
lppool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], p=p, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [14.560219778561036, 16.24807680927192], [21.633307652783937, 23.49468024894146], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_dilations") ```
lppool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], p=p, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads_required=extra_pads, pads=pads, p=p, ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads") ```
lppool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower") ```
lppool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper") ```
lppool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_strides") ```
lppool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_3d_default") ```
### MatMul There are 1 test cases, listed as following:
matmul ```python node = onnx.helper.make_node( "MatMul", inputs=["a", "b"], outputs=["c"], ) # 2d a = np.random.randn(3, 4).astype(np.float32) b = np.random.randn(4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d") # 3d a = np.random.randn(2, 3, 4).astype(np.float32) b = np.random.randn(2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d") # 4d a = np.random.randn(1, 2, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d") # broadcasting a = np.random.randn(3, 1, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 2).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_bcast") # 1d + 3d a = np.random.randn(4).astype(np.float32) b = np.random.randn(2, 4, 1).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_3d") # 3d + 1d a = np.random.randn(1, 2, 4, 3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d_1d") # 1d + 1d a = np.random.randn(3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_1d") ```
### MatMulInteger There are 1 test cases, listed as following:
matmulinteger ```python node = onnx.helper.make_node( "MatMulInteger", inputs=["A", "B", "a_zero_point", "b_zero_point"], outputs=["Y"], ) A = np.array( [ [11, 7, 3], [10, 6, 2], [9, 5, 1], [8, 4, 0], ], dtype=np.uint8, ) a_zero_point = np.array([12], dtype=np.uint8) B = np.array( [ [1, 4], [2, 5], [3, 6], ], dtype=np.uint8, ) b_zero_point = np.array([0], dtype=np.uint8) output = np.array( [ [-38, -83], [-44, -98], [-50, -113], [-56, -128], ], dtype=np.int32, ) expect( node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output], name="test_matmulinteger", ) ```
### Max There are 2 test cases, listed as following:
max ```python data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 3]).astype(np.float32) result = np.array([3, 5, 4]).astype(np.float32) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_max_example", ) node = onnx.helper.make_node( "Max", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input") result = np.maximum(data_0, data_1) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs" ) ```
max_all_numeric_types ```python for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([3, 4, 4]).astype(op_dtype) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_max_{np.dtype(op_dtype).name}", ) ```
### MaxPool There are 19 test cases, listed as following:
maxpool_1d_default ```python """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default") ```
maxpool_2d_ceil ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil") ```
maxpool_2d_ceil_output_size_reduce_by_one ```python """input_shape: [1, 1, 2, 2] output_shape: [1, 1, 1, 1] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[1, 1], strides=[2, 2], ceil_mode=True, ) x = np.array([[[[1, 2], [3, 4]]]]).astype(np.float32) y = np.array([[[[1]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil_output_size_reduce_by_one", ) ```
maxpool_2d_default ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default") ```
maxpool_2d_dilations ```python """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations") ```
maxpool_2d_pads ```python """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads") ```
maxpool_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads") ```
maxpool_2d_precomputed_same_upper ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper" ) ```
maxpool_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2] ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides" ) ```
maxpool_2d_same_lower ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower") ```
maxpool_2d_same_upper ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper") ```
maxpool_2d_strides ```python """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3] ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides") ```
maxpool_2d_uint8 ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.uint8) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.uint8) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8") ```
maxpool_3d_default ```python """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default") ```
maxpool_3d_dilations ```python """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[11, 12], [15, 16]], [[11, 12], [15, 16]]]]]).astype( np.float32 ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations") ```
maxpool_3d_dilations_use_ref_impl ```python """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ dilations = [2, 2, 2] kernel_shape = [2, 2, 2] strides = [1, 1, 1] ceil_mode = False node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=dilations, ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) x_shape = x.shape[2:] out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = x y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl" ) ```
maxpool_3d_dilations_use_ref_impl_large ```python x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) ceil_mode = True node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = np.pad( x, ( (0, 0), (0, 0), (pads[0], pads[3]), (pads[1], pads[4]), (pads[2], pads[5]), ), mode="constant", constant_values=0, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl_large", ) ```
maxpool_with_argmax_2d_precomputed_pads ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) z = np.array( [ [ [ [12, 13, 14, 14, 14], [17, 18, 19, 19, 19], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], ] ] ] ).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_pads", ) ```
maxpool_with_argmax_2d_precomputed_strides ```python """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[2, 2], strides=[2, 2], storage_order=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_strides", ) ```
### MaxUnpool There are 2 test cases, listed as following:
with_output_shape ```python node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI", "output_shape"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) output_shape = np.array((1, 1, 5, 5), dtype=np.int64) y = np.array( [ [ [ [0, 0, 0, 0, 0], [0, 5, 0, 6, 0], [0, 0, 0, 0, 0], [0, 7, 0, 8, 0], [0, 0, 0, 0, 0], ] ] ], dtype=np.float32, ) expect( node, inputs=[xT, xI, output_shape], outputs=[y], name="test_maxunpool_export_with_output_shape", ) ```
without_output_shape ```python node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) y = np.array( [[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]], dtype=np.float32, ) expect( node, inputs=[xT, xI], outputs=[y], name="test_maxunpool_export_without_output_shape", ) ```
### Mean There are 1 test cases, listed as following:
mean ```python data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([2, 3, 4]).astype(np.float32) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_mean_example", ) node = onnx.helper.make_node( "Mean", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input") result = np.divide(np.add(data_0, data_1), 2.0) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs" ) ```
### MeanVarianceNormalization There are 1 test cases, listed as following:
meanvariancenormalization ```python node = onnx.helper.make_node( "MeanVarianceNormalization", inputs=["X"], outputs=["Y"] ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1) data_mean_squared = np.power(data_mean, 2) data_squared = np.power(input_data, 2) data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1) std = np.sqrt(data_squared_mean - data_mean_squared) expected_output = (input_data - data_mean) / (std + 1e-9) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn") ```
### MelWeightMatrix There are 1 test cases, listed as following:
melweightmatrix ```python node = onnx.helper.make_node( "MelWeightMatrix", inputs=[ "num_mel_bins", "dft_length", "sample_rate", "lower_edge_hertz", "upper_edge_hertz", ], outputs=["output"], ) num_mel_bins = np.int32(8) dft_length = np.int32(16) sample_rate = np.int32(8192) lower_edge_hertz = np.float32(0) upper_edge_hertz = np.float32(8192 / 2) num_spectrogram_bins = dft_length // 2 + 1 frequency_bins = np.arange(0, num_mel_bins + 2) low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700) high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700) mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0] frequency_bins = frequency_bins * mel_step + low_frequency_mel frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1) frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate frequency_bins = frequency_bins.astype(int) output = np.zeros((num_spectrogram_bins, num_mel_bins)) output.flags.writeable = True for i in range(num_mel_bins): lower_frequency_value = frequency_bins[i] # left center_frequency_point = frequency_bins[i + 1] # center higher_frequency_point = frequency_bins[i + 2] # right low_to_center = center_frequency_point - lower_frequency_value if low_to_center == 0: output[center_frequency_point, i] = 1 else: for j in range(lower_frequency_value, center_frequency_point + 1): output[j, i] = float(j - lower_frequency_value) / float( low_to_center ) center_to_high = higher_frequency_point - center_frequency_point if center_to_high > 0: for j in range(center_frequency_point, higher_frequency_point): output[j, i] = float(higher_frequency_point - j) / float( center_to_high ) # Expected output # 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, output = output.astype(np.float32) expect( node, inputs=[ num_mel_bins, dft_length, sample_rate, lower_edge_hertz, upper_edge_hertz, ], outputs=[output], name="test_melweightmatrix", ) ```
### Min There are 2 test cases, listed as following:
min ```python data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 0]).astype(np.float32) result = np.array([1, 2, 0]).astype(np.float32) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_min_example", ) node = onnx.helper.make_node( "Min", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input") result = np.minimum(data_0, data_1) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs" ) ```
min_all_numeric_types ```python for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([1, 2, 1]).astype(op_dtype) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_min_{np.dtype(op_dtype).name}", ) ```
### Mish There are 1 test cases, listed as following:
mish ```python node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"]) input_data = np.linspace(-10, 10, 10000, dtype=np.float32) # Calculate expected output data expected_output = input_data * np.tanh(np.log1p(np.exp(input_data))) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish") ```
### Mod There are 13 test cases, listed as following:
mod_broadcast ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32) y = np.array([7]).astype(np.int32) z = np.mod(x, y) # array([[[0, 1, 2, 3, 4], # [5, 6, 0, 1, 2]], # [[3, 4, 5, 6, 0], # [1, 2, 3, 4, 5]], # [[6, 0, 1, 2, 3], # [4, 5, 6, 0, 1]]], dtype=int32) expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast") ```
mod_int64_fmod ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.fmod(x, y) # expected output [ 0, 1, 5, 0, -1, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod") ```
mod_mixed_sign_float16 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16) z = np.fmod( x, y ) # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 , 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16") ```
mod_mixed_sign_float32 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32) z = np.fmod( x, y ) # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32") ```
mod_mixed_sign_float64 ```python node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64) z = np.fmod(x, y) # expected output [-0.1, 0.4, 5. , 0.1, -0.4, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64") ```
mod_mixed_sign_int16 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16") ```
mod_mixed_sign_int32 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32") ```
mod_mixed_sign_int64 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64") ```
mod_mixed_sign_int8 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8") ```
mod_uint16 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint16) y = np.array([2, 3, 8]).astype(np.uint16) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16") ```
mod_uint32 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint32) y = np.array([2, 3, 8]).astype(np.uint32) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32") ```
mod_uint64 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint64) y = np.array([2, 3, 8]).astype(np.uint64) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64") ```
mod_uint8 ```python node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint8) y = np.array([2, 3, 8]).astype(np.uint8) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8") ```
### Momentum There are 3 test cases, listed as following:
momentum ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
momentum_multiple ```python # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Momentum. x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta) x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2], outputs=[x1_new, x2_new, v1_new, v2_new], name="test_momentum_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
nesterov_momentum ```python # Define operator attributes. norm_coefficient = 0.01 alpha = 0.95 beta = 1.0 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="nesterov", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_nesterov_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) ```
### Mul There are 2 test cases, listed as following:
mul ```python node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = x * y # expected output [4., 10., 18.] expect(node, inputs=[x, y], outputs=[z], name="test_mul_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint32") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint64") ```
mul_broadcast ```python node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast") ```
### Neg There are 1 test cases, listed as following:
neg ```python node = onnx.helper.make_node( "Neg", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.negative(x) # expected output [4., -2.], expect(node, inputs=[x], outputs=[y], name="test_neg_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.negative(x) expect(node, inputs=[x], outputs=[y], name="test_neg") ```
### NegativeLogLikelihoodLoss There are 18 test cases, listed as following:
input_shape_is_NC ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N,)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NC", ) ```
input_shape_is_NCd1 ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1", ) ```
input_shape_is_NCd1_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_ii", ) ```
input_shape_is_NCd1_mean_weight_negative_ii ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) input = np.random.rand(N, C, dim1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) target[0][0] = -1 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_mean_weight_negative_ii", ) ```
input_shape_is_NCd1_weight ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight", ) ```
input_shape_is_NCd1_weight_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight_ii", ) ```
input_shape_is_NCd1d2 ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2", ) ```
input_shape_is_NCd1d2_no_weight_reduction_mean_ii ```python reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii", ) ```
input_shape_is_NCd1d2_reduction_mean ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_mean", ) ```
input_shape_is_NCd1d2_reduction_sum ```python reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_sum", ) ```
input_shape_is_NCd1d2_with_weight ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight", ) ```
input_shape_is_NCd1d2_with_weight_reduction_mean ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_mean", ) ```
input_shape_is_NCd1d2_with_weight_reduction_sum ```python reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum", ) ```
input_shape_is_NCd1d2_with_weight_reduction_sum_ii ```python reduction = "sum" ignore_index = np.int64(0) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(0) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) target[0][0][0][0] = -5 negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N)).astype(np.int64) target[0] = 10 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_sum_weight_high_ii", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight ```python reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_mean_weight", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight ```python reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_none_no_weight", ) ```
### NonMaxSuppression There are 9 test cases, listed as following:
nonmaxsuppression_center_point_box_format ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], center_point_box=1, ) boxes = np.array( [ [ [0.5, 0.5, 1.0, 1.0], [0.5, 0.6, 1.0, 1.0], [0.5, 0.4, 1.0, 1.0], [0.5, 10.5, 1.0, 1.0], [0.5, 10.6, 1.0, 1.0], [0.5, 100.5, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_center_point_box_format", ) ```
nonmaxsuppression_flipped_coordinates ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [1.0, 1.0, 0.0, 0.0], [0.0, 0.1, 1.0, 1.1], [0.0, 0.9, 1.0, -0.1], [0.0, 10.0, 1.0, 11.0], [1.0, 10.1, 0.0, 11.1], [1.0, 101.0, 0.0, 100.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_flipped_coordinates", ) ```
nonmaxsuppression_identical_boxes ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]] ).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_identical_boxes", ) ```
nonmaxsuppression_limit_output_size ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_limit_output_size", ) ```
nonmaxsuppression_single_box ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32) scores = np.array([[[0.9]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_single_box", ) ```
nonmaxsuppression_suppress_by_IOU ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU", ) ```
nonmaxsuppression_suppress_by_IOU_and_scores ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.4]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU_and_scores", ) ```
nonmaxsuppression_two_batches ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_batches", ) ```
nonmaxsuppression_two_classes ```python node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_classes", ) ```
### NonZero There are 1 test cases, listed as following:
nonzero ```python node = onnx.helper.make_node( "NonZero", inputs=["condition"], outputs=["result"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) result = np.array( np.nonzero(condition), dtype=np.int64 ) # expected output [[0, 1, 1], [0, 0, 1]] expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example") ```
### Not There are 1 test cases, listed as following:
not ```python node = onnx.helper.make_node( "Not", inputs=["x"], outputs=["not"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d") ```
### OneHot There are 4 test cases, listed as following:
with_axis ```python axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_axis", ) ```
with_negative_axis ```python axisValue = -2 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_negative_axis", ) ```
with_negative_indices ```python axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([0, -7, -8], dtype=np.int64) # print(y) # [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.] # [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.] # [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]] depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_negative_indices", ) ```
without_axis ```python on_value = 5 off_value = 2 output_type = np.int32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"] ) indices = np.array([0, 7, 8], dtype=np.int64) depth = np.float32(12) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_without_axis", ) ```
### OptionalHasElement There are 4 test cases, listed as following:
empty ```python optional = None tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[] ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_proto in [tensor_type_proto, optional_type_proto]: input_name_options = { "empty": "optional_input", "empty_no_input_name": "", "empty_no_input": None, } for test_name_surfix, input_name in input_name_options.items(): if input_type_proto == tensor_type_proto and input_name: # the input tensor cannot be empty if input name is provided. continue node = onnx.helper.make_node( "OptionalHasElement", inputs=[] if input_name is None else [input_name], outputs=["output"], ) output = optional_has_element_reference_implementation(optional) test_name = ( "test_optional_has_element_" + test_name_surfix + ( "_optional_input" if input_type_proto == optional_type_proto else "_tensor_input" ) ) expect( node, inputs=[optional] if input_name else [], outputs=[output], input_type_protos=[input_type_proto] if input_name else [], name=test_name, ) ```
get_element_sequence ```python optional = [np.array([1, 2, 3, 4]).astype(np.int32)] tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[ 4, ], ) seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto) optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_sequence", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[seq_type_proto], name="test_optional_get_element_sequence", ) ```
get_element_tensor ```python optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_tensor", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[tensor_type_proto], name="test_optional_get_element_tensor", ) ```
optionalhaselement ```python optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_protos in [tensor_type_proto, optional_type_proto]: node = onnx.helper.make_node( "OptionalHasElement", inputs=["optional_input"], outputs=["output"] ) output = optional_has_element_reference_implementation(optional) test_name = "test_optional_has_element_" + ( "optional_input" if input_type_protos == optional_type_proto else "tensor_input" ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name=test_name, ) ```
### Or There are 2 test cases, listed as following:
or ```python node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or4d") ```
or_broadcast ```python node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d") ```
### PRelu There are 2 test cases, listed as following:
prelu ```python node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example") ```
prelu_broadcast ```python node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast") ```
### Pad There are 4 test cases, listed as following:
constant_pad ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) y = pad_impl(x, pads, "constant", 1.2) expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad") ```
constant_pad_axes ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([1, 3], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [1, 3], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_axes", ) ```
constant_pad_negative_axes ```python node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([-3, -1], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [-3, -1], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_negative_axes", ) ```
reflection_edge_and_wrap_pad ```python for mode in ("edge", "reflect", "wrap"): node = onnx.helper.make_node( "Pad", inputs=["x", "pads"], outputs=["y"], mode=mode ) x = np.random.randn(1, 3, 4, 5).astype(np.int32) pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] y = pad_impl(x, pads, mode) expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad") ```
### Pow There are 3 test cases, listed as following:
pow ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_example") x = np.arange(60).reshape(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow") ```
pow_broadcast ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array(2).astype(np.float32) z = pow(x, y) # expected output [1., 4., 9.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar") node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32) y = np.array([1, 2, 3]).astype(np.float32) # expected output [[1, 4, 27], [4, 25, 216]] z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array") ```
types ```python node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32") ```
### QLinearConv There are 1 test cases, listed as following:
qlinearconv ```python node = onnx.helper.make_node( "QLinearConv", inputs=[ "x", "x_scale", "x_zero_point", "w", "w_scale", "w_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) x = np.array( [ [255, 174, 162, 25, 203, 168, 58], [15, 59, 237, 95, 129, 0, 64], [56, 242, 153, 221, 168, 12, 166], [232, 178, 186, 195, 237, 162, 237], [188, 39, 124, 77, 80, 102, 43], [127, 230, 21, 83, 41, 40, 134], [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) x_scale = np.float32(0.00369204697) x_zero_point = np.uint8(132) w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1)) w_scale = np.array([0.00172794575], dtype=np.float32) w_zero_point = np.array([255], dtype=np.uint8) y_scale = np.float32(0.00162681262) y_zero_point = np.uint8(123) output = np.array( [ [0, 81, 93, 230, 52, 87, 197], [240, 196, 18, 160, 126, 255, 191], [199, 13, 102, 34, 87, 243, 89], [23, 77, 69, 60, 18, 93, 18], [67, 216, 131, 178, 175, 153, 212], [128, 25, 234, 172, 214, 215, 121], [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) expect( node, inputs=[ x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, ], outputs=[output], name="test_qlinearconv", ) ```
### QLinearMatMul There are 1 test cases, listed as following:
int ```python for quant_type_name in ["uint8", "int8"]: quant_type = getattr(np, quant_type_name) for dtype_name in ["float32", "float16"]: dtype = getattr(np, dtype_name) node = onnx.helper.make_node( "QLinearMatMul", inputs=[ "a", "a_scale", "a_zero_point", "b", "b_scale", "b_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) # 2D a = np.array([[208, 236, 0, 238], [3, 214, 255, 29]]) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]] ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array( [114 - 127] if quant_type == np.int8 else [114], dtype=quant_type ) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: output = np.array([[41, -12, -9], [1, -75, 20]]) else: output = np.array([[168, 115, 255], [1, 66, 151]]) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_2D_{quant_type_name}_{dtype_name}", ) # 3D a = np.array( [ [[208, 236, 0, 238], [3, 214, 255, 29]], [[208, 236, 0, 238], [3, 214, 255, 29]], ], ) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [ [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], ], ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array([114], dtype=quant_type) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: if dtype == np.float32: output = np.array( [ [[-86, 117, 120], [115, 39, -121]], [[-86, 117, 120], [115, 39, -121]], ] ) else: output = np.array( [ [[-86, 116, 119], [115, 39, -121]], [[-86, 116, 119], [115, 39, -121]], ] ) else: output = np.array( [ [[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]], ] ) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_3D_{quant_type_name}_{dtype_name}", ) ```
### QuantizeLinear There are 13 test cases, listed as following:
axis ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ [ [[-162, 10], [-100, 232], [-20, -50]], [[-76, 0], [0, 252], [32, -44]], [[245, -485], [-960, -270], [-375, -470]], ], ], dtype=np.float32, ) y_scale = np.array([2, 4, 5], dtype=np.float32) y_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype( np.uint8 ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_axis", ) ```
blocked_asymmetric ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [6.0, 12.0, 50.0, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) y_zero_point = np.array( [ [0, 1], [1, 0], [2, 3], ], dtype=np.uint8, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) assert y_scale.shape == y_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_zero_point_elementwise = np.repeat( y_zero_point, repeats=repeats, axis=block_axis ) y = np.rint(x / y_scale_elementwise + y_zero_point_elementwise).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_blocked_asymmetric", ) ```
blocked_symmetric ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale"], outputs=["y"], axis=1, block_size=2, output_dtype=TensorProto.INT16, ) x = np.array( [ [6.0, -8, -10, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_val = np.clip( np.rint(x / y_scale_elementwise), a_min=-32768, a_max=32767 ).astype(np.int16) y = make_tensor( "y", TensorProto.INT16, x.shape, y_val, ) expect( node, inputs=[x, y_scale], outputs=[y], name="test_quantizelinear_blocked_symmetric", ) ```
e4m3fn ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) y = make_tensor("y", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e4m3fn", ) ```
e5m2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E5M2, [1], [0.0]) y = make_tensor("y", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e5m2", ) ```
float4e2m1 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [-0.0, -2.5, -4.8, -8.6], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.FLOAT4E2M1, y_scale.shape, np.zeros_like(y_scale), ) y = make_tensor( "y", TensorProto.FLOAT4E2M1, x.shape, [0, 1, 2, 4, -6, -6, 2, 3, 0, -0.5, -1, -2], ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_float4e2m1", ) ```
int16 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -514.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65022.0, -66046.0, 65023.0, -66047.0, 65024.0, -66048.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.int16(256) y = np.array( [ 256, -1, 258, 254, 257, 255, 258, 254, 32767, -32767, 32767, -32768, 32767, -32768, 32767, -32768, ] ).astype(np.int16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int16", ) ```
int2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-4.0, -3.0, 1.0, 2.0], [-0.0, -2.5, -4.8, -8.6], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.INT2, x.shape, [0, 1, 1, 1, -1, -1, 0, 1, 0, -1, -1, -2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int2", ) ```
int4 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.INT4, x.shape, [1, 2, 3, 5, -8, -6, 3, 4, 4, 5, 5, 7] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int4", ) ```
quantizelinear ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32) y_scale = np.float32(2) y_zero_point = np.uint8(128) y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear", ) ```
uint16 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -128.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65536.0, -65534.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.uint16(32767) y = np.array( [ 32767, 32703, 32769, 32765, 32768, 32766, 32769, 32765, 65535, 0, 65535, 0, ] ).astype(np.uint16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint16", ) ```
uint2 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-2.0, -1.0, 1.0, 3.0], [4.0, 5.0, 6.0, 7.0], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT2, x.shape, [0, 1, 2, 3, 0, 0, 0, 1, 1, 1, 2, 2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint2", ) ```
uint4 ```python node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT4, x.shape, [1, 2, 3, 5, 0, 0, 3, 4, 4, 5, 5, 11] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint4", ) ```
### RMSNormalization There are 4 test cases, listed as following:
d ```python X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_2d_axis_negative_{-axis}" else: name = f"test_rms_normalization_2d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
d_epsilon ```python epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis, epsilon=epsilon) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_rms_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_rms_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
default_axis ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in RMSNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y = _rms_normalization(X, W) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], ) expect( node, inputs=[X, W], outputs=[Y], name="test_rms_normalization_default_axis", ) ```
rmsnormalization ```python X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_4d_axis_negative_{-axis}" else: name = f"test_rms_normalization_4d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) ```
### RNN There are 4 test cases, listed as following:
batchwise ```python input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.5 layout = 1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_simple_rnn_batchwise", ) ```
defaults ```python input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R) _, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_defaults", ) ```
initial_bias ```python input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 5 custom_bias = 0.1 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32) R_B = np.zeros((1, hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_with_initial_bias", ) ```
seq_length ```python input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, hidden_size, input_size).astype(np.float32) R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32) # Adding custom bias W_B = np.random.randn(1, hidden_size).astype(np.float32) R_B = np.random.randn(1, hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_rnn_seq_length", ) ```
### Range There are 2 test cases, listed as following:
range_float_type_positive_delta ```python node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.float32(1) limit = np.float32(5) delta = np.float32(2) output = np.arange( start, limit, delta, dtype=np.float32 ) # expected output [1.0, 3.0] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_float_type_positive_delta", ) ```
range_int32_type_negative_delta ```python node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.int32(10) limit = np.int32(6) delta = np.int32(-3) output = np.arange( start, limit, delta, dtype=np.int32 ) # expected output [10, 7] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_int32_type_negative_delta", ) ```
### Reciprocal There are 1 test cases, listed as following:
reciprocal ```python node = onnx.helper.make_node( "Reciprocal", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.reciprocal(x) # expected output [-0.25, 0.5], expect(node, inputs=[x], outputs=[y], name="test_reciprocal_example") x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5 y = np.reciprocal(x) expect(node, inputs=[x], outputs=[y], name="test_reciprocal") ```
### ReduceL1 There are 5 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 7.], [11., 15.], [19., 23.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_random", ) ```
### ReduceL2 There are 5 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[25.49509757]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[2.23606798, 5.], # [7.81024968, 10.63014581], # [13.45362405, 16.2788206]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_random", ) ```
### ReduceLogSum There are 4 test cases, listed as following:
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_empty_set", ) ```
keepdims ```python node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, keepdims=True)) axes = np.array([], dtype=np.int64) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_default", ) ```
negative_axes_keepdims ```python axes = np.array([-2], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True)) # print(reduced) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_negative_axes", ) ```
nokeepdims ```python shape = [3, 4, 5] axes = np.array([2, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_desc_axes", ) axes = np.array([0, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_asc_axes", ) ```
### ReduceLogSumExp There are 5 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[60.00671387]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[20., 2.31326175] # [40.00004578, 2.31326175] # [60.00671387, 2.31326175]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log( np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_random", ) ```
### ReduceMax There are 6 test cases, listed as following:
bool_inputs ```python axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[True], # [True], # [True], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_bool_inputs", ) ```
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdim_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[20., 2.] # [40., 2.] # [60., 2.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = -(one / zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
### ReduceMean There are 4 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[18.25]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[12.5, 1.5] # [35., 1.5] # [57.5, 1.5]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_random", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_random", ) ```
### ReduceMin There are 6 test cases, listed as following:
bool_inputs ```python axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[ True], # [False], # [False], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_bool_inputs", ) ```
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[1.]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[5., 1.] # [30., 1.] # [55., 1.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = one / zero # inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) ```
### ReduceProd There are 5 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[4.790016e+08]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 8.] # [35., 48.] # [99., 120.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.ones(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_random", ) ```
### ReduceSum There are 7 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[4., 6.] # [12., 14.] # [20., 22.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_random", ) ```
empty_axes_input_noop ```python shape = [3, 2, 2] keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, noop_with_empty_axes=True, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) axes = np.array([], dtype=np.int64) reduced = np.array(data) # print(reduced) # [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.array(data) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop", ) ```
empty_set ```python """Test case with the reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_random", ) ```
non_reduced_axis_zero ```python """Test case with the non-reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 0, 1] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([2], dtype=np.int64) reduced = np.array([], dtype=np.float32).reshape(reduced_shape) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set_non_reduced_axis_zero", ) ```
### ReduceSumSquare There are 5 test cases, listed as following:
default_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[650.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_random", ) ```
do_not_keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[10., 20.] # [74., 100.] # [202., 244.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_random", ) ```
empty_set ```python shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_empty_set", ) ```
keepdims ```python shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_random", ) ```
negative_axes_keepdims ```python shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.s]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_random", ) ```
### RegexFullMatch There are 3 test cases, listed as following:
basic ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"www\.[\w.-]+\.\bcom\b", ) x = np.array(["www.google.com", "www.facebook.com", "www.bbc.co.uk"]).astype( object ) result = np.array([True, True, False]) expect(node, inputs=[x], outputs=[result], name="test_regex_full_match_basic") ```
match_email_domain ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array( [ ["account@gmail.com", "account@hotmail.com"], ["not email", "account2@yahoo.com"], ] ).astype(object) result = np.array([[True, False], [False, True]]) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_email_domain", ) ```
match_empty ```python node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array([[], []]).astype(object) result = np.array([[], []]).astype(bool) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_empty", ) ```
### Relu There are 1 test cases, listed as following:
relu ```python node = onnx.helper.make_node( "Relu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) expect(node, inputs=[x], outputs=[y], name="test_relu") ```
### Reshape There are 2 test cases, listed as following:
allowzero ```python original_shape = [0, 3, 4] test_cases = { "allowzero_reordered": np.array([3, 4, 0], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], allowzero=1, # if allowzero=1, final shape = (3, 4, 0) # if allowzero=0, final shape = (3, 4, 4) ) reshaped = reshape_reference_implementation(data, shape, allowzero=1) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) ```
reshape ```python original_shape = [2, 3, 4] test_cases = { "reordered_all_dims": np.array([4, 2, 3], dtype=np.int64), "reordered_last_dims": np.array([2, 4, 3], dtype=np.int64), "reduced_dims": np.array([2, 12], dtype=np.int64), "extended_dims": np.array([2, 3, 2, 2], dtype=np.int64), "one_dim": np.array([24], dtype=np.int64), "negative_dim": np.array([2, -1, 2], dtype=np.int64), "negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64), "zero_dim": np.array([2, 0, 4, 1], dtype=np.int64), "zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], ) reshaped = reshape_reference_implementation(data, shape) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) ```
### Resize There are 39 test cases, listed as following:
resize_downsample_scales_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.47119141 2.78125 4.08251953] # [ 6.71142578 8.02148438 9.32275391] # [11.91650391 13.2265625 14.52783203]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic", ) ```
resize_downsample_scales_cubic_A_n0p5_exclude_outside ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.36812675 2.6695014 4.0133367 ] # [ 6.57362535 7.875 9.2188353 ] # [11.94896657 13.25034122 14.59417652]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside", ) ```
resize_downsample_scales_cubic_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1. 2.39519159 3.79038317] # [ 6.58076634 7.97595793 9.37114951] # [12.16153268 13.55672427 14.95191585]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_align_corners", ) ```
resize_downsample_scales_cubic_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.5180721 4.2858863] # [ 9.589329 11.357142 ]]]] output = interpolate_nd( data, cubic_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_antialias", ) ```
resize_downsample_scales_linear ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[2.6666665 4.3333331]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear", ) ```
resize_downsample_scales_linear_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.142857]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_align_corners", ) ```
resize_downsample_scales_linear_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.875 4.5 ] # [ 9.375 11. ]]]] output = interpolate_nd( data, linear_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_antialias", ) ```
resize_downsample_scales_linear_half_pixel_symmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32) # [[[[1.6666667, 3.3333333]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_half_pixel_symmetric", ) ```
resize_downsample_scales_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_nearest", ) ```
resize_downsample_sizes_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.63078704 3.00462963 4.37847222] # [ 7.12615741 8.5 9.87384259] # [12.62152778 13.99537037 15.36921296]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic", ) ```
resize_downsample_sizes_cubic_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.7750092 3.1200073 4.4650054] # [ 7.1550016 8.5 9.844998 ] # [12.534994 13.8799925 15.224991 ]]]] output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype( np.float32 ) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic_antialias", ) ```
resize_downsample_sizes_linear_antialias ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 2.3636363 3.590909 4.818182 ] # [ 7.2727275 8.5 9.727273 ] # [12.181818 13.409091 14.636364 ]]]] output = interpolate_nd( data, linear_coeffs_antialias, output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_antialias", ) ```
resize_downsample_sizes_linear_pytorch_half_pixel ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="pytorch_half_pixel", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 1], dtype=np.int64) # [[[[ 1.6666666] # [ 7. ] # [12.333333 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, coordinate_transformation_mode="pytorch_half_pixel", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_pytorch_half_pixel", ) ```
resize_downsample_sizes_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 1, 3], dtype=np.int64) # [[[[1. 2. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest", ) ```
resize_downsample_sizes_nearest_not_larger ```python keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2 # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_larger", ) ```
resize_downsample_sizes_nearest_not_smaller ```python keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3 # [[[[1. 2. 4.] # [5. 6. 8.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_smaller", ) ```
resize_tf_crop_and_resize ```python node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize", ) ```
resize_tf_crop_and_resize_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_2_3", ) ```
resize_tf_crop_and_resize_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_3_2", ) ```
resize_tf_crop_and_resize_extrapolation_value ```python node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 10. 10. ] # [12.400001 10. 10. ] # [10. 10. 10. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_extrapolation_value", ) ```
resize_upsample_scales_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125 # 2.91015625 3.38671875 3.68359375] # [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875 # 4.09765625 4.57421875 4.87109375] # [ 3.56640625 3.86328125 4.33984375 4.96875 5.375 # 6.00390625 6.48046875 6.77734375] # [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625 # 8.51953125 8.99609375 9.29296875] # [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625 # 10.14453125 10.62109375 10.91796875] # [10.22265625 10.51953125 10.99609375 11.625 12.03125 # 12.66015625 13.13671875 13.43359375] # [12.12890625 12.42578125 12.90234375 13.53125 13.9375 # 14.56640625 15.04296875 15.33984375] # [13.31640625 13.61328125 14.08984375 14.71875 15.125 # 15.75390625 16.23046875 16.52734375]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic", ) ```
resize_upsample_scales_cubic_A_n0p5_exclude_outside ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882 # 2.93713516 3.47917561 3.73529412] # [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285 # 3.96160918 4.50364964 4.75976814] # [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466 # 6.12977099 6.67181144 6.92792995] # [ 5.91176471 6.16788321 6.70992366 7.25 7.75 # 8.29007634 8.83211679 9.08823529] # [ 7.91176471 8.16788321 8.70992366 9.25 9.75 # 10.29007634 10.83211679 11.08823529] # [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534 # 12.45038168 12.99242213 13.24854064] # [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715 # 14.61854349 15.16058394 15.41670245] # [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118 # 15.64301751 16.18505796 16.44117647]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside", ) ```
resize_upsample_scales_cubic_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394 # 3.19970845 3.65889213 4. ] # [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542 # 4.56413994 5.02332362 5.36443149] # [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012 # 6.40087464 6.86005831 7.20116618] # [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819 # 8.51749271 8.97667638 9.31778426] # [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968 # 9.8819242 10.34110787 10.68221574] # [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776 # 11.99854227 12.45772595 12.79883382] # [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245 # 13.83527697 14.29446064 14.63556851] # [13. 13.34110787 13.80029155 14.32944606 14.67055394 # 15.19970845 15.65889213 16. ]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_align_corners", ) ```
resize_upsample_scales_cubic_asymmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="asymmetric", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4. # 4.09375] # [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625 # 5.71875] # [ 5. 5.40625 6. 6.5 7. 7.59375 8. # 8.09375] # [ 7. 7.40625 8. 8.5 9. 9.59375 10. # 10.09375] # [ 9. 9.40625 10. 10.5 11. 11.59375 12. # 12.09375] # [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375 # 14.46875] # [13. 13.40625 14. 14.5 15. 15.59375 16. # 16.09375] # [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375 # 16.46875]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.75), scale_factors=scales, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_asymmetric", ) ```
resize_upsample_scales_linear ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.25 1.75 2. ] # [1.5 1.75 2.25 2.5 ] # [2.5 2.75 3.25 3.5 ] # [3. 3.25 3.75 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear", ) ```
resize_upsample_scales_linear_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.33333333 1.66666667 2. ] # [1.66666667 2. 2.33333333 2.66666667] # [2.33333333 2.66666667 3. 3.33333333] # [3. 3.33333333 3.66666667 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_align_corners", ) ```
resize_upsample_scales_linear_half_pixel_symmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32) # [[[[1. , 1.15986395, 1.5 , 1.84013605, 2. ], # [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738], # [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262], # [3. , 3.15986395, 3.5 , 3.84013605, 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_half_pixel_symmetric", ) ```
resize_upsample_scales_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest", ) ```
resize_upsample_scales_nearest_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_2_3", ) ```
resize_upsample_scales_nearest_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([3.0, 2.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_3_2", ) ```
resize_upsample_sizes_cubic ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 9, 10], dtype=np.int64) # [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922 # 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922] # [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963 # 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963] # [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693 # 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693] # [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069 # 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069] # [ 6.88975 7.07525 7.40625 7.85725 8.342 # 8.658 9.14275 9.59375 9.92475 10.11025 ] # [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931 # 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931] # [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307 # 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307] # [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037 # 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037] # [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078 # 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_cubic", ) ```
resize_upsample_sizes_nearest ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest", ) ```
resize_upsample_sizes_nearest_axes_2_3 ```python axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_2_3", ) ```
resize_upsample_sizes_nearest_axes_3_2 ```python axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([8, 7], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_3_2", ) ```
resize_upsample_sizes_nearest_ceil_half_pixel ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="half_pixel", nearest_mode="ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_ceil_half_pixel", ) ```
resize_upsample_sizes_nearest_floor_align_corners ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="align_corners", nearest_mode="floor", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [13. 13. 13. 14. 14. 15. 15. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="floor"), output_size=sizes, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_floor_align_corners", ) ```
resize_upsample_sizes_nearest_not_larger ```python keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7 # [[[[1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_larger", ) ```
resize_upsample_sizes_nearest_not_smaller ```python keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8 # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_smaller", ) ```
resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric ```python node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="round_prefer_ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"), output_size=sizes, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", ) ```
### ReverseSequence There are 2 test cases, listed as following:
reversesequence_batch ```python node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=1, batch_axis=0, ) x = np.array( [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64) y = np.array( [ [0.0, 1.0, 2.0, 3.0], [5.0, 4.0, 6.0, 7.0], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_batch", ) ```
reversesequence_time ```python node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=0, batch_axis=1, ) x = np.array( [ [0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64) y = np.array( [ [3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_time", ) ```
### RoiAlign There are 3 test cases, listed as following:
roialign_aligned_false ```python node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.4664, 0.4466, 0.3405, 0.5688, 0.6068], [0.3714, 0.4296, 0.3835, 0.5562, 0.3510], [0.2768, 0.4883, 0.5222, 0.5528, 0.4171], [0.4713, 0.4844, 0.6904, 0.4920, 0.8774], [0.6239, 0.7125, 0.6289, 0.3355, 0.3495], ] ], [ [ [0.3022, 0.4305, 0.4696, 0.3978, 0.5423], [0.3656, 0.7050, 0.5165, 0.3172, 0.7015], [0.2912, 0.5059, 0.6476, 0.6235, 0.8299], [0.5916, 0.7389, 0.7048, 0.8372, 0.8893], [0.6227, 0.6153, 0.7097, 0.6154, 0.4585], ] ], [ [ [0.2384, 0.3379, 0.3717, 0.6100, 0.7601], [0.3767, 0.3785, 0.7147, 0.9243, 0.9727], [0.5749, 0.5826, 0.5709, 0.7619, 0.8770], [0.5355, 0.2566, 0.2141, 0.2796, 0.3600], [0.4365, 0.3504, 0.2887, 0.3661, 0.2349], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false", ) ```
roialign_aligned_true ```python node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.5178, 0.3434, 0.3229, 0.4474, 0.6344], [0.4031, 0.5366, 0.4428, 0.4861, 0.4023], [0.2512, 0.4002, 0.5155, 0.6954, 0.3465], [0.3350, 0.4601, 0.5881, 0.3439, 0.6849], [0.4932, 0.7141, 0.8217, 0.4719, 0.4039], ] ], [ [ [0.3070, 0.2187, 0.3337, 0.4880, 0.4870], [0.1871, 0.4914, 0.5561, 0.4192, 0.3686], [0.1433, 0.4608, 0.5971, 0.5310, 0.4982], [0.2788, 0.4386, 0.6022, 0.7000, 0.7524], [0.5774, 0.7024, 0.7251, 0.7338, 0.8163], ] ], [ [ [0.2393, 0.4075, 0.3379, 0.2525, 0.4743], [0.3671, 0.2702, 0.4105, 0.6419, 0.8308], [0.5556, 0.4543, 0.5564, 0.7502, 0.9300], [0.6626, 0.5617, 0.4813, 0.4954, 0.6663], [0.6636, 0.3721, 0.2056, 0.1928, 0.2478], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true", ) ```
roialign_mode_max ```python X = np.array( [ [ [ [ 0.2764, 0.715, 0.1958, 0.3416, 0.4638, 0.0259, 0.2963, 0.6518, 0.4856, 0.725, ], [ 0.9637, 0.0895, 0.2919, 0.6753, 0.0234, 0.6132, 0.8085, 0.5324, 0.8992, 0.4467, ], [ 0.3265, 0.8479, 0.9698, 0.2471, 0.9336, 0.1878, 0.4766, 0.4308, 0.34, 0.2162, ], [ 0.0206, 0.172, 0.2155, 0.4394, 0.0653, 0.3406, 0.7724, 0.3921, 0.2541, 0.5799, ], [ 0.4062, 0.2194, 0.4473, 0.4687, 0.7109, 0.9327, 0.9815, 0.632, 0.1728, 0.6119, ], [ 0.3097, 0.1283, 0.4984, 0.5068, 0.4279, 0.0173, 0.4388, 0.043, 0.4671, 0.7119, ], [ 0.1011, 0.8477, 0.4726, 0.1777, 0.9923, 0.4042, 0.1869, 0.7795, 0.9946, 0.9689, ], [ 0.1366, 0.3671, 0.7011, 0.6234, 0.9867, 0.5585, 0.6985, 0.5609, 0.8788, 0.9928, ], [ 0.5697, 0.8511, 0.6711, 0.9406, 0.8751, 0.7496, 0.165, 0.1049, 0.1559, 0.2514, ], [ 0.7012, 0.4056, 0.7879, 0.3461, 0.0415, 0.2998, 0.5094, 0.3727, 0.5482, 0.0502, ], ] ] ], dtype=np.float32, ) rois = np.array( [[0.0, 0.0, 9.0, 9.0], [0.0, 5.0, 4.0, 9.0], [5.0, 5.0, 9.0, 9.0]], dtype=np.float32, ) batch_indices = np.array([0, 0, 0], dtype=np.int64) Y = np.array( [ [ [ [0.3445228, 0.37310338, 0.37865096, 0.446696, 0.37991184], [0.4133513, 0.5455125, 0.6651902, 0.55805874, 0.27110294], [0.21223956, 0.40924096, 0.8417618, 0.792561, 0.37196714], [0.46835402, 0.39741728, 0.8012819, 0.4969306, 0.5495158], [0.3595896, 0.5196813, 0.5403741, 0.23814403, 0.19992709], ] ], [ [ [0.30517197, 0.5086199, 0.3189761, 0.4054401, 0.47630402], [0.50862, 0.8477, 0.37808004, 0.24936005, 0.79384017], [0.17620805, 0.29368007, 0.44870415, 0.4987201, 0.63148826], [0.51066005, 0.8511, 0.5368801, 0.9406, 0.70008016], [0.4487681, 0.51066035, 0.5042561, 0.5643603, 0.42004836], ] ], [ [ [0.21062402, 0.3510401, 0.37416005, 0.5967599, 0.46507207], [0.32336006, 0.31180006, 0.6236001, 0.9946, 0.7751202], [0.35744014, 0.5588001, 0.35897616, 0.7030401, 0.6353923], [0.5996801, 0.27940005, 0.17948808, 0.35152006, 0.31769615], [0.3598083, 0.40752012, 0.2385281, 0.43856013, 0.26313624], ] ], ], dtype=np.float32, ) node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], mode="max", outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_mode_max", ) ```
### RotaryEmbedding There are 8 test cases, listed as following:
rotary_embedding ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding", ) ```
rotary_embedding_3d_input ```python num_heads = 4 node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], num_heads=num_heads, ) input_data = np.random.rand(2, 3, 32).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, num_heads=num_heads, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_3d_input", ) ```
rotary_embedding_interleaved ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_interleaved", ) ```
rotary_embedding_no_position_ids ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding(input_data, cos_cache_data, sin_cache_data) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids", ) ```
rotary_embedding_no_position_ids_interleaved ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_interleaved", ) ```
rotary_embedding_no_position_ids_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 2).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_rotary_dim", ) ```
rotary_embedding_with_interleaved_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_interleaved_rotary_dim", ) ```
rotary_embedding_with_rotary_dim ```python node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_rotary_dim", ) ```
### Round There are 1 test cases, listed as following:
round ```python node = onnx.helper.make_node( "Round", inputs=["x"], outputs=["y"], ) x = np.array( [ 0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8, ] ).astype(np.float32) # expected output y = np.array( [ 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, -1.0, -2.0, -2.0, -2.0, -2.0, -3.0, ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_round") ```
### STFT There are 1 test cases, listed as following:
stft ```python signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1) length = np.array(16).astype(np.int64) onesided_length = (length >> 1) + 1 step = np.array(8).astype(np.int64) no_window = "" # optional input, not supplied node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", no_window, "frame_length"], outputs=["output"], ) nstfts = ((signal.shape[1] - length) // step) + 1 # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) output = output.astype(signal.dtype) expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft") node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", "window"], outputs=["output"], ) # Test with window a0 = 0.5 a1 = 0.5 window = a0 + a1 * np.cos( 2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length ) nstfts = 1 + (signal.shape[1] - window.shape[0]) // step # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[ 0:onesided_length ] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) window = window.astype(signal.dtype) output = output.astype(signal.dtype) expect( node, inputs=[signal, step, window], outputs=[output], name="test_stft_with_window", ) ```
### Scan There are 2 test cases, listed as following:
scan_8 ```python # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node no_sequence_lens = "" # optional input, not supplied node = onnx.helper.make_node( "Scan", inputs=[no_sequence_lens, "initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for batch-size 1, sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((1, 2)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((1, 2)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan_sum", opset_imports=[onnx.helper.make_opsetid("", 8)], ) ```
scan_9 ```python # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node node = onnx.helper.make_node( "Scan", inputs=["initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((2,)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((2,)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan9_sum", opset_imports=[onnx.helper.make_opsetid("", 9)], ) ```
### Scatter There are 2 test cases, listed as following:
scatter_with_axis ```python axis = 1 node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter(data, indices, updates, axis=axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_with_axis", opset_imports=[helper.make_opsetid("", 10)], ) ```
scatter_without_axis ```python node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_without_axis", opset_imports=[helper.make_opsetid("", 10)], ) ```
### ScatterElements There are 6 test cases, listed as following:
scatter_elements_with_axis ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_axis", ) ```
scatter_elements_with_duplicate_indices ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="add", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="add") # print(y) produces # [[1.0, 5.2, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_duplicate_indices", ) ```
scatter_elements_with_negative_indices ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, -3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 2.1, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_negative_indices", ) ```
scatter_elements_with_reduction_max ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="max", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="max") # print(y) produces # [[1.0, 2.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_max", ) ```
scatter_elements_with_reduction_min ```python axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="min", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="min") # print(y) produces # [[1.0, 1.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_min", ) ```
scatter_elements_without_axis ```python node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter_elements(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_without_axis", ) ```
### ScatterND There are 5 test cases, listed as following:
scatternd ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [2]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates) expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd", ) ```
scatternd_add ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="add", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="add") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_add", ) ```
scatternd_max ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="max", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="max") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_max", ) ```
scatternd_min ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="min", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="min") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_min", ) ```
scatternd_multiply ```python node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="mul", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="mul") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_multiply", ) ```
### Selu There are 2 test cases, listed as following:
selu ```python node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-3.79272318, 0., 3.] y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu") ```
selu_default ```python default_alpha = 1.67326319217681884765625 default_gamma = 1.05070102214813232421875 node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * default_gamma + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma ) expect(node, inputs=[x], outputs=[y], name="test_selu_default") ```
### SequenceInsert There are 1 test cases, listed as following:
sequenceinsert ```python test_cases = { "at_back": [np.array([10, 11, 12]).astype(np.int64)], "at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)], } sequence = [ np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64), ] for test_name, test_inputs in test_cases.items(): tensor = test_inputs[0].astype(np.int64) if len(test_inputs) > 1: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor", "position"], outputs=["output_sequence"], ) position = test_inputs[1] inserted = sequence_insert_reference_implementation( sequence, tensor, position ) expect( node, inputs=[sequence, tensor, position], outputs=[inserted], name="test_sequence_insert_" + test_name, ) else: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor"], outputs=["output_sequence"], ) inserted = sequence_insert_reference_implementation(sequence, tensor) expect( node, inputs=[sequence, tensor], outputs=[inserted], name="test_sequence_insert_" + test_name, ) ```
### SequenceMap There are 6 test cases, listed as following:
sequence_map_add_1_sequence_1_tensor ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)] x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y0 = [x0[i] + x1 for i in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_1_sequence_1_tensor", ) ```
sequence_map_add_2_sequences ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) N = [np.random.randint(1, 10) for _ in range(3)] x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] y0 = [x0[k] + x1[k] for k in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_2_sequences", ) ```
sequence_map_extract_shapes ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Shape", ["x"], ["shape"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "x", onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ], [onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body ) shapes = [ np.array([40, 30, 3], dtype=np.int64), np.array([20, 10, 3], dtype=np.int64), np.array([10, 5, 3], dtype=np.int64), ] x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3]) ), ] expect( node, inputs=[x0], outputs=[shapes], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_extract_shapes", ) ```
sequence_map_identity_1_sequence ```python body = onnx.helper.make_graph( [onnx.helper.make_node("Identity", ["in0"], ["out0"])], "seq_map_body", [onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x"], outputs=["y"], body=body ) x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)] y = x input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x], outputs=[y], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence", ) ```
sequence_map_identity_1_sequence_1_tensor ```python body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) y0 = x0 y1 = [x1 for _ in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence_1_tensor", ) ```
sequence_map_identity_2_sequences ```python body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] y0 = x0 y1 = x1 input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_2_sequences", ) ```
### Shape There are 1 test cases, listed as following:
shape ```python x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) test_shape("_example", x) # preserve names of original test cases x = np.random.randn(3, 4, 5).astype(np.float32) test_shape("", x) # preserve names of original test cases test_shape("_start_1", x, start=1) test_shape("_end_1", x, end=1) test_shape("_start_negative_1", x, start=-1) test_shape("_end_negative_1", x, end=-1) test_shape("_start_1_end_negative_1", x, start=1, end=-1) test_shape("_start_1_end_2", x, start=1, end=2) test_shape("_clip_start", x, start=-10) test_shape("_clip_end", x, end=10) test_shape("_start_greater_than_end", x, start=2, end=1) ```
### Shrink There are 2 test cases, listed as following:
hard_shrink ```python node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard") ```
soft_shrink ```python node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, bias=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft") ```
### Sigmoid There are 1 test cases, listed as following:
sigmoid ```python node = onnx.helper.make_node( "Sigmoid", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = 1.0 / ( 1.0 + np.exp(np.negative(x)) ) # expected output [0.26894143, 0.5, 0.7310586] expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = 1.0 / (1.0 + np.exp(np.negative(x))) expect(node, inputs=[x], outputs=[y], name="test_sigmoid") ```
### Sign There are 1 test cases, listed as following:
sign ```python node = onnx.helper.make_node( "Sign", inputs=["x"], outputs=["y"], ) x = np.array(range(-5, 6)).astype(np.float32) y = np.sign(x) expect(node, inputs=[x], outputs=[y], name="test_sign") ```
### Sin There are 1 test cases, listed as following:
sin ```python node = onnx.helper.make_node( "Sin", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin") ```
### Sinh There are 1 test cases, listed as following:
sinh ```python node = onnx.helper.make_node( "Sinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sinh(x) # expected output [-1.17520118, 0., 1.17520118] expect(node, inputs=[x], outputs=[y], name="test_sinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sinh(x) expect(node, inputs=[x], outputs=[y], name="test_sinh") ```
### Size There are 1 test cases, listed as following:
size ```python node = onnx.helper.make_node( "Size", inputs=["x"], outputs=["y"], ) x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) y = np.array(6).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.array(x.size).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size") ```
### Slice There are 8 test cases, listed as following:
slice ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) y = x[0:3, 0:10] starts = np.array([0, 0], dtype=np.int64) ends = np.array([3, 10], dtype=np.int64) axes = np.array([0, 1], dtype=np.int64) steps = np.array([1, 1], dtype=np.int64) expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice" ) ```
slice_default_axes ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes" ) ```
slice_default_steps ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_default_steps", ) ```
slice_end_out_of_bounds ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_end_out_of_bounds", ) ```
slice_neg ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0], dtype=np.int64) ends = np.array([-1], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 0:-1] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg", ) ```
slice_neg_steps ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([20, 10, 4], dtype=np.int64) ends = np.array([0, 0, 1], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) steps = np.array([-1, -3, -2]).astype(np.int64) y = x[20:0:-1, 10:0:-3, 4:1:-2] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg_steps", ) ```
slice_negative_axes ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, -2, -1], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_negative_axes", ) ```
slice_start_out_of_bounds ```python node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1000], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1000:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_start_out_of_bounds", ) ```
### Softmax There are 2 test cases, listed as following:
softmax ```python node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output [[0.09003058, 0.24472848, 0.66524094]] y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_example") ```
softmax_axis ```python x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[0.032058604 0.08714432 0.23688284 0.6439143 ] # [0.032058604 0.08714432 0.23688284 0.6439143 ]] y = softmax(x) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=0, ) y = softmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=1, ) y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=2, ) y = softmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=-1, ) y = softmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis") ```
### SoftmaxCrossEntropyLoss There are 34 test cases, listed as following:
input_shape_is_NCd1_mean_weight_negative_ii ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1_mean_weight_negative_ii", ) ```
input_shape_is_NCd1_mean_weight_negative_ii_log_prob ```python reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1_mean_weight_negative_ii_log_prob", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3_none_no_weight_negative_ii", ) ```
input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob ```python reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3_sum_weight_high_ii", ) ```
input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob ```python reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight ```python reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3d4d5_mean_weight", ) ```
input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob ```python reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight ```python reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) sce = softmaxcrossentropy(x, labels, reduction=reduction) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3d4d5_none_no_weight", ) ```
input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob ```python reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob", ) ```
softmaxcrossentropy_mean ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels) # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean") ```
softmaxcrossentropy_mean_3d ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, y) # Check results expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d") ```
softmaxcrossentropy_mean_3d_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True) # Check results expect( node, inputs=[x, y], outputs=[loss, log_prob], name="test_sce_mean_3d_log_prob", ) ```
softmaxcrossentropy_mean_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii" ) ```
softmaxcrossentropy_mean_no_weights_ii_3d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_3d", ) ```
softmaxcrossentropy_mean_no_weights_ii_3d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_3d_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii_4d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_4d", ) ```
softmaxcrossentropy_mean_no_weights_ii_4d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_4d_log_prob", ) ```
softmaxcrossentropy_mean_no_weights_ii_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_log_prob", ) ```
softmaxcrossentropy_mean_weights ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight", ) ```
softmaxcrossentropy_mean_weights_ii ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii", ) ```
softmaxcrossentropy_mean_weights_ii_3d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_3d", ) ```
softmaxcrossentropy_mean_weights_ii_3d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_3d_log_prob", ) ```
softmaxcrossentropy_mean_weights_ii_4d ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_4d", ) ```
softmaxcrossentropy_mean_weights_ii_4d_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True, ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_4d_log_prob", ) ```
softmaxcrossentropy_mean_weights_ii_log_prob ```python # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_log_prob", ) ```
softmaxcrossentropy_mean_weights_log_prob ```python # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_log_prob", ) ```
softmaxcrossentropy_none ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="none") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none") ```
softmaxcrossentropy_none_log_prob ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_none_log_prob", ) ```
softmaxcrossentropy_none_weights ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none") # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_none_weights", ) ```
softmaxcrossentropy_none_weights_log_prob ```python # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_none_weights_log_prob", ) ```
softmaxcrossentropy_sum ```python # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="sum") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum") ```
softmaxcrossentropy_sum_log_prob ```python # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="sum", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_sum_log_prob", ) ```
### Softplus There are 1 test cases, listed as following:
softplus ```python node = onnx.helper.make_node( "Softplus", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.log( np.exp(x) + 1 ) # expected output [0.31326166, 0.69314718, 1.31326163] expect(node, inputs=[x], outputs=[y], name="test_softplus_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.log(np.exp(x) + 1) expect(node, inputs=[x], outputs=[y], name="test_softplus") ```
### Softsign There are 1 test cases, listed as following:
softsign ```python node = onnx.helper.make_node( "Softsign", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-0.5, 0, 0.5]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_softsign_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = x / (1 + np.abs(x)) expect(node, inputs=[x], outputs=[y], name="test_softsign") ```
### SpaceToDepth There are 2 test cases, listed as following:
example ```python node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=2, ) # (1, 1, 4, 6) input tensor x = np.array( [ [ [ [0, 6, 1, 7, 2, 8], [12, 18, 13, 19, 14, 20], [3, 9, 4, 10, 5, 11], [15, 21, 16, 22, 17, 23], ] ] ] ).astype(np.float32) # (1, 4, 2, 3) output tensor y = np.array( [ [ [[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]], [[12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23]], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example") ```
spacetodepth ```python b, c, h, w = shape = (2, 2, 6, 6) blocksize = 2 node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=blocksize, ) x = np.random.random_sample(shape).astype(np.float32) tmp = np.reshape( x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize] ) tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4]) y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize]) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth") ```
### Split There are 10 test cases, listed as following:
1d_opset13 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
1d_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset18", ) ```
1d_uneven_split_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3", "output_4"], num_outputs=4, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), np.array([7.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_1d_uneven_split_opset18", ) ```
2d_opset13 ```python node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1 ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
2d_opset18 ```python node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1, num_outputs=2, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset18", ) ```
2d_uneven_split_opset18 ```python node_input = np.array( [ [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0], ] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=1, num_outputs=3, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32), np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_2d_uneven_split_opset18", ) ```
default_values_opset13 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
default_values_opset18 ```python node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset18", ) ```
zero_size_splits_opset13 ```python # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) ```
zero_size_splits_opset18 ```python # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset18", ) ```
### SplitToSequence There are 3 test cases, listed as following:
nokeepdims ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) node = onnx.helper.make_node( "SplitToSequence", ["data"], ["seq"], axis=1, keepdims=0, ) expected_outputs = [[data[:, i] for i in range(data.shape[1])]] expect( node, inputs=[data], outputs=expected_outputs, name="test_split_to_sequence_nokeepdims", ) ```
with_split_1 ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array(2, dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=1 ) expected_outputs = [ [ np.array([[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]], dtype=np.float32), np.array([[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]], dtype=np.float32), np.array([[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]], dtype=np.float32), ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_1", ) ```
with_split_2 ```python data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array([1, 2], dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=0 ) expected_outputs = [ [ data[:1], data[1:], ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_2", ) ```
### Sqrt There are 1 test cases, listed as following:
sqrt ```python node = onnx.helper.make_node( "Sqrt", inputs=["x"], outputs=["y"], ) x = np.array([1, 4, 9]).astype(np.float32) y = np.sqrt(x) # expected output [1., 2., 3.] expect(node, inputs=[x], outputs=[y], name="test_sqrt_example") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) y = np.sqrt(x) expect(node, inputs=[x], outputs=[y], name="test_sqrt") ```
### Squeeze There are 2 test cases, listed as following:
squeeze ```python node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) axes = np.array([0], dtype=np.int64) y = np.squeeze(x, axis=0) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze") ```
squeeze_negative_axes ```python node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2], dtype=np.int64) y = np.squeeze(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes") ```
### StringConcat There are 1 test cases, listed as following:
stringconcat ```python node = onnx.helper.make_node( "StringConcat", inputs=["x", "y"], outputs=["result"], ) x = np.array(["abc", "def"]).astype("object") y = np.array([".com", ".net"]).astype("object") result = np.array(["abc.com", "def.net"]).astype("object") expect(node, inputs=[x, y], outputs=[result], name="test_string_concat") x = np.array(["cat", "dog", "snake"]).astype("object") y = np.array(["s"]).astype("object") result = np.array(["cats", "dogs", "snakes"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_broadcasting", ) x = np.array("cat").astype("object") y = np.array("s").astype("object") result = np.array("cats").astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_zero_dimensional", ) x = np.array(["abc", ""]).astype("object") y = np.array(["", "abc"]).astype("object") result = np.array(["abc", "abc"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_empty_string", ) x = np.array(["įš„", "中"]).astype("object") y = np.array(["įš„", "中"]).astype("object") result = np.array(["įš„įš„", "中中"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_utf8", ) ```
### StringNormalizer There are 6 test cases, listed as following:
monday_casesensintive_lower ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="LOWER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_lower", ) ```
monday_casesensintive_nochangecase ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_nochangecase", ) ```
monday_casesensintive_upper ```python input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_upper", ) ```
monday_empty_output ```python input = np.array(["monday", "monday"]).astype(object) output = np.array([""]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_empty_output", ) ```
monday_insensintive_upper_twodim ```python input = ( np.array( ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"] ) .astype(object) .reshape([1, 6]) ) # It does upper case cecedille, accented E # and german umlaut but fails # with german eszett output = ( np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"]) .astype(object) .reshape([1, 4]) ) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_insensintive_upper_twodim", ) ```
nostopwords_nochangecase ```python input = np.array(["monday", "tuesday"]).astype(object) output = input # No stopwords. This is a NOOP node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_nostopwords_nochangecase", ) ```
### StringSplit There are 5 test cases, listed as following:
basic ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=".", maxsplit=None, ) x = np.array(["abc.com", "def.net"]).astype(object) substrings = np.array([["abc", "com"], ["def", "net"]]).astype(object) length = np.array([2, 2], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_basic", ) ```
consecutive_delimiters ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter="-", maxsplit=None, ) x = np.array(["o-n-n--x-", "o-n----nx"]).astype(object) substrings = np.array( [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]] ).astype(object) length = np.array([6, 6], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_consecutive_delimiters", ) ```
empty_string_delimiter ```python for delimiter, test_name in ( ("", "test_string_split_empty_string_delimiter"), (None, "test_string_split_no_delimiter"), ): node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=delimiter, maxsplit=None, ) x = np.array( ["hello world !", " hello world !", " hello world ! "] ).astype(object) substrings = np.array( [ ["hello", "world", "!"], ["hello", "world", "!"], ["hello", "world", "!"], ] ).astype(object) length = np.array([3, 3, 3], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name=test_name, ) ```
empty_string_split ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=None, maxsplit=None, ) x = np.array([]).astype(object) substrings = np.array([]).astype(object).reshape(0, 0) length = np.array([], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_empty_tensor", output_type_protos=[ onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, (0, None)), None, ], ) ```
maxsplit ```python node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], maxsplit=2, ) x = np.array( [["hello world", "def.net"], ["o n n x", "the quick brown fox"]] ).astype(object) substrings = np.array( [ [["hello", "world", ""], ["def.net", "", ""]], [["o", "n", "n x"], ["the", "quick", "brown fox"]], ] ).astype(object) length = np.array([[2, 1], [3, 3]], np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_maxsplit", ) ```
### Sub There are 2 test cases, listed as following:
sub ```python node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([3, 2, 1]).astype(np.float32) z = x - y # expected output [-2., 0., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_sub_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint32") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint64) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint64") ```
sub_broadcast ```python node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_bcast") ```
### Sum There are 1 test cases, listed as following:
sum ```python data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([6, 9, 12]).astype(np.float32) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_sum_example", ) node = onnx.helper.make_node( "Sum", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_sum_one_input") result = np.add(data_0, data_1) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_sum_two_inputs" ) ```
### Swish There are 1 test cases, listed as following:
swish ```python node = onnx.helper.make_node( "Swish", inputs=["x"], outputs=["y"], alpha=1.0, # pass alpha as attribute ) x = np.array([3, 4, 5], dtype=np.float32) y = swish(x, alpha=1.0) expect( node, inputs=[x], outputs=[y], name="test_swish", opset_imports=[onnx.helper.make_opsetid("", 24)], ) ```
### Tan There are 1 test cases, listed as following:
tan ```python node = onnx.helper.make_node( "Tan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan") ```
### Tanh There are 1 test cases, listed as following:
tanh ```python node = onnx.helper.make_node( "Tanh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tanh(x) # expected output [-0.76159418, 0., 0.76159418] expect(node, inputs=[x], outputs=[y], name="test_tanh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tanh(x) expect(node, inputs=[x], outputs=[y], name="test_tanh") ```
### TensorScatter There are 3 test cases, listed as following:
tensorscatter ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="linear", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [[[5, 5, 5, 5, 5]]], [[[1, 1, 1, 1, 1]]], ], dtype=np.float32, ) write_indices = np.array([1, 2], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [1, 1, 1, 1, 1], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter", ) ```
tensorscatter_3d ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], ) past_cache = np.array( [ [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) update = np.array( [ [ [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], ], [ [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], ], ], dtype=np.float32, ) write_indices = np.array([1, 2, 0], dtype=np.int64) present_cache = np.array( [ [ [1, 2, 3, 4, 5], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_3d", ) ```
tensorscatter_circular ```python node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="circular", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [ [ [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], ] ], [ [ [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], ] ], ], dtype=np.float32, ) write_indices = np.array([1, 3], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [4, 3, 2, 1, 0]]], [[[2, 2, 2, 2, 2], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [1, 1, 1, 1, 1]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_circular", ) ```
### TfIdfVectorizer There are 7 test cases, listed as following:
tf_batch_onlybigrams_skip0 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0", ) ```
tf_batch_onlybigrams_skip5 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5", ) ```
tf_batch_uniandbigrams_skip5 ```python input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5", ) ```
tf_only_bigrams_skip0 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_only_bigrams_skip0", ) ```
tf_onlybigrams_levelempty ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 0]).astype(np.int64) ngram_indexes = np.array([0, 1, 2]).astype(np.int64) pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype( # unigrams none np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_levelempty", ) ```
tf_onlybigrams_skip5 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_skip5", ) ```
tf_uniandbigrams_skip5 ```python input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_uniandbigrams_skip5", ) ```
### ThresholdedRelu There are 2 test cases, listed as following:
default ```python default_alpha = 1.0 node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"]) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, default_alpha, np.inf) y[y == default_alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default") ```
thresholdedrelu ```python alpha = 2.0 node = onnx.helper.make_node( "ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha ) x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32) y = np.clip(x, alpha, np.inf) # expected output [0., 0., 0., 0., 2.2] y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, alpha, np.inf) y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu") ```
### Tile There are 2 test cases, listed as following:
tile ```python node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.random.rand(2, 3, 4, 5).astype(np.float32) repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64) z = np.tile(x, repeats) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile") ```
tile_precomputed ```python node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.array([[0, 1], [2, 3]], dtype=np.float32) repeats = np.array([2, 2], dtype=np.int64) z = np.array( [[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32 ) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed") ```
### TopK There are 7 test cases, listed as following:
top_k ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k" ) ```
top_k_negative_axis ```python axis = -1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_negative_axis", ) ```
top_k_same_values ```python axis = 0 largest = 0 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # (Pdb) print(values_ref) # [0 0 0] # (Pdb) print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values", ) ```
top_k_same_values_2d ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 1, 1]], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[0 0 0] # [1 1 1] # [1 1 2]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [2 3 0]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_2d", ) ```
top_k_same_values_largest ```python axis = 0 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [0 0 0] # print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_largest", ) ```
top_k_smallest ```python axis = 1 largest = 0 sorted_ = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis, largest=largest, sorted=sorted_, ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [11, 10, 9, 8], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 0. 1. 2.] # [ 4. 5. 6.] # [ 8. 9. 10.]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_smallest", ) ```
top_k_uint64 ```python axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.uint64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3 2 1] # [ 7 6 5] # [11 10 9]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_uint64", ) ```
### Transpose There are 2 test cases, listed as following:
all_permutations ```python shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) permutations = list(itertools.permutations(np.arange(len(shape)))) for i, permutation in enumerate(permutations): node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"], perm=permutation, ) transposed = np.transpose(data, permutation) expect( node, inputs=[data], outputs=[transposed], name=f"test_transpose_all_permutations_{i}", ) ```
default ```python shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"] ) transposed = np.transpose(data) expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default") ```
### Trilu There are 18 test cases, listed as following:
tril ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 0, 0, 0, 0], # [1, 2, 0, 0, 0], # [9, 4, 1, 0, 0], # [4, 3, 4, 2, 0]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril") ```
tril_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [1, 0, 0, 0, 0], # [9, 4, 0, 0, 0], # [4, 3, 4, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg") ```
tril_one_row ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) # X: # [[[6, 2, 4, 1, 6]], # # [[8, 3, 8, 7, 0]], # # [[2, 2, 9, 5, 9]]] # expect result: # [[[6, 0, 0, 0, 0]], # # [[8, 0, 0, 0, 0]], # # [[2, 0, 0, 0, 0]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg") ```
tril_out_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg") ```
tril_out_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos") ```
tril_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 0, 0], # [1, 2, 8, 6, 0], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos") ```
tril_square ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 5]], # # [[2, 0, 0], # [2, 6, 0], # [2, 6, 5]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_square") ```
tril_square_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 0]], # # [[0, 0, 0], # [2, 0, 0], # [2, 6, 0]]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg") ```
tril_zero ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero") ```
triu ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [0, 2, 8, 6, 9], # [0, 0, 0, 8, 7], # [0, 0, 0, 2, 4]] y = triu_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_triu") ```
triu_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [0, 4, 0, 8, 7], # [0, 0, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg") ```
triu_one_row ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) k = np.array(1).astype(np.int64) # X: # [[[1, 4, 9, 7, 1]], # # [[9, 2, 8, 8, 4]], # # [[3, 9, 7, 4, 2]]] # expect result: # [[[0, 4, 9, 7, 1]], # # [[0, 2, 8, 8, 4]], # # [[0, 9, 7, 4, 2]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row") ```
triu_out_neg_out ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out") ```
triu_out_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos") ```
triu_pos ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 3, 7, 9], # [0, 0, 0, 6, 9], # [0, 0, 0, 0, 7], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos") ```
triu_square ```python node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) y = triu_reference_implementation(x) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [0, 5, 4], # [0, 0, 2]], # # [[1, 4, 9], # [0, 6, 3], # [0, 0, 8]]] expect(node, inputs=[x], outputs=[y], name="test_triu_square") ```
triu_square_neg ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [7, 5, 4], # [0, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [0, 9, 8]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg") ```
triu_zero ```python node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero") ```
### Unique There are 6 test cases, listed as following:
length_1 ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, ) x = np.array([0], dtype=np.int64) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [0] # print(indices) # [0] # print(inverse_indices) # [0] # print(counts) # [1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_length_1", ) ```
not_sorted_without_axis ```python node_not_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=0, ) # numpy unique does not retain original order (it sorts the output unique values) # https://github.com/numpy/numpy/issues/8621 # we need to recover unsorted output and indices x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) # prepare index mapping from sorted to unsorted argsorted_indices = np.argsort(indices) inverse_indices_map = dict( zip(argsorted_indices, np.arange(len(argsorted_indices)), strict=True) ) indices = indices[argsorted_indices] y = np.take(x, indices, axis=0) inverse_indices = np.asarray( [inverse_indices_map[i] for i in inverse_indices], dtype=np.int64 ) counts = counts[argsorted_indices] indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # print(y) # [2.0, 1.0, 3.0, 4.0] # print(indices) # [0 1 3 4] # print(inverse_indices) # [0, 1, 1, 2, 3, 2] # print(counts) # [1, 2, 2, 1] expect( node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_not_sorted_without_axis", ) ```
sorted_with_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=0, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[1. 0. 0.] # [2. 3. 4.]] # print(indices) # [0 2] # print(inverse_indices) # [0 0 1] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis", ) ```
sorted_with_axis_3d ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=1, ) x = np.array( [ [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], ], dtype=np.float32, ) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[[0. 1.] # [1. 1.] # [2. 1.]] # [[0. 1.] # [1. 1.] # [2. 1.]]] # print(indices) # [1 0 2] # print(inverse_indices) # [1 0 2 0] # print(counts) # [2 1 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis_3d", ) ```
sorted_with_negative_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=-1, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[0. 1.] # [0. 1.] # [3. 2.]] # print(indices) # [1 0] # print(inverse_indices) # [1 0 0] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_negative_axis", ) ```
sorted_without_axis ```python node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], ) x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_without_axis", ) ```
### Unsqueeze There are 5 test cases, listed as following:
unsqueeze_negative_axes ```python node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2]).astype(np.int64) y = np.expand_dims(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes") ```
unsqueeze_one_axis ```python x = np.random.randn(3, 4, 5).astype(np.float32) for i in range(x.ndim): axes = np.array([i]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=i) expect( node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_axis_" + str(i), ) ```
unsqueeze_three_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([2, 4, 5]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes") ```
unsqueeze_two_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([1, 4]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=1) y = np.expand_dims(y, axis=4) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes") ```
unsqueeze_unsorted_axes ```python x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([5, 4, 2]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes") ```
### Upsample There are 1 test cases, listed as following:
nearest ```python node = onnx.helper.make_node( "Upsample", inputs=["X", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) output = np.array( [ [ [ [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4], [3, 3, 3, 4, 4, 4], ] ] ], dtype=np.float32, ) expect( node, inputs=[data, scales], outputs=[output], name="test_upsample_nearest", opset_imports=[helper.make_opsetid("", 9)], ) ```
### Where There are 2 test cases, listed as following:
long ```python node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.int64) y = np.array([[9, 8], [7, 6]], dtype=np.int64) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect( node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example" ) ```
where ```python node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.float32) y = np.array([[9, 8], [7, 6]], dtype=np.float32) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example") ```
### Xor There are 2 test cases, listed as following:
xor ```python node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor4d") ```
xor_broadcast ```python node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d") ```

## 💔No Cover Common Operators ### ConcatFromSequence (call for test cases) ### GlobalLpPool (call for test cases) ### GreaterOrEqual (call for test cases) ### LessOrEqual (call for test cases) ### MaxRoiPool (call for test cases) ### Multinomial (random generator operator) ### Optional (call for test cases) ### OptionalGetElement (call for test cases) ### RandomNormal (random generator operator) ### RandomNormalLike (random generator operator) ### RandomUniform (random generator operator) ### RandomUniformLike (random generator operator) ### SequenceAt (call for test cases) ### SequenceConstruct (call for test cases) ### SequenceEmpty (call for test cases) ### SequenceErase (call for test cases) ### SequenceLength (call for test cases)
## 💚Covered Experimental Operators
## 💔No Cover Experimental Operators
# Model Test Coverage ## bvlc_alexnet bvlc_alexnet has 40 nodes. Of these, 40 are covered by node tests (100.0%)
nodes
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 1 kernel_shape: 3 pads: 3 strides: 2
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 2 storage_order: 0 strides: 1
## densenet121 densenet121 has 1746 nodes. Of these, 1746 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 1 pads: 1 strides: 1
BatchNormalization: 1 out of 3 attributes covered epsilon: 1 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 1 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 1
Unsqueeze: 1 out of 0 attributes covered
## inception_v1 inception_v1 has 237 nodes. Of these, 237 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 2 pads: 2 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 1 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 1 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 2
Unsqueeze: 1 out of 0 attributes covered
## inception_v2 inception_v2 has 916 nodes. Of these, 916 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 1 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 1 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 2
Unsqueeze: 1 out of 0 attributes covered
## resnet50 resnet50 has 415 nodes. Of these, 415 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 2 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 1 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 2
Unsqueeze: 1 out of 0 attributes covered
## shufflenet shufflenet has 446 nodes. Of these, 446 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 2 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 6 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 2
Transpose: 1 out of 1 attributes covered perm: 1
Unsqueeze: 1 out of 0 attributes covered
## squeezenet_old squeezenet_old has 105 nodes. Of these, 105 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 2 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 6 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 1 pads: 3 storage_order: 0 strides: 2
Transpose: 1 out of 1 attributes covered perm: 1
Unsqueeze: 1 out of 0 attributes covered
## vgg19 vgg19 has 82 nodes. Of these, 82 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 2 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 6 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 1 beta: 1 bias: 1 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 2 pads: 3 storage_order: 0 strides: 2
Transpose: 1 out of 1 attributes covered perm: 1
Unsqueeze: 1 out of 0 attributes covered
## zfnet512 zfnet512 has 38 nodes. Of these, 38 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 count_include_pad: 0 dilations: 0 kernel_shape: 3 pads: 3 strides: 2
BatchNormalization: 1 out of 3 attributes covered epsilon: 2 momentum: 0 training_mode: 0
Concat: 1 out of 1 attributes covered axis: 1
ConstantOfShape: 1 out of 1 attributes covered value: 1
Conv: 4 out of 6 attributes covered auto_pad: 0 dilations: 0 group: 6 kernel_shape: 5 pads: 4 strides: 3
Dropout: 1 out of 1 attributes covered seed: 0
Gemm: 1 out of 4 attributes covered alpha: 0 beta: 0 transA: 0 transB: 1
LRN: 4 out of 4 attributes covered alpha: 2 beta: 1 bias: 2 size: 1
MaxPool: 3 out of 7 attributes covered auto_pad: 0 ceil_mode: 0 dilations: 0 kernel_shape: 2 pads: 3 storage_order: 0 strides: 2
Transpose: 1 out of 1 attributes covered perm: 1
Unsqueeze: 1 out of 0 attributes covered
# Overall Test Coverage ## To be filled. onnx-onnx-bca0315/docs/TypeDenotation.md000066400000000000000000000052001511334557700203040ustar00rootroot00000000000000 # Type Denotation Type Denotation is used to describe semantic information around what the inputs and outputs are. It is stored on the TypeProto message. ## Motivation The motivation of such a mechanism can be illustrated via a simple example. In the neural network SqueezeNet, it takes in an NCHW image input float[1,3,244,244] and produces a output float[1,1000,1,1]: ```text input_in_NCHW -> data_0 -> SqueezeNet() -> output_softmaxout_1 ``` In order to run this model the user needs a lot of information. In this case the user needs to know: * the input is an image * the image is in the format of NCHW * the color channels are in the order of bgr * the pixel data is 8 bit * the pixel data is normalized as values 0-255 This proposal consists of three key components to provide all of this information: * Type Denotation, * [Dimension Denotation](DimensionDenotation.md), * [Model Metadata](MetadataProps.md). ## Type Denotation Definition To begin with, we define a set of semantic types that define what models generally consume as inputs and produce as outputs. Specifically, in our first proposal we define the following set of standard denotations: 0. `TENSOR` describes that a type holds a generic tensor using the standard TypeProto message. 1. `IMAGE` describes that a type holds an image. You can use dimension denotation to learn more about the layout of the image, and also the optional model metadata_props. 2. `AUDIO` describes that a type holds an audio clip. 3. `TEXT` describes that a type holds a block of text. Model authors SHOULD add type denotation to inputs and outputs for the model as appropriate. ## An Example with input IMAGE Let's use the same SqueezeNet example from above and show everything to properly annotate the model: * First set the TypeProto.denotation =`IMAGE` for the ValueInfoProto `data_0` * Because it's an image, the model consumer now knows to go look for image metadata on the model * Then include 3 metadata strings on ModelProto.metadata_props * `Image.BitmapPixelFormat` = `Bgr8` * `Image.ColorSpaceGamma` = `SRGB` * `Image.NominalPixelRange` = `NominalRange_0_255` * For that same ValueInfoProto, make sure to also use Dimension Denotations to denote NCHW * TensorShapeProto.Dimension[0].denotation = `DATA_BATCH` * TensorShapeProto.Dimension[1].denotation = `DATA_CHANNEL` * TensorShapeProto.Dimension[2].denotation = `DATA_FEATURE` * TensorShapeProto.Dimension[3].denotation = `DATA_FEATURE` Now there is enough information in the model to know everything about how to pass a correct image into the model. onnx-onnx-bca0315/docs/VersionConverter.md000066400000000000000000000046471511334557700206710ustar00rootroot00000000000000 # ONNX Version Converter ONNX provides a library for converting ONNX models between different opset versions. The primary motivation is to improve backwards compatibility of ONNX models without having to strengthen the spec for ONNX backends. This allows backend developers to offer support for a particular opset version and for users to write or export models to a particular opset version but run in an environment with a different opset version. Implementation wise, the library leverages the in-memory representation that is much more convenient to manipulate than the raw protobuf structs, and converters to and from the protobuf format which were developed for the ONNX Optimizer. You may be interested in invoking the provided op-specific adapters, or in implementing new ones (or both). Default adapters only work in the default domain, but can be generalized to work cross-domain or utilizing new conversion methods, dependent on the nature of relevant breaking changes. ## Invoking The Version Converter The version converter may be invoked either via C++ or Python. The Python API is described, with example, [here](PythonAPIOverview.md#converting-version-of-an-onnx-model-within-default-domain-aionnx). The C++ API consists of a single function ```cpp ModelProto ConvertVersion( const ModelProto& mp_in, const OpSetID& initial_version, const OpSetID& target_version); ``` which accepts an input `ModelProto`, the initial opset version of the model, and the target opset version, and which returns a new `ModelProto` which is the result of apply all relevant adapters between initial_version and target_version. For a list of available passes, see [convert.h](/onnx/version_converter/convert.h). ## Implementing Adapters You can implement a new adapter by subclassing `Adapter`, and registering your new adapter with `VersionConverter::registerAdapter()`. Adapters operate on an in-memory graph representation defined in [ir.h](/onnx/common/ir.h). There are a number of examples in the [adapters](/onnx/version_converter/adapters) directory. Please ensure that all adapters convert from opset version i to i + 1 or i - 1, i.e. from Version 6 to Version 5 or vice versa, even if the 2 versions being converted between are Version 1 and Version 6. If your adapter applies in the default domain, please consider adding it to the core ONNX repository onnx-onnx-bca0315/docs/Versioning.md000066400000000000000000000317441511334557700174750ustar00rootroot00000000000000 # ONNX Versioning This document describes the rules for versioning ONNX. MUST, SHOULD et al are used consistent with [RFC2119](https://tools.ietf.org/html/rfc2119). ## Versioning Principles ONNX defines the versioning policy and mechanism for three classes of entities: * The [intermediate representation (IR) specification](IR.md), which is the abstract model for graphs and operators and the concrete format that represents them. These are always versioned atomically and are referred to as the *IR version*. * Operator specifications that may be referenced by a given ONNX graph. We refer to this as the *operator version*. * A defined/trained model that defines a specific graph in terms of specific operators. We refer to this as the *model version*. The versioning of all three of these entity types is distinct and largely independent. The IR specification evolves at a different (generally slower) rate than the operator specifications. Model versions are entirely independent of the other two versions. Specific policies for version management are mandated only for IR version and operator version. For model versioning, they are merely recommendations. For model versioning, ONNX users and systems MAY follow whichever local customs make sense; however, to facilitate easily managing shared collections of ONNX models, they SHOULD adhere to the policies described under model versioning. New IR and operator versions are released as part of ONNX _releases_, which have their own versioning scheme. The release versioning scheme is not described as part of the standard itself. It is discussed in the [ONNX release management document](../RELEASE-MANAGEMENT.md). ### Semantic Versioning or Simple Numbers? The ONNX versioning system allows for simple monotonically increasing numbers or [semantic versioning (SemVer)](https://semver.org/). For IR and operator sets, versioning is based on simple numbers. For models, ONNX does not require any scheme, but recommends a set of shared conventions. Which versioning scheme is in use by a model is made clear by inspecting the most significant four bytes, which MUST be non-zero when using semantic versioning and MUST be zero when using simple numbers. In other words, when using SemVer, at least one of the MAJOR or MINOR numbers must be non-zero. ### SemVer, Files and Consumers For model and release versioning, ONNX builds on the principles and syntax defined by [SemVer 2.0.0](http://semver.org/spec/v2.0.0.html). Throughout this document, we use the terms *breaking change*, *non-breaking change*, and *patch* consistent with SemVer 2.0.0. Because ONNX models are serialized files (not APIs), it's worth making clear the relationship between a serialized model and a piece of software that consumes that model. As a rough approximation, the serialized model plays the role of an API's *callee*, while the consumer of the serialized model plays the role of the API's *caller*. The ONNX versioning principles are based on the [robustness principle](https://en.wikipedia.org/wiki/Robustness_principle): "be conservative in what you do, be liberal in what you accept from others". 1. A producer of a given ONNX model (and the ONNX specification itself) MUST strictly adhere to the rules for breaking vs. non-breaking changes defined in this specification. 2. A consumer of a given ONNX model SHOULD consume an updated ONNX file, provided there are no breaking changes in the new ONNX file's IR version, referenced operator versions, or model version (meaning the MAJOR version numbers have not changed between the two ONNX files). 3. A consumer of a given ONNX model MAY consume an updated ONNX file, provided there are one or more breaking changes in the new ONNX file's IR version, referenced operator versions, or model version. ### Serializing SemVer version numbers in protobuf For efficiency, ONNX serializes the MAJOR, MINOR, and PATCH values as a bit-packed 64-bit integer; the two most significant bytes are the MAJOR component, the next two most significant bytes are the MINOR component, and the least significant four bytes are the PATCH component. For example, *1.2.345* is represented as *0x0001000200000159*. Pre-release and build metadata are not stored in the model. ## IR versioning The IR format is versioned using simple numbers, which MUST be monotonically increasing. Breaking changes to the format or semantics of the ONNX specification require an increment of the version. Non-breaking changes to the IR format do not require changing the version number. NOTE: breaking changes include those that do not alter the serialized binary format, but still break software using libraries that write or read it. For example, changing the spelling of a message property will cause code accessing the property break. The IR format adheres to the versioning guidelines defined in the [Updating a Message Type](https://developers.google.com/protocol-buffers/docs/proto3#updating) section of the proto3 specification. As a general principle, implementations SHOULD be robust in the face of missing fields. However, to ensure basic interoperation, a subset of message fields will be marked as required for a given IR version and all producers MUST set these fields correctly. Required fields MUST always be marked with the comment: // This field MUST be present for this version of the IR. For example, the `ModelProto.ir_version` property MUST be present in every model. The ONNX checker (`onnx/checker.py`) will enforce these rules. Because the protocol buffer message definitions (.proto / .proto3 files) are expected to be consumed by multiple independent developers, changes to those definitions SHOULD NOT break code that depends on generated language bindings (e.g., changing the type of an existing field). ## Operator versioning The IR can evolve independently from the set of operators. Operators represent both the signature and semantics of a given operation. Operators are abstract interfaces in that they do not imply a specific implementation; rather, they are simply the contract between a model author and the implementations that model may execute on. A given operator is identified by a three-tuple: `(domain, op_type, since_version)`, written as `domain.op_type:since_version` in prose (e.g., `com.acme.FastConv:3`). `since_version` is the version of the operator set that introduced the operator. Breaking operator changes include: * Adding/removing/renaming an attribute. This even includes the case of adding a new optional attribute, where omitting the attribute would imply a default value yielding semantics identical to the previous operator version. * Adding/removing/reordering inputs or outputs. * Adding/removing types supported by inputs and outputs, and changing types used by attributes. * Supporting new behavior even when the existing parameter signature is otherwise identical (e.g. implicitly supporting tensor broadcasting in the Mean operator). The following are not breaking: * Clarifications of specification ambiguities to match prevailing implementation practice. Changes to the semantics of an operator or function MUST be introduced in a new operator, which MUST be introduced in a new [operator set](#operator-sets). > In practice, this means that BC-breaking changes in the ONNX > repository require contributors to follow these steps: > > 1. Increment the maximum version in `DomainToVersionRange`. > 2. Copy the old operator schema to an `old.cc` file. > 3. Update the `SinceVersion` signifier to the new max version from > step (1). > 4. Register the new operator in the corresponding `operator_sets` > header file. > 5. Add a version adapter to `convert.h` so that the version > converter can upgrade the old version of the operator to the new > one. This can be a `CompatibleAdapter` in case operators following > the old schema are still valid under the new one (which is usually > true). > 6. A version adapter to downgrade the new operator to the older version > can also be added to `convert.h` but it's not mandatory. How nodes bind to operator declarations is strictly defined, and are designed to increase model compatibility across ONNX implementations, in the spirit of the conservative clause of the robustness principle. How ONNX implementations bind an operator declaration to a specific implementation is outside the scope of this specification. Implementations of ONNX MAY elect to introduce more sophisticated operator declaration/implementation binding modes, in the spirit of the liberal clause of the robustness principle. ### Operator sets ONNX uses operator sets to group together immutable operator specifications. An operator set represents a specific version of a domain, indicated by a pair (domain, version). This represents the set of all operators belonging to the specified domain with the specified version (referred to as the `opset_version`). When the inventory of a given operator set changes either by addition, removal, or a change in semantics of a contained operator, its version MUST increase. Models declare which operator sets they require as a list of `(domain, opset_version)` pairs in `ModelProto.opset_import`. The empty string ("") domain indicates the operators defined as part of the ONNX specification; other domains correspond to operator sets of other vendors (meaning they can be used to provide vendor-specific extensions to ONNX). The union of the operator sets specified by a given model MUST have a compatible operator declaration for each node in the model's graph. ### Example This section is not normative and informational only. Given the following operator sets: OpSet|Operators|Comments -|-|- 1|{A} | A introduced 2|{A, B} | B introduced 3|{A', B, C} | A updated (to A'), C introduced 4|{B, C'} | A removed, C updated (to C') The operators for a given operator set will have the following `since_version` values: Operator|OpSet 1|OpSet 2|OpSet 3|OpSet 4 -|-|-|-|- A|**1** |1 |**3** |**-** B|- |**2** |2 |2 C|- |- |**3** |**4** Notes: - Values that are new or updated from a previous OpSet version are in **bold**. ## Model versioning This section of the specification is not normative. It simply outlines a set of recommended practices. Model authors and applications/systems MAY elect to ignore the model versioning mechanism and policy rules. For models that will be shared across developers, teams, or organizations, model authors and applications/systems SHOULD adhere to the following version policies: ### Signature Changes 1. Breaking changes to the ModelProto.graph.GraphProto.input or .output MUST increment the MAJOR version of `ModelProto.model_version`. Breaking changes include: * Breaking changes to the semantics of an input or output (e.g., changing the required contents of an input tensor from a color image to a black and white image). * Changing the declared type of an input or output to an incompatible type (e.g., `tensor(int)->tensor(string)`). * Adding a new input for which there is no meaningful or specified default value. Recall that default values for inputs are specified in the initializer list. * Removing an existing output for which there is no meaningful or specified default value. 2. Non-breaking changes to the ModelProto.graph.GraphProto.input or .output MUST increment the MINOR version of `ModelProto.model_version`. Non-breaking changes include: * Changing the declared type of an input or output to a compatible/widening type (e.g., `tensor(int32)->tensor(int64)`, `tensor(float16)->tensor(float32)`). * Adding a new input for which there is a meaningful or specified default value. * Adding new behavior that is only triggered in the presence of inputs that were not possible in prior versions of the graph (typically by the presence of a new input or allowing a previously invalid input value). ### Accuracy or performance changes Changes that impact accuracy or performance significantly but do not change the model's inputs or outputs SHOULD increment the PATCH version of `ModelProto.model_version`. ## Released Versions ONNX version|IR version|Opset version ai.onnx|Opset version ai.onnx.ml|Opset version ai.onnx.training ------------|-------------------|---------------------|------------------------|------------------------------ 1.0|3|1|1|- 1.1|3|5|1|- 1.1.2|3|6|1|- 1.2|3|7|1|- 1.3|3|8|1|- 1.4.1|4|9|1|- 1.5.0|5|10|1|- 1.6.0|6|11|2|- 1.7.0|7|12|2|1 1.8.0|7|13|2|1 1.8.1|7|13|2|1 1.9.0|7|14|2|1 1.10.0|8|15|2|1 1.10.1|8|15|2|1 1.10.2|8|15|2|1 1.11.0|8|16|3|1 1.12.0|8|17|3|1 1.13.0|8|18|3|1 1.13.1|8|18|3|1 1.14.0|9|19|3|1 1.14.1|9|19|3|1 1.15.0|9|20|4|1 1.16.0|10|21|5|1 1.16.1|10|21|5|1 1.16.2|10|21|5|1 1.17.0|10|22|5|1 1.18.0|11|23|5|1 1.19.0|12|24|5|1 1.19.1|12|24|5|1 1.20.0|13|25|5|1 A programmatically accessible version of the above table is available [here](../onnx/helper.py). 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ÖwQHĨ4a4Š"›ŠBiŋ¤”æ-ŋ¯Âh”F 6w•R:H)M{\†ˇ)Ĩˆ€ĸ¤@;w)ĨèXÖeĩ´ęgŸEnÖx đæņņŅz€<&)Ĩ“hĨų6ĶĪŦ‚hįņxh< H€nÕ{kūUí&Z‚jÍ Āh¤@÷âąí=—š‰ hŗøFG €,~˛ČA €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €,ŌČB €öRJ˙û8ö'zžkcIENDŽB`‚onnx-onnx-bca0315/docs/docsgen/source/_static/readme.txt000066400000000000000000000021661511334557700233720ustar00rootroot00000000000000diff2html: https://github.com/rtfpessoa/diff2html Copyright 2014-2016 Rodrigo Fernandes https://rtfpessoa.github.io/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. onnx-onnx-bca0315/docs/docsgen/source/api/000077500000000000000000000000001511334557700205125ustar00rootroot00000000000000onnx-onnx-bca0315/docs/docsgen/source/api/backend.md000066400000000000000000000007251511334557700224270ustar00rootroot00000000000000# onnx.backend ## Backend ```{eval-rst} .. autoclass:: onnx.backend.base.Backend :members: ``` ## BackendRep ```{eval-rst} .. autoclass:: onnx.backend.base.BackendRep :members: ``` ## Device ```{eval-rst} .. autoclass:: onnx.backend.base.Device :members: ``` ## DeviceType ```{eval-rst} .. autoclass:: onnx.backend.base.DeviceType :members: ``` ## load_model_tests ```{eval-rst} .. autofunction:: onnx.backend.test.loader.load_model_tests ``` onnx-onnx-bca0315/docs/docsgen/source/api/checker.md000066400000000000000000000003661511334557700224450ustar00rootroot00000000000000# onnx.checker ## CheckerContext ```{eval-rst} .. autoclass:: onnx.checker.DEFAULT_CONTEXT :members: ``` ## The `onnx.checker` module ```{eval-rst} .. automodule:: onnx.checker :members: :undoc-members: :show-inheritance: ``` onnx-onnx-bca0315/docs/docsgen/source/api/classes.md000066400000000000000000000213561511334557700225000ustar00rootroot00000000000000(l-onnx-classes)= # Protos This structures are defined with protobuf in files `onnx/*.proto`. It is recommended to use function in module {ref}`l-mod-onnx-helper` to create them instead of directly instantiated them. Every structure can be printed with function `print` and is rendered as a json string. ## AttributeProto This class is used to define an attribute of an operator defined itself by a NodeProto. It is a named attribute containing either singular float, integer, string, graph, and tensor values, or repeated float, integer, string, graph, and tensor values. An AttributeProto MUST contain the name field, and *only one* of the following content fields, effectively enforcing a C/C++ union equivalent. ```{eval-rst} .. autoclass:: onnx.AttributeProto :members: ``` (l-onnx-function-proto)= ## FunctionProto This defines a function. It is not a model but can be used to define custom operators used in a model. ```{eval-rst} .. autoclass:: onnx.FunctionProto :members: ``` (l-onnx-graph-proto)= ## GraphProto This defines a graph or a set of nodes called from a loop or a test for example. A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs. This is the equivalent of the *network* or *graph* in many deep learning frameworks. ```{eval-rst} .. autoclass:: onnx.GraphProto :members: ``` (l-onnx-map-proto)= ## MapProto This defines a map or a dictionary. It specifies an associative table, defined by keys and values. MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR, SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain the same throughout the instantiation of the MapProto. ```{eval-rst} .. autoclass:: onnx.MapProto :members: ``` (l-modelproto)= ## ModelProto This defines a model. That is the type every converting library returns after converting a machine learned model. ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata. The semantics of the model are described by the associated GraphProto's. ```{eval-rst} .. autoclass:: onnx.ModelProto :members: ``` (l-nodeproto)= ## NodeProto This defines an operator. A model is a combination of mathematical functions, each of them represented as an onnx operator, stored in a NodeProto. Computation graphs are made up of a DAG of nodes, which represent what is commonly called a *layer* or *pipeline stage* in machine learning frameworks. For example, it can be a node of type *Conv* that takes in an image, a filter tensor and a bias tensor, and produces the convolved output. ```{eval-rst} .. autoclass:: onnx.NodeProto :members: ``` (l-operatorproto)= ## OperatorProto This class is rarely used by users. An OperatorProto represents the immutable specification of the signature and semantics of an operator. Operators are declared as part of an OperatorSet, which also defines the domain name for the set. Operators are uniquely identified by a three part identifier (domain, op_type, since_version) where - *domain* is the domain of an operator set that contains this operator specification. - *op_type* is the name of the operator as referenced by a NodeProto.op_type - *since_version* is the version of the operator set that this operator was initially declared in. ```{eval-rst} .. autoclass:: onnx.OperatorProto :members: ``` (l-operatorsetidproto)= ## OperatorSetIdProto This is the type of attribute `opset_import` of class ModelProto. This attribute specifies the versions of operators used in the model. Every operator or node belongs to a domain. All operators for the same domain share the same version. ```{eval-rst} .. autoclass:: onnx.OperatorSetIdProto :members: ``` (l-operatorsetproto)= ## OperatorSetProto An OperatorSetProto represents an immutable set of immutable operator specifications. The domain of the set (OperatorSetProto.domain) is a reverse-DNS name that disambiguates operator sets defined by independent entities. The version of the set (opset_version) is a monotonically increasing integer that indicates changes to the membership of the operator set. Operator sets are uniquely identified by a two part identifier (domain, opset_version) Like ModelProto, OperatorSetProto is intended as a top-level file/wire format, and thus has the standard format headers in addition to the operator set information. ```{eval-rst} .. autoclass:: onnx.OperatorSetProto :members: ``` (l-optionalproto)= ## OptionalProto Some input or output of a model are optional. This class must be used in this case. An instance of class OptionalProto may contain or not an instance of type TensorProto, SparseTensorProto, SequenceProto, MapProto and OptionalProto. ```{eval-rst} .. autoclass:: onnx.OptionalProto :members: ``` (l-onnx-sequence-proto)= ## SequenceProto This defines a dense, ordered, collection of elements that are of homogeneous types. Sequences can be made out of tensors, maps, or sequences. If a sequence is made out of tensors, the tensors must have the same element type (i.e. int32). In some cases, the tensors in a sequence can have different shapes. Whether the tensors can have different shapes or not depends on the type/shape associated with the corresponding `ValueInfo`. For example, `Sequence` means that all tensors have same shape. However, `Sequence` means they can have different shapes (all of rank 2), where *omitted* means the corresponding dimension has no symbolic/constant value. Finally, `Sequence>` means that the different tensors can have different ranks, when the *shape* itself is omitted from the tensor-type. For a more complete description, refer to [Static tensor shapes](https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes). ```{eval-rst} .. autoclass:: onnx.SequenceProto :members: ``` (l-onnx-sparsetensor-proto)= ## SparseTensorProto This defines a sparse tensor. The sequence of non-default values are encoded as a tensor of shape `[NNZ]`. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list. ```{eval-rst} .. autoclass:: onnx.SparseTensorProto :members: ``` (l-onnx-stringstringentry-proto)= ## StringStringEntryProto This is equivalent to a pair of strings. This is used to store metadata in ModelProto. ```{eval-rst} .. autoclass:: onnx.StringStringEntryProto :members: ``` (l-tensorproto)= ## TensorProto This defines a tensor. A tensor is fully described with a shape (see ShapeProto), the element type (see TypeProto), and the elements themselves. All available types are listed in {ref}`l-mod-onnx-mapping`. ```{eval-rst} .. autoclass:: onnx.TensorProto :members: ``` (l-tensorshapeproto)= ## TensorShapeProto This defines the shape of a tensor or a sparse tensor. It is a list of dimensions. A dimension can be either an integer value or a symbolic variable. A symbolic variable represents an unknown dimension. ```{eval-rst} .. autoclass:: onnx.TensorShapeProto :members: ``` (l-traininginfoproto)= ## TrainingInfoProto TrainingInfoProto stores information for training a model. In particular, this defines two functionalities: an initialization-step and a training-algorithm-step. Initialization resets the model back to its original state as if no training has been performed. Training algorithm improves the model based on input data. The semantics of the initialization-step is that the initializers in ModelProto.graph and in TrainingInfoProto.algorithm are first initialized as specified by the initializers in the graph, and then updated by the *initialization_binding* in every instance in ModelProto.training_info. The field *algorithm* defines a computation graph which represents a training algorithm's step. After the execution of a TrainingInfoProto.algorithm, the initializers specified by *update_binding* may be immediately updated. If the targeted training algorithm contains consecutive update steps (such as block coordinate descent methods), the user needs to create a TrainingInfoProto for each step. ```{eval-rst} .. autoclass:: onnx.TrainingInfoProto :members: ``` (l-typeproto)= ## TypeProto This defines a type of a tensor which consists in an element type and a shape (ShapeProto). ```{eval-rst} .. autoclass:: onnx.TypeProto :members: ``` (l-valueinfoproto)= ## ValueInfoProto This defines a input or output type of a GraphProto. It contains a name, a type (TypeProto), and a documentation string. ```{eval-rst} .. autoclass:: onnx.ValueInfoProto :members: ``` onnx-onnx-bca0315/docs/docsgen/source/api/compose.md000066400000000000000000000011121511334557700224740ustar00rootroot00000000000000# onnx.compose ```{eval-rst} .. currentmodule:: onnx.compose ``` ```{eval-rst} .. autosummary:: merge_graphs merge_models ``` ## merge_graphs ```{eval-rst} .. autofunction:: onnx.compose.merge_graphs ``` ## merge_models ```{eval-rst} .. autofunction:: onnx.compose.merge_models ``` ## prefix ```{eval-rst} .. autofunction:: onnx.compose.add_prefix_graph ``` ```{eval-rst} .. autofunction:: onnx.compose.add_prefix ``` ## dimension ```{eval-rst} .. autofunction:: onnx.compose.expand_out_dim ``` ```{eval-rst} .. autofunction:: onnx.compose.expand_out_dim_graph ``` onnx-onnx-bca0315/docs/docsgen/source/api/defs.md000066400000000000000000000020631511334557700217560ustar00rootroot00000000000000(l-mod-onnx-defs)= # onnx.defs (l-api-opset-version)= ## Opset Version ```{eval-rst} .. autofunction:: onnx.defs.onnx_opset_version ``` ## Operators and Functions Schemas ```{eval-rst} .. autofunction:: onnx.defs.has .. autofunction:: onnx.defs.get_schema .. autofunction:: onnx.defs.get_all_schemas .. autofunction:: onnx.defs.get_all_schemas_with_history .. autofunction:: onnx.defs.get_function_ops .. autofunction:: onnx.defs.register_schema .. autofunction:: onnx.defs.deregister_schema ``` ## class `OpSchema` ```{eval-rst} .. autoclass:: onnx.defs.OpSchema :members: :undoc-members: ``` ## Exceptions ```{eval-rst} .. autoclass:: onnx.defs.SchemaError ``` ## Constants Domains officially supported in onnx package. ```{eval-rst} .. exec_code:: from onnx.defs import ( ONNX_DOMAIN, ONNX_ML_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) print(f"ONNX_DOMAIN={ONNX_DOMAIN!r}") print(f"ONNX_ML_DOMAIN={ONNX_ML_DOMAIN!r}") print(f"AI_ONNX_PREVIEW_TRAINING_DOMAIN={AI_ONNX_PREVIEW_TRAINING_DOMAIN!r}") ``` onnx-onnx-bca0315/docs/docsgen/source/api/external_data_helper.md000066400000000000000000000022551511334557700252120ustar00rootroot00000000000000# onnx.external_data_helper ## convert_model_from_external_data ```{eval-rst} .. autofunction:: onnx.external_data_helper.convert_model_from_external_data ``` ## convert_model_to_external_data ```{eval-rst} .. autofunction:: onnx.external_data_helper.convert_model_to_external_data ``` ## ExternalDataInfo ```{eval-rst} .. autoclass:: onnx.external_data_helper.ExternalDataInfo ``` ## load_external_data_for_model ```{eval-rst} .. autofunction:: onnx.external_data_helper.load_external_data_for_model ``` ## load_external_data_for_tensor ```{eval-rst} .. autofunction:: onnx.external_data_helper.load_external_data_for_tensor ``` ## remove_external_data_field ```{eval-rst} .. autofunction:: onnx.external_data_helper.remove_external_data_field ``` ## save_external_data ```{eval-rst} .. autofunction:: onnx.external_data_helper.save_external_data ``` ## set_external_data ```{eval-rst} .. autofunction:: onnx.external_data_helper.set_external_data ``` ## uses_external_data ```{eval-rst} .. autofunction:: onnx.external_data_helper.uses_external_data ``` ## write_external_data_tensors ```{eval-rst} .. autofunction:: onnx.external_data_helper.write_external_data_tensors ``` onnx-onnx-bca0315/docs/docsgen/source/api/helper.md000066400000000000000000000053041511334557700223150ustar00rootroot00000000000000(l-mod-onnx-helper)= # onnx.helper ```{eval-rst} .. currentmodule:: onnx.helper ``` (l-onnx-make-function)= ## Helper functions to make ONNX graph components All functions used to create an ONNX graph. ```{eval-rst} .. autofunction:: onnx.helper.make_attribute ``` ```{eval-rst} .. autofunction:: onnx.helper.make_attribute_ref ``` ```{eval-rst} .. autofunction:: onnx.helper.make_empty_tensor_value_info ``` ```{eval-rst} .. autofunction:: onnx.helper.make_function ``` ```{eval-rst} .. autofunction:: onnx.helper.make_graph ``` ```{eval-rst} .. autofunction:: onnx.helper.make_map ``` ```{eval-rst} .. autofunction:: onnx.helper.make_map_type_proto ``` ```{eval-rst} .. autofunction:: onnx.helper.make_model ``` ```{eval-rst} .. autofunction:: onnx.helper.make_node ``` ```{eval-rst} .. autofunction:: onnx.helper.make_operatorsetid ``` ```{eval-rst} .. autofunction:: onnx.helper.make_opsetid ``` ```{eval-rst} .. autofunction:: onnx.helper.make_model_gen_version ``` ```{eval-rst} .. autofunction:: onnx.helper.make_optional ``` ```{eval-rst} .. autofunction:: onnx.helper.make_optional_type_proto ``` ```{eval-rst} .. autofunction:: onnx.helper.make_sequence ``` ```{eval-rst} .. autofunction:: onnx.helper.make_sequence_type_proto ``` ```{eval-rst} .. autofunction:: onnx.helper.make_sparse_tensor ``` ```{eval-rst} .. autofunction:: onnx.helper.make_sparse_tensor_type_proto ``` ```{eval-rst} .. autofunction:: onnx.helper.make_sparse_tensor_value_info ``` ```{eval-rst} .. autofunction:: onnx.helper.make_tensor ``` ```{eval-rst} .. autofunction:: onnx.helper.make_tensor_sequence_value_info ``` ```{eval-rst} .. autofunction:: onnx.helper.make_tensor_type_proto ``` ```{eval-rst} .. autofunction:: onnx.helper.make_training_info ``` ```{eval-rst} .. autofunction:: onnx.helper.make_tensor_value_info ``` ```{eval-rst} .. autofunction:: onnx.helper.make_value_info ``` ## Type Mappings ```{eval-rst} .. autofunction:: onnx.helper.get_all_tensor_dtypes ``` ```{eval-rst} .. autofunction:: onnx.helper.np_dtype_to_tensor_dtype ``` ```{eval-rst} .. autofunction:: onnx.helper.tensor_dtype_to_field ``` ```{eval-rst} .. autofunction:: onnx.helper.tensor_dtype_to_np_dtype ``` ```{eval-rst} .. autofunction:: onnx.helper.tensor_dtype_to_storage_tensor_dtype ``` ```{eval-rst} .. autofunction:: onnx.helper.tensor_dtype_to_string ``` ## Tools ```{eval-rst} .. autofunction:: onnx.helper.find_min_ir_version_for ``` ## Other functions ```{eval-rst} .. autosummary:: get_attribute_value get_node_attr_value set_metadata_props set_model_props printable_attribute printable_dim printable_graph printable_node printable_tensor_proto printable_type printable_value_info ``` onnx-onnx-bca0315/docs/docsgen/source/api/hub.md000066400000000000000000000012001511334557700216030ustar00rootroot00000000000000# onnx.hub ## ModelInfo ```{eval-rst} .. autoclass:: onnx.hub.ModelInfo :members: ``` ## download_model_with_test_data ```{eval-rst} .. autofunction:: onnx.hub.download_model_with_test_data ``` ## get_model_info ```{eval-rst} .. autofunction:: onnx.hub.get_model_info ``` ## list_models ```{eval-rst} .. autofunction:: onnx.hub.list_models ``` ## load ```{eval-rst} .. autofunction:: onnx.hub.load ``` ## load_composite_model ```{eval-rst} .. autofunction:: onnx.hub.load_composite_model ``` ## set_dir ```{eval-rst} .. autofunction:: onnx.hub.set_dir ``` ## get_dir ```{eval-rst} .. autofunction:: onnx.hub.get_dir ``` onnx-onnx-bca0315/docs/docsgen/source/api/index.md000066400000000000000000000035331511334557700221470ustar00rootroot00000000000000(l-python-onnx-api)= # API Reference ```{tip} The [ir-py project](https://github.com/onnx/ir-py) provides alternative Pythonic APIs for creating and manipulating ONNX models without interaction with Protobuf. ``` ## Versioning The following example shows how to retrieve onnx version, the onnx opset, the IR version. Every new major release increments the opset version (see {ref}`l-api-opset-version`). ```{eval-rst} .. exec_code:: from onnx import __version__, IR_VERSION from onnx.defs import onnx_opset_version print(f"onnx.__version__={__version__!r}, opset={onnx_opset_version()}, IR_VERSION={IR_VERSION}") ``` The intermediate representation (IR) specification is the abstract model for graphs and operators and the concrete format that represents them. Adding a structure, modifying one them increases the IR version. The opset version increases when an operator is added or removed or modified. A higher opset means a longer list of operators and more options to implement an ONNX functions. An operator is usually modified because it supports more input and output type, or an attribute becomes an input. ## Data Structures Every ONNX object is defined based on a [protobuf message](https://googleapis.dev/python/protobuf/latest/google/protobuf/message.html) and has a name ended with suffix `Proto`. For example, {ref}`l-nodeproto` defines an operator, {ref}`l-tensorproto` defines a tensor. Next page lists all of them. ```{toctree} :maxdepth: 1 classes serialization ``` ## Functions An ONNX model can be directly from the classes described in previous section but it is faster to create and verify a model with the following helpers. ```{toctree} :maxdepth: 1 backend checker compose defs external_data_helper helper hub inliner mapping model_container numpy_helper parser printer reference shape_inference tools utils version_converter ``` onnx-onnx-bca0315/docs/docsgen/source/api/inliner.md000066400000000000000000000003351511334557700224750ustar00rootroot00000000000000# onnx.inliner ## inline_local_functions ```{eval-rst} .. autofunction:: onnx.inliner.inline_local_functions ``` ## inline_selected_functions ```{eval-rst} .. autofunction:: onnx.inliner.inline_selected_functions ``` onnx-onnx-bca0315/docs/docsgen/source/api/model_container.md000066400000000000000000000005141511334557700241760ustar00rootroot00000000000000# onnx.model_container ## ModelContainer ```{eval-rst} .. autoclass:: onnx.model_container.ModelContainer :members: ``` ## make_large_model ```{eval-rst} .. autofunction:: onnx.model_container.make_large_model ``` ## make_large_tensor_proto ```{eval-rst} .. autofunction:: onnx.model_container.make_large_tensor_proto ``` onnx-onnx-bca0315/docs/docsgen/source/api/numpy_helper.md000066400000000000000000000016411511334557700235450ustar00rootroot00000000000000# onnx.numpy_helper ```{eval-rst} .. currentmodule:: onnx.numpy_helper ``` ```{eval-rst} .. autosummary:: from_array from_dict from_list from_optional to_array to_dict to_list to_optional ``` (l-numpy-helper-onnx-array)= ## array ```{eval-rst} .. autofunction:: onnx.numpy_helper.from_array ``` ```{eval-rst} .. autofunction:: onnx.numpy_helper.to_array ``` Arrays with data types not supported natively by NumPy will be return with ``ml_dtypes`` dtypes. ## sequence ```{eval-rst} .. autofunction:: onnx.numpy_helper.to_list ``` ```{eval-rst} .. autofunction:: onnx.numpy_helper.from_list ``` ## dictionary ```{eval-rst} .. autofunction:: onnx.numpy_helper.to_dict ``` ```{eval-rst} .. autofunction:: onnx.numpy_helper.from_dict ``` ## optional ```{eval-rst} .. autofunction:: onnx.numpy_helper.to_optional ``` ```{eval-rst} .. autofunction:: onnx.numpy_helper.from_optional ``` onnx-onnx-bca0315/docs/docsgen/source/api/parser.md000066400000000000000000000005061511334557700223310ustar00rootroot00000000000000# onnx.parser ## parse_node ```{eval-rst} .. autofunction:: onnx.parser.parse_node ``` ## parse_function ```{eval-rst} .. autofunction:: onnx.parser.parse_function ``` ## parse_graph ```{eval-rst} .. autofunction:: onnx.parser.parse_graph ``` ## parse_model ```{eval-rst} .. autofunction:: onnx.parser.parse_model ``` onnx-onnx-bca0315/docs/docsgen/source/api/printer.md000066400000000000000000000001251511334557700225150ustar00rootroot00000000000000# onnx.printer ## to_text ```{eval-rst} .. autofunction:: onnx.printer.to_text ``` onnx-onnx-bca0315/docs/docsgen/source/api/reference.md000066400000000000000000000015161511334557700227750ustar00rootroot00000000000000(l-reference-implementation)= # onnx.reference ## DefaultNone ```{eval-rst} .. autoclass:: onnx.reference.op_run.DefaultNone :members: ``` ## ReferenceEvaluator ```{eval-rst} .. autoclass:: onnx.reference.ReferenceEvaluator :members: input_names, output_names, opsets, run ``` ## OpFunction ```{eval-rst} .. autoclass:: onnx.reference.op_run.OpFunction :members: create, eval, input, output, implicit_inputs, domain, need_context, run, make_node ``` ## OpRun ```{eval-rst} .. autoclass:: onnx.reference.op_run.OpRun :members: create, eval, input, output, implicit_inputs, domain, need_context, run, make_node ``` ## RuntimeTypeError ```{eval-rst} .. autoclass:: onnx.reference.op_run.RuntimeTypeError :members: ``` ## SparseTensor ```{eval-rst} .. autoclass:: onnx.reference.op_run.SparseTensor :members: ``` onnx-onnx-bca0315/docs/docsgen/source/api/serialization.md000066400000000000000000000045361511334557700237210ustar00rootroot00000000000000(l-serialization)= # Serialization ## Save a model and any Proto class This ONNX graph needs to be serialized into one contiguous memory buffer. Method `SerializeToString` is available in every ONNX objects. ``` with open("model.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) ``` This method has the following signature. ```{eval-rst} .. autoclass:: onnx.ModelProto :members: SerializeToString ``` Every Proto class implements method `SerializeToString`. Therefore the following code works with any class described in page {ref}`l-onnx-classes`. ``` with open("proto.pb", "wb") as f: f.write(proto.SerializeToString()) ``` Next example shows how to save a {ref}`l-nodeproto`. ```{eval-rst} .. exec_code:: from onnx import NodeProto node = NodeProto() node.name = "example-type-proto" node.op_type = "Add" node.input.extend(["X", "Y"]) node.output.extend(["Z"]) with open("node.pb", "wb") as f: f.write(node.SerializeToString()) ``` ## Load a model Following function only automates the loading of a class {ref}`l-modelproto`. Next sections shows how to restore any other proto class. ```{eval-rst} .. autofunction:: onnx.load ``` ``` from onnx import load onnx_model = load("model.onnx") ``` Or: ``` from onnx import load with open("model.onnx", "rb") as f: onnx_model = load(f) ``` Next function does the same from a bytes array. ```{eval-rst} .. autofunction:: onnx.load_model_from_string ``` (l-onnx-load-data)= ## Load a Proto Proto means here any type containing data including a model, a tensor, a sparse tensor, any class listed in page {ref}`l-onnx-classes`. The user must know the type of the data he needs to restore and then call method `ParseFromString`. [protobuf](https://developers.google.com/protocol-buffers) does not store any information about the class of the saved data. Therefore, this class must be known before restoring an object. ```{eval-rst} .. autoclass:: onnx.ModelProto :members: ParseFromString ``` Next example shows how to restore a {ref}`l-nodeproto`. ```{eval-rst} .. exec_code:: from onnx import NodeProto tp2 = NodeProto() with open("node.pb", "rb") as f: content = f.read() tp2.ParseFromString(content) print(tp2) ``` A shortcut exists for {ref}`l-tensorproto`: ```{eval-rst} .. autofunction:: onnx.load_tensor_from_string ``` onnx-onnx-bca0315/docs/docsgen/source/api/shape_inference.md000066400000000000000000000006531511334557700241560ustar00rootroot00000000000000# onnx.shape_inference ## infer_shapes ```{eval-rst} .. autofunction:: onnx.shape_inference.infer_shapes ``` ## infer_shapes_path ```{eval-rst} .. autofunction:: onnx.shape_inference.infer_shapes_path ``` ## infer_node_outputs ```{eval-rst} .. autofunction:: onnx.shape_inference.infer_node_outputs ``` ## infer_function_output_types ```{eval-rst} .. autofunction:: onnx.shape_inference.infer_function_output_types ``` onnx-onnx-bca0315/docs/docsgen/source/api/tools.md000066400000000000000000000013641511334557700222000ustar00rootroot00000000000000# onnx.tools ## net_drawer ```{eval-rst} .. autofunction:: onnx.tools.net_drawer.GetPydotGraph ``` ```{eval-rst} .. autofunction:: onnx.tools.net_drawer.GetOpNodeProducer ``` ``` from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer pydot_graph = GetPydotGraph( model_onnx.graph, # model_onnx is a ModelProto instance name=model_onnx.graph.name, rankdir="TP", node_producer=GetOpNodeProducer("docstring")) pydot_graph.write_dot("graph.dot") ``` ## update_inputs_outputs_dims ```{eval-rst} .. autofunction:: onnx.tools.update_model_dims.update_inputs_outputs_dims ``` ## replace_initializer_by_constant_of_shape ```{eval-rst} .. autofunction:: onnx.tools.replace_constants.replace_initializer_by_constant_of_shape ``` onnx-onnx-bca0315/docs/docsgen/source/api/utils.md000066400000000000000000000002601511334557700221720ustar00rootroot00000000000000# onnx.utils ## Extractor ```{eval-rst} .. autoclass:: onnx.utils.Extractor :members: ``` ## extract_model ```{eval-rst} .. autofunction:: onnx.utils.extract_model ``` onnx-onnx-bca0315/docs/docsgen/source/api/version_converter.md000066400000000000000000000001711511334557700246070ustar00rootroot00000000000000# onnx.version_converter ## convert_version ```{eval-rst} .. autofunction:: onnx.version_converter.convert_version ``` onnx-onnx-bca0315/docs/docsgen/source/conf.py000066400000000000000000000060751511334557700212500ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # type: ignore from __future__ import annotations import os import sys import warnings import onnx sys.path.append(os.path.abspath(os.path.dirname(__file__))) # -- Project information ----------------------------------------------------- author = "ONNX" copyright = "2024" project = "ONNX" release = onnx.__version__ version = onnx.__version__ # define the latest opset to document, # this is meant to avoid documenting opset not released yet max_opset = onnx.helper.VERSION_TABLE[-1][2] # define the latest opset to document for every opset _opsets = [t for t in onnx.helper.VERSION_TABLE if t[2] == max_opset][-1] max_opsets = { "": max_opset, "ai.onnx.ml": _opsets[3], "ai.onnx.training": _opsets[4], } # -- General configuration --------------------------------------------------- extensions = [ "myst_parser", "onnx_sphinx", "sphinx_copybutton", "sphinx_exec_code", "sphinx_tabs.tabs", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.coverage", "sphinx.ext.doctest", "sphinx.ext.githubpages", "sphinx.ext.graphviz", "sphinx.ext.ifconfig", "sphinx.ext.intersphinx", "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx.ext.viewcode", ] myst_enable_extensions = [ "amsmath", "attrs_inline", "colon_fence", "deflist", "dollarmath", "fieldlist", "html_admonition", "html_image", "linkify", "replacements", "smartquotes", "strikethrough", "substitution", "tasklist", ] coverage_show_missing_items = True exclude_patterns = [] graphviz_output_format = "svg" html_css_files = ["css/custom.css"] html_favicon = "onnx-favicon.png" html_sidebars = {} html_static_path = ["_static"] html_theme = "furo" language = "en" mathdef_link_only = True master_doc = "index" onnx_doc_folder = os.path.join(os.path.abspath(os.path.dirname(__file__)), "operators") pygments_style = "default" source_suffix = [".rst", ".md"] templates_path = ["_templates"] html_context = { "default_mode": "auto", # auto: the documentation theme will follow the system default that you have set (light or dark) } html_theme_options = { "light_logo": "onnx-horizontal-color.png", "dark_logo": "onnx-horizontal-white.png", } intersphinx_mapping = { "numpy": ("https://numpy.org/doc/stable/", None), "python": (f"https://docs.python.org/{sys.version_info.major}/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), "torch": ("https://pytorch.org/docs/stable/", None), } sphinx_gallery_conf = { "examples_dirs": ["examples"], "gallery_dirs": ["auto_examples", "auto_tutorial"], "capture_repr": ("_repr_html_", "__repr__"), "ignore_repr_types": r"matplotlib.text|matplotlib.axes", "binder": { "org": "onnx", "repo": ".", "notebooks_dir": "auto_examples", "binderhub_url": "https://mybinder.org", "branch": "master", "dependencies": "./requirements.txt", }, } warnings.filterwarnings("ignore", category=FutureWarning) onnx-onnx-bca0315/docs/docsgen/source/expect_onnxruntime.md000066400000000000000000000047651511334557700242350ustar00rootroot00000000000000 (l-function-expect)= # Sample operator test code Many examples from the documentation end by calling function `expect` to check a runtime returns the expected outputs for the given example. Here is one implementation based on [onnxruntime](https://onnxruntime.ai/). ``` from typing import Any, Sequence import numpy as np import onnx import onnxruntime def expect( node: onnx.NodeProto, inputs: Sequence[np.ndarray], outputs: Sequence[np.ndarray], name: str, **kwargs: Any, ) -> None: # Builds the model present_inputs = [x for x in node.input if (x != "")] present_outputs = [x for x in node.output if (x != "")] input_type_protos = [None] * len(inputs) if "input_type_protos" in kwargs: input_type_protos = kwargs["input_type_protos"] del kwargs["input_type_protos"] output_type_protos = [None] * len(outputs) if "output_type_protos" in kwargs: output_type_protos = kwargs["output_type_protos"] del kwargs["output_type_protos"] inputs_vi = [ _extract_value_info(arr, arr_name, input_type) for arr, arr_name, input_type in zip(inputs, present_inputs, input_type_protos) ] outputs_vi = [ _extract_value_info(arr, arr_name, output_type) for arr, arr_name, output_type in zip( outputs, present_outputs, output_type_protos ) ] graph = onnx.helper.make_graph( nodes=[node], name=name, inputs=inputs_vi, outputs=outputs_vi ) kwargs["producer_name"] = "backend-test" if "opset_imports" not in kwargs: # To make sure the model will be produced with the same opset_version after opset changes # By default, it uses since_version as opset_version for produced models produce_opset_version = onnx.defs.get_schema( node.op_type, domain=node.domain ).since_version kwargs["opset_imports"] = [ onnx.helper.make_operatorsetid(node.domain, produce_opset_version) ] model = onnx.helper.make_model_gen_version(graph, **kwargs) # Checking the produces are the expected ones. sess = onnxruntime.InferenceSession(model.SerializeToString(), providers=["CPUExecutionProvider"]) feeds = {name: value for name, value in zip(node.input, inputs)} results = sess.run(None, feeds) for expected, output in zip(outputs, results): assert_allclose(expected, output) ``` onnx-onnx-bca0315/docs/docsgen/source/index.md000066400000000000000000000003461511334557700213750ustar00rootroot00000000000000 (l-main-doc-page)= # ONNX documentation ```{toctree} :maxdepth: 2 intro/index api/index operators/index technical/index repo-docs/index ``` onnx-onnx-bca0315/docs/docsgen/source/intro/000077500000000000000000000000001511334557700210745ustar00rootroot00000000000000onnx-onnx-bca0315/docs/docsgen/source/intro/concepts.md000066400000000000000000000330131511334557700232340ustar00rootroot00000000000000# ONNX Concepts ONNX can be compared to a programming language specialized in mathematical functions. It defines all the necessary operations a machine learning model needs to implement its inference function with this language. A linear regression could be represented in the following way: ``` def onnx_linear_regressor(X): "ONNX code for a linear regression" return onnx.Add(onnx.MatMul(X, coefficients), bias) ``` ```{index} ONNX graph ``` This example is very similar to an expression a developer could write in Python. It can be also represented as a graph that shows step-by-step how to transform the features to get a prediction. That's why a machine-learning model implemented with ONNX is often referenced as an **ONNX graph**. ```{image} images/linreg1.png ``` ONNX aims at providing a common language any machine learning framework can use to describe its models. The first scenario is to make it easier to deploy a machine learning model in production. An ONNX interpreter (or **runtime**) can be specifically implemented and optimized for this task in the environment where it is deployed. With ONNX, it is possible to build a unique process to deploy a model in production and independent from the learning framework used to build the model. *onnx* implements a python runtime that can be used to evaluate ONNX models and to evaluate ONNX ops. This is intended to clarify the semantics of ONNX and to help understand and debug ONNX tools and converters. It is not intended to be used for production and performance is not a goal (see {ref}`l-reference-implementation`). ## Input, Output, Node, Initializer, Attributes Building an ONNX graph means implementing a function with the ONNX language or more precisely the {ref}`l-onnx-operators`. A linear regression would be written this way. The following lines do not follow python syntax. It is just a kind of pseudo-code to illustrate the model. ``` Input: float[M,K] x, float[K,N] a, float[N] c Output: float[M, N] y r = onnx.MatMul(x, a) y = onnx.Add(r, c) ``` This code implements a function `f(x, a, c) -> y = x @ a + c`. And *x*, *a*, *c* are the **inputs**, *y* is the **output**. *r* is an intermediate result. *MatMul* and *Add* are the **nodes**. They also have inputs and outputs. A node has also a type, one of the operators in {ref}`l-onnx-operators`. This graph was built with the example in Section {ref}`l-onnx-linear-regression-onnx-api`. The graph could also have an **initializer**. When an input never changes such as the coefficients of the linear regression, it is most efficient to turn it into a constant stored in the graph. ``` Input: float[M,K] x Initializer: float[K,N] a, float[N] c Output: float[M, N] xac xa = onnx.MatMul(x, a) xac = onnx.Add(xa, c) ``` Visually, this graph would look like the following image. The right side describes operator *Add* where the second input is defined as an initializer. This graph was obtained with this code {ref}`l-onnx-linear-regression-onnx-api-init`. ```{image} images/linreg2.png :alt: Snapshot of Netron ``` An **attribute** is a fixed parameter of an operator. Operator {ref}`l-onnx-doc-Gemm` has four attributes, *alpha*, *beta*, *transA*, *transB*. Unless the runtime allows it through its API, once it has loaded the ONNX graph, these values cannot be changed and remain frozen for all the predictions. ## Serialization with protobuf The deployment of a machine-learned model into production usually requires replicating the entire ecosystem used to train the model, most of the time with a *docker*. Once a model is converted into ONNX, the production environment only needs a runtime to execute the graph defined with ONNX operators. This runtime can be developed in any language suitable for the production application, C, java, python, javascript, C#, Webassembly, ARM... But to make that happen, the ONNX graph needs to be saved. ONNX uses *protobuf* to serialize the graph into one single block (see [Parsing and Serialization](https://developers.google.com/protocol-buffers/docs/pythontutorial#parsing-and-serialization)). It aims at optimizing the model size as much as possible. ## Metadata Machine learned models are continuously refreshed. It is important to keep track of the model version, the author of the model and how it was trained. ONNX offers the possibility to store additional data in the model itself. - **doc_string**: Human-readable documentation for this model. : Markdown is allowed. - **domain**: A reverse-DNS name to indicate the model namespace or domain, : for example, 'org.onnx' - **metadata_props**: Named metadata as dictionary `map`, : `(values, keys)` should be distinct. - **model_author**: A comma-separated list of names, : The personal name of the author(s) of the model, and/or their organizations. - **model_license**: The well-known name or URL of the license : under which the model is made available. - **model_version**: The version of the model itself, encoded in an integer. - **producer_name**: The name of the tool used to generate the model. - **producer_version**: The version of the generating tool. - **training_info**: An optional extension that contains : information for training (see {ref}`l-traininginfoproto`) ## List of available operators and domains The main list is described here: {ref}`l-onnx-operators`. It merges standard matrix operators (Add, Sub, MatMul, Transpose, Greater, IsNaN, Shape, Reshape...), reductions (ReduceSum, ReduceMin, ...) image transformations (Conv, MaxPool, ...), deep neural networks layer (RNN, DropOut, ...), activations functions (Relu, Softmax, ...). It covers most of the operations needed to implement inference functions from standard and deep machine learning. ONNX does not implement every existing machine learning operator, the list of operator would be infinite. The main list of operators is identified with a domain **ai.onnx**. A **domain** can be defined as a set of operators. A few operators in this list are dedicated to text but they hardly cover the needs. The main list is also missing tree based models very popular in standard machine learning. These are part of another domain **ai.onnx.ml**, it includes tree bases models (TreeEnsemble Regressor, ...), preprocessing (OneHotEncoder, LabelEncoder, ...), SVM models (SVMRegressor, ...), imputer (Imputer). ONNX only defines these two domains. But the library onnx supports any custom domains and operators (see {ref}`l-onnx-extensibility`). ## Supported Types ONNX specifications are optimized for numerical computation with tensors. A *tensor* is a multidimensional array. It is defined by: - a type: the element type, the same for all elements in the tensor - a shape: an array with all dimensions, this array can be empty, a dimension can be null - a contiguous array: it represents all the values This definition does not include *strides* or the possibility to define a view of a tensor based on an existing tensor. An ONNX tensor is a dense full array with no stride. ### Element Type ONNX was initially developed to help deploying deep learning model. That's why the specifications were initially designed for floats (32 bits). The current version supports all common types. Dictionary {ref}`l-onnx-types-mapping` gives the correspondence between *ONNX* and {mod}`numpy`. ```{eval-rst} .. exec_code:: import re from onnx import TensorProto reg = re.compile('^[0-9A-Z_]+$') values = {} for att in sorted(dir(TensorProto)): if att in {'DESCRIPTOR'}: continue if reg.match(att): values[getattr(TensorProto, att)] = att for i, att in sorted(values.items()): si = str(i) if len(si) == 1: si = " " + si print("%s: onnx.TensorProto.%s" % (si, att)) ``` ONNX is strongly typed and its definition does not support implicit cast. ONNX does not allow addition of two tensors or matrices with different types, even if other languages do. That's why an explicit cast must be inserted in a graph. ### Sparse Tensor Sparse tensors are useful to represent arrays having many null coefficients. ONNX supports 2D sparse tensor. Class {ref}`l-onnx-sparsetensor-proto` defines attributes `dims`, `indices` (int64) and `values`. ### Other types In addition to tensors and sparse tensors, ONNX supports sequences of tensors, map of tensors, sequences of map of tensors through types {ref}`l-onnx-sequence-proto`, {ref}`l-onnx-map-proto`. They are rarely used. ## What is an opset version? The opset is mapped to the version of the *onnx* package. It is incremented every time the minor version increases. Every version brings updated or new operators. ```{eval-rst} .. exec_code:: import onnx print(onnx.__version__, " opset=", onnx.defs.onnx_opset_version()) ``` An opset version is also attached to every ONNX graph. It defines the version of all operators inside the graph. Operator *Add* was updated in version 6, 7, 13 and 14. If the graph opset is 15, it means operator *Add* follows specifications version 14. If the graph opset is 12, then operator *Add* follows specifications version 7. An operator in a graph follows its most recent definition below (or equal) the global graph opset. A graph may include operators from several domains, `ai.onnx` and `ai.onnx.ml` for example. In that case, the graph must define a global opset for every domain. The rule is applied to every operators within the same domain. ## Subgraphs, tests and loops ONNX implements tests and loops. They all take another ONNX graphs as an attribute. These structures are usually slow and complex. It is better to avoid them if possible. ### If Operator {ref}`l-onnx-doc-If` executes one of the two graphs depending on the condition evaluation. ``` If(condition) then execute this ONNX graph (`then_branch`) else execute this ONNX graph (`else_branch`) ``` Those two graphs can use any result already computed in the graph and must produce the exact same number of outputs. These outputs will be the output of the operator `If`. ```{image} images/dot_if.png ``` (l-operator-scan-onnx-tutorial)= ### Scan Operator {ref}`l-onnx-doc-Scan` implements a loop with a fixed number of iterations. It loops over the rows (or any other dimension) of the inputs and concatenates the outputs along the same axis. Let's see an example which implements pairwise distances: $M(i,j) = \lVert X_i - X_j \rVert^2$. ```{image} images/dot_scan.png ``` This loop is efficient even if it is still slower than a custom implementation of pairwise distances. It assumes inputs and outputs are tensors and automatically concatenate the outputs of every iteration into single tensors. The previous example only has one but it could have several. ### Loop Operator {ref}`l-onnx-doc-Loop` implements a for and a while loop. It can do a fixed number of iterators and/or ends when a condition is not met anymore. Outputs are processed in two different ways. First one is similar to loop {ref}`l-onnx-doc-Scan`, outputs are concatenated into tensors (along the first dimension). This also means that these outputs must have compatible shapes. Second mechanism concatenates tensors into a sequence of tensors. (l-onnx-extensibility)= ## Extensibility ONNX defines a list of operators as the standard: {ref}`l-onnx-operators`. However, it is very possible to define your own operators under this domain or a new one. *onnxruntime* defines custom operators to improve inference. Every node has a type, a name, named inputs and outputs, and attributes. As long as a node is described under these constraints, a node can be added to any ONNX graph. Pairwise distances can be implemented with operator Scan. However, a dedicated operator called CDist is proved significantly faster, significantly enough to make the effort to implement a dedicated runtime for it. ## Functions Functions are one way to extend ONNX specifications. Some model requires the same combination of operators. This can be avoided by creating a function itself defined with existing ONNX operators. Once defined, a function behaves like any other operators. It has inputs, outputs and attributes. There are two advantages of using functions. The first one is to have a shorter code and easier to read. The second one is that any onnxruntime can leverage that information to run predictions faster. The runtime could have a specific implementation for a function not relying on the implementation of the existing operators. ## Shape (and Type) Inference Knowing the shapes of results is not necessary to execute an ONNX graph but this information can be used to make it faster. If you have the following graph: ``` Add(x, y) -> z Abs(z) -> w ``` If *x* and *y* have the same shape, then *z* and *w* also have the same shape. Knowing that, it is possible to reuse the buffer allocated for *z*, to compute the absolute value *w* inplace. Shape inference helps the runtime to manage the memory and therefore to be more efficient. ONNX package can compute in most of the cases the output shape knowing the input shape for every standard operator. It cannot obviously do that for any custom operator outside of the official list. ## Tools [netron](https://netron.app/) is very useful to help visualize ONNX graphs. That's the only one without programming. The first screenshot was made with this tool. ```{image} images/linreg1.png ``` [onnx2py.py](https://github.com/microsoft/onnxconverter-common/blob/master/onnxconverter_common/onnx2py.py) creates a python file from an ONNX graph. This script can create the same graph. It may be modified by a user to change the graph. [zetane](https://github.com/zetane/viewer) can load onnx model and show intermediate results when the model is executed. onnx-onnx-bca0315/docs/docsgen/source/intro/converters.md000066400000000000000000000302041511334557700236070ustar00rootroot00000000000000# Converters Using ONNX in production means the prediction function of a model can be implemented with ONNX operators. A runtime must be chosen, one available on the platform the model is deployed. Discrepancies are checked and finally, the latency is measured. The first step of the model conversion can be easy if there exists a converting library for this framework supporting all the pieces of the model. If it is not the case, the missing parts must be implemented in ONNX. That may be very time consuming. ## What is a converting library? [sklearn-onnx](https://onnx.ai/sklearn-onnx/) converts [scikit-learn](https://scikit-learn.org/stable/) models into ONNX. It rewrites the prediction function of a model, whatever it is, with ONNX operators using the API introduced above. It ensures that the predictions are equal or at least very close to the expected predictions computed with the original model. Machine learning libraries usually have their own design. That's why there exists a specific converting library for each of them. Many of them are listed there: [Converting to ONNX format](https://github.com/onnx/tutorials#converting-to-onnx-format). Here is a short list: - [sklearn-onnx](https://onnx.ai/sklearn-onnx/): converts models from [scikit-learn](https://scikit-learn.org/stable/), - [tensorflow-onnx](https://github.com/onnx/tensorflow-onnx): converts models from [tensorflow](https://www.tensorflow.org/), - [onnxmltools](https://github.com/onnx/onnxmltools): converts models from [lightgbm](https://lightgbm.readthedocs.io/), [xgboost](https://xgboost.readthedocs.io/en/stable/), [pyspark](https://spark.apache.org/docs/latest/api/python/), [libsvm](https://github.com/cjlin1/libsvm) - [torch.onnx](https://pytorch.org/docs/master/onnx.html): converts model from [pytorch](https://pytorch.org/). The main challenge for all these libraries is to keep up the rhythm. They must be updated everytime ONNX or the library they support have a new released version. That means three to five new releases per year. Converting libraries are not compatible among each others. [tensorflow-onnx](https://github.com/onnx/tensorflow-onnx) is dedicated to tensorflow and only tensorflow. The same goes for sklearn-onnx specialized into scikit-learn. One challenge is customization. It is difficult to support custom pieces in a machine learned model. They have to write the specific converter for this piece. Somehow, it is like implementing twice the prediction function. There is one easy case: deep learning frameworks have their own primitives to ensure the same code can be executed on different environments. As long as a custom layer or a subpart is using pieces of pytorch or tensorflow, there is not much to do. It is a different story for scikit-learn. This package does not have its own addition or multiplication, it relies on numpy or scipy. The user must implement its transformer or predictor with ONNX primitives, whether or not it was implemented with numpy. ## Alternatives One alternative for implementing ONNX export capability is to leverage standard protocols such as the [Array API standard](https://data-apis.org/array-api/latest/), which standardizes a common set of array operations. It enables code reuse across libraries like NumPy, JAX, PyTorch, CuPy and more. [ndonnx](https://github.com/Quantco/ndonnx) enables execution with an ONNX backend and instant ONNX export for Array API compliant code. This diminishes the need for dedicated converter library code since the same code used to implement most of a library can reused in ONNX conversion. It also provides a convenient primitive for converter authors looking for a NumPy-like experience when constructing ONNX graphs. ## Opsets ONNX releases packages with version numbers like `major.minor.fix`. Every minor update means the list of operators is different or the signature has changed. It is also associated to an opset, version `1.10` is opset 15, `1.11` will be opset 16. Every ONNX graph should define the opset it follows. Changing this version without updating the operators could make the graph invalid. If the opset is left unspecified, ONNX will consider that the graph is valid for the latest opset. New opsets usually introduce new operators. A same inference function could be implemented differently, usually in a more efficient way. However, the runtime the model is running on may not support newest opsets or at least not in the installed version. That's why every converting library offers the possibility to create an ONNX graph for a specific opset usually called `target_opset`. ONNX language describes simple and complex operators. Changing the opset is similar to upgrading a library. onnx and onnx runtimes must support backward compatibility. ## Other API Examples in previous sections show that onnx API is very verbose. It is also difficult to get a whole picture of a graph by reading the code unless it is a small one. Almost every converting library has implemented a different API to create a graph, usually more simple, less verbose than the API of onnx package. All API automate the addition of initializers, hide the creation of a name of every intermediate result, deal with different version for different opset. ### A class Graph with a method add_node `tensorflow-onnx` implements a class graph. It rewrites tensorflow function with ONNX operator when ONNX does not have a similar function (see [Erf](https://github.com/onnx/tensorflow-onnx/blob/master/tf2onnx/onnx_opset/math.py#L414). sklearn-onnx defines two different API. The first one introduced in that example [Implement a converter](https://onnx.ai/sklearn-onnx/auto_tutorial/plot_jcustom_syntax.html) follows a similar design that tensorflow-onnx follows. The following lines are extracted from the converter of a linear classifier. ``` # initializer coef = scope.get_unique_variable_name('coef') model_coef = np.array( classifier_attrs['coefficients'], dtype=np.float64) model_coef = model_coef.reshape((number_of_classes, -1)).T container.add_initializer( coef, proto_dtype, model_coef.shape, model_coef.ravel().tolist()) intercept = scope.get_unique_variable_name('intercept') model_intercept = np.array( classifier_attrs['intercepts'], dtype=np.float64) model_intercept = model_intercept.reshape((number_of_classes, -1)).T container.add_initializer( intercept, proto_dtype, model_intercept.shape, model_intercept.ravel().tolist()) # add nodes multiplied = scope.get_unique_variable_name('multiplied') container.add_node( 'MatMul', [operator.inputs[0].full_name, coef], multiplied, name=scope.get_unique_operator_name('MatMul')) # [...] argmax_output_name = scope.get_unique_variable_name('label') container.add_node('ArgMax', raw_score_name, argmax_output_name, name=scope.get_unique_operator_name('ArgMax'), axis=1) ``` ### Operator as function The second API shown in [Implement a new converter](https://onnx.ai/sklearn-onnx/auto_tutorial/plot_icustom_converter.html) is more compact and defines every ONNX operator as composable functions. The syntax looks like this for [KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html), less verbose and easier to read. ``` rs = OnnxReduceSumSquare( input_name, axes=[1], keepdims=1, op_version=opv) gemm_out = OnnxMatMul( input_name, (C.T * (-2)).astype(dtype), op_version=opv) z = OnnxAdd(rs, gemm_out, op_version=opv) y2 = OnnxAdd(C2, z, op_version=opv) ll = OnnxArgMin(y2, axis=1, keepdims=0, output_names=out[:1], op_version=opv) y2s = OnnxSqrt(y2, output_names=out[1:], op_version=opv) ``` ## Tricks learned from experience ### Discrepancies ONNX is strongly typed and optimizes for float32, the most common type in deep learning. Libraries in standard machine learning use both float32 and float64. numpy usually cast to the most generic type, float64. It has no significant impact when the prediction function is contiguous. When it is not, the right type must be used. Example [Issues when switching to float](https://onnx.ai/sklearn-onnx/auto_tutorial/plot_ebegin_float_double.html) gives more insights on that topic. Parallelization changes the order of computation. It is usually not significant but it may explain some weird discrepancies. `1 + 1e17 - 1e17 = 0` but `1e17 - 1e17 + 1 = 1`. High order of magnitude are rare but not so rare when a model uses the inverse of a matrix. ### IsolationForest Trick ONNX only implements a {ref}`TreeEnsembleRegressor ` but it does not offer the possibility to retrieve any information about the path the decision followed or statistics to the graph. The trick is to used one forest to predict the leaf index and map this leaf index one or multiple times with the information needed. ```{image} images/iff.png ``` ### Discretization Looking in which interval a feature falls into. That's easy to do with numpy but not so easy to do efficiently with ONNX. The fastest way is to use a TreeEnsembleRegressor, a binary search, which outputs the interval index. That's what this example implements: [Converter for WOE](https://onnx.ai/sklearn-onnx/auto_tutorial/plot_woe_transformer.html). ### Contribute [onnx repository](https://github.com/onnx/onnx) must be forked and cloned. ### Build The windows build requires conda. The following steps might not be up to date. Folder [onnx/.github/workflows](https://github.com/onnx/onnx/tree/main/.github/workflows) contains the latest instructions. **Windows** The build is easier with Anaconda. First: create an environment. It must be done only once. ``` conda create --yes --quiet --name py3.9 python=3.9 conda install -n py3.9 -y -c conda-forge numpy libprotobuf=3.16.0 ``` Then build the package: ```sh git submodule update --init --recursive set ONNX_BUILD_TESTS=1 set ONNX_ML=$(onnx_ml) set CMAKE_ARGS=-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON python -m build --wheel ``` The package can now be installed. **Linux** After cloning the repository, the following instructions can be run: ```sh python -m build --wheel ``` ### Build the markdown documentation The package must be built first (see previous section). ``` set ONNX_BUILD_TESTS=1 set ONNX_ML=$(onnx_ml) set CMAKE_ARGS=-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON python onnx\gen_proto.py -l python onnx\gen_proto.py -l --ml pip install -e . python onnx\backend\test\cmd_tools.py generate-data python onnx\backend\test\stat_coverage.py python onnx\defs\gen_doc.py set ONNX_ML=0 python onnx\defs\gen_doc.py set ONNX_ML=1 ``` ### Update an existing operator All operators are defined in folder [onnx/onnx/defs](https://github.com/onnx/onnx/tree/main/onnx/defs). There are two files in every subfolder, one called `defs.cc` and another one called `old.cc`. - `defs.cc`: contains the most recent definition for every operator - `old.cc`: contains the deprecated version of the operators in previous opset Updating an operator means copying the definition from `defs.cc` to `old.cc` and updating the existing one in `defs.cc`. One file following the pattern `onnx/defs/operator_sets*.h` must be modified. These headers registers the list of existing operators. File [onnx/defs/schema.h](https://github.com/onnx/onnx/blob/main/onnx/defs/schema.h) contains the latest opset version. It must be updated too if one opset was upgraded. File [onnx/version_converter/convert.h](https://github.com/onnx/onnx/blob/main/onnx/version_converter/convert.h) contains rules to apply when converter a node from an opset to the next one. This file may be updated too. The package must be compiled and the documentation must be generated again to automatically update the markdown documentation and it must be included in the PR. Then unit test must be updated. **Summary** - Modify files `defs.cc`, `old.cc`, `onnx/defs/operator_sets*.h`, `onnx/defs/schema.h` - Optional: modify file `onnx/version_converter/convert.h` - Build onnx. - Build the documentation. - Update unit test. 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It shows how it is used with examples in python and finally explains some of challenges faced when moving to ONNX in production. ```{toctree} :maxdepth: 2 concepts python converters ``` onnx-onnx-bca0315/docs/docsgen/source/intro/python.md000066400000000000000000001460261511334557700227500ustar00rootroot00000000000000# ONNX with Python ```{tip} Check out the [ir-py project](https://github.com/onnx/ir-py) for an alternative set of Python APIs for creating and manipulating ONNX models. The ir-py project provides a more modern and ergonomic interface compared to the ONNX Protobuf APIs described here. ``` Next sections highlight the main functions used to build an ONNX graph with the {ref}`Python API ` *onnx* offers. (l-onnx-linear-regression-onnx-api)= ## A simple example: a linear regression The linear regression is the most simple model in machine learning described by the following expression $Y = XA + B$. We can see it as a function of three variables $Y = f(X, A, B)$ decomposed into `y = Add(MatMul(X, A), B)`. That's what we need to represent with ONNX operators. The first thing is to implement a function with {ref}`ONNX operators `. ONNX is strongly typed. Shape and type must be defined for both input and output of the function. That said, we need four functions to build the graph among the {ref}`l-onnx-make-function`: - `make_tensor_value_info`: declares a variable (input or output) given its shape and type - `make_node`: creates a node defined by an operation (an operator type), its inputs and outputs - `make_graph`: a function to create an ONNX graph with the objects created by the two previous functions - `make_model`: a last function which merges the graph and additional metadata All along the creation, we need to give a name to every input, output of every node of the graph. Input and output of the graph are defined by onnx objects, strings are used to refer to intermediate results. This is how it looks like. ```{eval-rst} .. exec_code:: # imports from onnx import TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model # inputs # 'X' is the name, TensorProto.FLOAT the type, [None, None] the shape X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) # outputs, the shape is left undefined Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) # nodes # It creates a node defined by the operator type MatMul, # 'X', 'A' are the inputs of the node, 'XA' the output. node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) # from nodes to graph # the graph is built from the list of nodes, the list of inputs, # the list of outputs and a name. graph = make_graph([node1, node2], # nodes 'lr', # a name [X, A, B], # inputs [Y]) # outputs # onnx graph # there is no metadata in this case. onnx_model = make_model(graph) # Let's check the model is consistent, # this function is described in section # Checker and Shape Inference. check_model(onnx_model) # the work is done, let's display it... print(onnx_model) ``` ```{image} images/dot_linreg.png ``` An empty shape (`None`) means any shape, a shape defined as `[None, None]` tells this object is a tensor with two dimensions without any further precision. The ONNX graph can also be inspected by looking into the fields of each object of the graph. ```{eval-rst} .. exec_code:: from onnx import TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model def shape2tuple(shape): return tuple(getattr(d, 'dim_value', 0) for d in shape.dim) X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) graph = make_graph([node1, node2], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) # the list of inputs print('** inputs **') print(onnx_model.graph.input) # in a more nicely format print('** inputs **') for obj in onnx_model.graph.input: print("name=%r dtype=%r shape=%r" % ( obj.name, obj.type.tensor_type.elem_type, shape2tuple(obj.type.tensor_type.shape))) # the list of outputs print('** outputs **') print(onnx_model.graph.output) # in a more nicely format print('** outputs **') for obj in onnx_model.graph.output: print("name=%r dtype=%r shape=%r" % ( obj.name, obj.type.tensor_type.elem_type, shape2tuple(obj.type.tensor_type.shape))) # the list of nodes print('** nodes **') print(onnx_model.graph.node) # in a more nicely format print('** nodes **') for node in onnx_model.graph.node: print("name=%r type=%r input=%r output=%r" % ( node.name, node.op_type, node.input, node.output)) ``` The tensor type is an integer value (=1 for `FLOAT`). The helper function {func}`onnx.helper.tensor_dtype_to_np_dtype` converts the integer to its corresponding numpy data type (float32 for 1). ```{eval-rst} .. exec_code:: from onnx import TensorProto from onnx.helper import tensor_dtype_to_np_dtype, tensor_dtype_to_string np_dtype = tensor_dtype_to_np_dtype(TensorProto.FLOAT) print(f"The converted numpy dtype for {tensor_dtype_to_string(TensorProto.FLOAT)} is {np_dtype}.") ``` ## Serialization ONNX is built on the top of protobuf. It adds the necessary definitions to describe a machine learning model and most of the time, ONNX is used to serialize or deserialize a model. First section addresses this need. Second section introduces the serialization and deserialization of data such as tensors, sparse tensors... ### Model Serialization The model needs to be saved to be deployed. ONNX is based on protobuf. It minimizes the space needed to save the graph on disk. Every object (see {ref}`l-onnx-classes`) in onnx can be serialized with method `SerializeToString`. That's the case for the whole model. ```{eval-rst} .. exec_code:: from onnx import TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model def shape2tuple(shape): return tuple(getattr(d, 'dim_value', 0) for d in shape.dim) X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) graph = make_graph([node1, node2], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) # The serialization with open("linear_regression.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) # display print(onnx_model) ``` The graph can be restored with function `load`: ```{eval-rst} .. exec_code:: from onnx import load with open("linear_regression.onnx", "rb") as f: onnx_model = load(f) # display print(onnx_model) ``` It looks exactly the same. Any model can be serialized this way unless they are bigger than 2 Gb. protobuf is limited to size smaller than this threshold. Next sections will show how to overcome that limit. ### Data Serialization The serialization of tensors usually happens like the following: ```{eval-rst} .. exec_code:: import numpy from onnx.numpy_helper import from_array numpy_tensor = numpy.array([0, 1, 4, 5, 3], dtype=numpy.float32) print(type(numpy_tensor)) onnx_tensor = from_array(numpy_tensor) print(type(onnx_tensor)) serialized_tensor = onnx_tensor.SerializeToString() print(type(serialized_tensor)) with open("saved_tensor.pb", "wb") as f: f.write(serialized_tensor) ``` And the deserialization like: ```{eval-rst} .. exec_code:: from onnx import TensorProto from onnx.numpy_helper import to_array with open("saved_tensor.pb", "rb") as f: serialized_tensor = f.read() print(type(serialized_tensor)) onnx_tensor = TensorProto() onnx_tensor.ParseFromString(serialized_tensor) print(type(onnx_tensor)) numpy_tensor = to_array(onnx_tensor) print(numpy_tensor) ``` The same schema can be used for but not limited to {ref}`l-tensorproto`: ```{eval-rst} .. exec_code:: import onnx import pprint pprint.pprint([p for p in dir(onnx) if p.endswith('Proto') and p[0] != '_']) ``` This code can be simplified with function *load_tensor_from_string* (see {ref}`l-onnx-load-data`). ```{eval-rst} .. exec_code:: from onnx import load_tensor_from_string with open("saved_tensor.pb", "rb") as f: serialized = f.read() proto = load_tensor_from_string(serialized) print(type(proto)) ``` (l-onnx-linear-regression-onnx-api-init)= ## Initializer, default value The previous model assumed the coefficients of the linear regression were also input of the model. That's not very convenient. They should be part of the model itself as constant or **initializer** to follow onnx semantic. Next example modifies the previous one to change inputs `A` and `B` into initializers. The package implements two functions to convert from numpy into onnx and the other way around (see {ref}`l-numpy-helper-onnx-array`). - `onnx.numpy_helper.to_array`: converts from onnx to numpy - `onnx.numpy_helper.from_array`: converts from numpy to onnx ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model # initializers value = numpy.array([0.5, -0.6], dtype=numpy.float32) A = numpy_helper.from_array(value, name='A') value = numpy.array([0.4], dtype=numpy.float32) C = numpy_helper.from_array(value, name='C') # the part which does not change X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['AX']) node2 = make_node('Add', ['AX', 'C'], ['Y']) graph = make_graph([node1, node2], 'lr', [X], [Y], [A, C]) onnx_model = make_model(graph) check_model(onnx_model) print(onnx_model) ``` ```{image} images/dot_linreg2.png ``` Again, it is possible to go through the onnx structure to check how the initializers look like. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model # initializers value = numpy.array([0.5, -0.6], dtype=numpy.float32) A = numpy_helper.from_array(value, name='A') value = numpy.array([0.4], dtype=numpy.float32) C = numpy_helper.from_array(value, name='C') # the part which does not change X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['AX']) node2 = make_node('Add', ['AX', 'C'], ['Y']) graph = make_graph([node1, node2], 'lr', [X], [Y], [A, C]) onnx_model = make_model(graph) check_model(onnx_model) print('** initializer **') for init in onnx_model.graph.initializer: print(init) ``` The type is defined as integer as well with the same meaning. In this second example, there is only one input left. Input `A` and `B` were removed. They could be kept. In that case, they are optional: every initializer sharing the same name as input is considered as a default value. It replaces the input if this one is not given. ## Attributes Some operators need attributes such as {ref}`l-onnx-doc-Transpose` operator. Let's build the graph for expression $y = XA' + B$ or `y = Add(MatMul(X, Transpose(A)) + B)`. Transpose needs an attribute defining the permutation of axes: `perm=[1, 0]`. It is added as a named attribute in function `make_node`. ```{eval-rst} .. exec_code:: from onnx import TensorProto from onnx.helper import ( make_model, make_node, make_graph, make_tensor_value_info) from onnx.checker import check_model # unchanged X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) # added node_transpose = make_node('Transpose', ['A'], ['tA'], perm=[1, 0]) # unchanged except A is replaced by tA node1 = make_node('MatMul', ['X', 'tA'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) # node_transpose is added to the list graph = make_graph([node_transpose, node1, node2], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) # the work is done, let's display it... print(onnx_model) ``` ```{image} images/dot_att.png ``` The whole list of *make* functions is the following. Many of them are described in section {ref}`l-onnx-make-function`. ```{eval-rst} .. exec_code:: import onnx import pprint pprint.pprint([k for k in dir(onnx.helper) if k.startswith('make')]) ``` ## Opset and metadata Let's load the ONNX file previously created and check what kind of metadata it has. ```{eval-rst} .. exec_code:: from onnx import load with open("linear_regression.onnx", "rb") as f: onnx_model = load(f) for field in ['doc_string', 'domain', 'functions', 'ir_version', 'metadata_props', 'model_version', 'opset_import', 'producer_name', 'producer_version', 'training_info']: print(field, getattr(onnx_model, field)) ``` Most of them are empty because it was not filled when the ONNX graph was created. Two of them have a value: ```{eval-rst} .. exec_code:: from onnx import load with open("linear_regression.onnx", "rb") as f: onnx_model = load(f) print("ir_version:", onnx_model.ir_version) for opset in onnx_model.opset_import: print("opset domain=%r version=%r" % (opset.domain, opset.version)) ``` `IR` defined the version of ONNX language. Opset defines the version of operators being used. Without any precision, ONNX uses the latest version available coming from the installed package. Another one can be used. ```{eval-rst} .. exec_code:: from onnx import load with open("linear_regression.onnx", "rb") as f: onnx_model = load(f) del onnx_model.opset_import[:] opset = onnx_model.opset_import.add() opset.domain = '' opset.version = 14 for opset in onnx_model.opset_import: print("opset domain=%r version=%r" % (opset.domain, opset.version)) ``` Any opset can be used as long as all operators are defined the way ONNX specifies it. Version 5 of operator *Reshape* defines the shape as an input and not as an attribute like in version 1. The opset tells which specifications is followed while describing the graph. The other metadata can be used to store any information, to store information about the way the model was generated, a way to distinguish a model from another one with a version number. ```{eval-rst} .. exec_code:: from onnx import load, helper with open("linear_regression.onnx", "rb") as f: onnx_model = load(f) onnx_model.model_version = 15 onnx_model.producer_name = "something" onnx_model.producer_version = "some other thing" onnx_model.doc_string = "documentation about this model" prop = onnx_model.metadata_props data = dict(key1="value1", key2="value2") helper.set_model_props(onnx_model, data) print(onnx_model) ``` Field `training_info` can be used to store additional graphs. See [training_tool_test.py](https://github.com/onnx/onnx/blob/main/onnx/test/training_tool_test.py) to see how it works. ## Subgraph: test and loops They are usually grouped in a category called *control flow*. It is usually better to avoid them as they are not as efficient as the matrix operation are much faster and optimized. ### If A test can be implemented with operator {ref}`l-onnx-doc-If`. It executes one subgraph or another depending on one boolean. This is not used very often as a function usually needs the result of many comparisons in a batch. The following example computes the sum of all floats in a matrix based on the sign, returns 1 or -1. ```{eval-rst} .. exec_code:: import numpy import onnx from onnx.helper import ( make_node, make_graph, make_model, make_tensor_value_info) from onnx.numpy_helper import from_array from onnx.checker import check_model from onnxruntime import InferenceSession # initializers value = numpy.array([0], dtype=numpy.float32) zero = from_array(value, name='zero') # Same as before, X is the input, Y is the output. X = make_tensor_value_info('X', onnx.TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', onnx.TensorProto.FLOAT, [None]) # The node building the condition. The first one # sum over all axes. rsum = make_node('ReduceSum', ['X'], ['rsum']) # The second compares the result to 0. cond = make_node('Greater', ['rsum', 'zero'], ['cond']) # Builds the graph is the condition is True. # Input for then then_out = make_tensor_value_info( 'then_out', onnx.TensorProto.FLOAT, None) # The constant to return. then_cst = from_array(numpy.array([1]).astype(numpy.float32)) # The only node. then_const_node = make_node( 'Constant', inputs=[], outputs=['then_out'], value=then_cst, name='cst1') # And the graph wrapping these elements. then_body = make_graph( [then_const_node], 'then_body', [], [then_out]) # Same process for the else branch. else_out = make_tensor_value_info( 'else_out', onnx.TensorProto.FLOAT, [5]) else_cst = from_array(numpy.array([-1]).astype(numpy.float32)) else_const_node = make_node( 'Constant', inputs=[], outputs=['else_out'], value=else_cst, name='cst2') else_body = make_graph( [else_const_node], 'else_body', [], [else_out]) # Finally the node If taking both graphs as attributes. if_node = onnx.helper.make_node( 'If', ['cond'], ['Y'], then_branch=then_body, else_branch=else_body) # The final graph. graph = make_graph([rsum, cond, if_node], 'if', [X], [Y], [zero]) onnx_model = make_model(graph) check_model(onnx_model) # Let's freeze the opset. del onnx_model.opset_import[:] opset = onnx_model.opset_import.add() opset.domain = '' opset.version = 15 onnx_model.ir_version = 8 # Save. with open("onnx_if_sign.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) # Let's see the output. sess = InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) x = numpy.ones((3, 2), dtype=numpy.float32) res = sess.run(None, {'X': x}) # It works. print("result", res) print() # Some display. print(onnx_model) ``` The whole is easier to visualize with the following image. ```{image} images/dot_if_py.png ``` Both else and then branches are very simple. Node *If* could even be replaced with a node *Where* and that would be faster. It becomes interesting when both branches are bigger and skipping one is more efficient. ### Scan {ref}`l-onnx-doc-Scan` seems quite complex when reading the specifications. It is useful to loop over one dimension of a tensor and store the results in a preallocated tensor. The following example implements a classic nearest neighbors for a regression problem. The first step consists in computing the pairwise distances between the input features *X* and the training set *W*: $dist(X,W) = (M_{ij}) = (\norm{X_i - W_j}^2)_{ij}$. It is followed by an operator {ref}`l-onnx-doc-TopK` which extracts the *k* nearest neighbors. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info) from onnx.checker import check_model # subgraph initializers = [] nodes = [] inputs = [] outputs = [] value = make_tensor_value_info('next_in', 1, [None, 4]) inputs.append(value) value = make_tensor_value_info('next', 1, [None]) inputs.append(value) value = make_tensor_value_info('next_out', 1, [None, None]) outputs.append(value) value = make_tensor_value_info('scan_out', 1, [None]) outputs.append(value) node = make_node( 'Identity', ['next_in'], ['next_out'], name='cdistd_17_Identity', domain='') nodes.append(node) node = make_node( 'Sub', ['next_in', 'next'], ['cdistdf_17_C0'], name='cdistdf_17_Sub', domain='') nodes.append(node) node = make_node( 'ReduceSumSquare', ['cdistdf_17_C0'], ['cdistdf_17_reduced0'], name='cdistdf_17_ReduceSumSquare', axes=[1], keepdims=0, domain='') nodes.append(node) node = make_node( 'Identity', ['cdistdf_17_reduced0'], ['scan_out'], name='cdistdf_17_Identity', domain='') nodes.append(node) graph = make_graph(nodes, 'OnnxIdentity', inputs, outputs, initializers) # main graph initializers = [] nodes = [] inputs = [] outputs = [] opsets = {'': 15, 'ai.onnx.ml': 15} target_opset = 15 # subgraphs # initializers list_value = [23.29599822460675, -120.86516699239603, -144.70495899914215, -260.08772982740413, 154.65272105889147, -122.23295157108991, 247.45232560871727, -182.83789715805776, -132.92727431421793, 147.48710175784703, 88.27761768038069, -14.87785569894749, 111.71487894705504, 301.0518319089629, -29.64235742280055, -113.78493504731911, -204.41218591022718, 112.26561056133608, 66.04032954135549, -229.5428380626701, -33.549262642481615, -140.95737409864623, -87.8145187836131, -90.61397011283958, 57.185488100413366, 56.864151796743855, 77.09054590340892, -187.72501631246712, -42.779503579806025, -21.642642730674076, -44.58517761667535, 78.56025104939847, -23.92423223842056, 234.9166231927213, -73.73512816431007, -10.150864499514297, -70.37105466673813, 65.5755688281476, 108.68676290979731, -78.36748960443065] value = numpy.array(list_value, dtype=numpy.float64).reshape((2, 20)) tensor = numpy_helper.from_array( value, name='knny_ArrayFeatureExtractorcst') initializers.append(tensor) list_value = [1.1394007205963135, -0.6848101019859314, -1.234825849533081, 0.4023416340351105, 0.17742614448070526, 0.46278226375579834, -0.4017809331417084, -1.630198359489441, -0.5096521973609924, 0.7774903774261475, -0.4380742907524109, -1.2527953386306763, -1.0485529899597168, 1.950775384902954, -1.420017957687378, -1.7062702178955078, 1.8675580024719238, -0.15135720372200012, -0.9772778749465942, 0.9500884413719177, -2.5529897212982178, -0.7421650290489197, 0.653618574142456, 0.8644362092018127, 1.5327792167663574, 0.37816253304481506, 1.4693588018417358, 0.154947429895401, -0.6724604368209839, -1.7262825965881348, -0.35955315828323364, -0.8131462931632996, -0.8707971572875977, 0.056165341287851334, -0.5788496732711792, -0.3115525245666504, 1.2302906513214111, -0.302302747964859, 1.202379822731018, -0.38732680678367615, 2.269754648208618, -0.18718385696411133, -1.4543657302856445, 0.04575851559638977, -0.9072983860969543, 0.12898291647434235, 0.05194539576768875, 0.7290905714035034, 1.4940791130065918, -0.8540957570075989, -0.2051582634449005, 0.3130677044391632, 1.764052391052246, 2.2408931255340576, 0.40015721321105957, 0.978738009929657, 0.06651721894741058, -0.3627411723136902, 0.30247190594673157, -0.6343221068382263, -0.5108051300048828, 0.4283318817615509, -1.18063223361969, -0.02818222902715206, -1.6138978004455566, 0.38690251111984253, -0.21274028718471527, -0.8954665660858154, 0.7610377073287964, 0.3336743414402008, 0.12167501449584961, 0.44386324286460876, -0.10321885347366333, 1.4542734622955322, 0.4105985164642334, 0.14404356479644775, -0.8877857327461243, 0.15634897351264954, -1.980796456336975, -0.34791216254234314] value = numpy.array(list_value, dtype=numpy.float32).reshape((20, 4)) tensor = numpy_helper.from_array(value, name='Sc_Scancst') initializers.append(tensor) value = numpy.array([2], dtype=numpy.int64) tensor = numpy_helper.from_array(value, name='To_TopKcst') initializers.append(tensor) value = numpy.array([2, -1, 2], dtype=numpy.int64) tensor = numpy_helper.from_array(value, name='knny_Reshapecst') initializers.append(tensor) # inputs value = make_tensor_value_info('input', 1, [None, 4]) inputs.append(value) # outputs value = make_tensor_value_info('variable', 1, [None, 2]) outputs.append(value) # nodes node = make_node( 'Scan', ['input', 'Sc_Scancst'], ['UU032UU', 'UU033UU'], name='Sc_Scan', body=graph, num_scan_inputs=1, domain='') nodes.append(node) node = make_node( 'Transpose', ['UU033UU'], ['Tr_transposed0'], name='Tr_Transpose', perm=[1, 0], domain='') nodes.append(node) node = make_node( 'Sqrt', ['Tr_transposed0'], ['Sq_Y0'], name='Sq_Sqrt', domain='') nodes.append(node) node = make_node( 'TopK', ['Sq_Y0', 'To_TopKcst'], ['To_Values0', 'To_Indices1'], name='To_TopK', largest=0, sorted=1, domain='') nodes.append(node) node = make_node( 'Flatten', ['To_Indices1'], ['knny_output0'], name='knny_Flatten', domain='') nodes.append(node) node = make_node( 'ArrayFeatureExtractor', ['knny_ArrayFeatureExtractorcst', 'knny_output0'], ['knny_Z0'], name='knny_ArrayFeatureExtractor', domain='ai.onnx.ml') nodes.append(node) node = make_node( 'Reshape', ['knny_Z0', 'knny_Reshapecst'], ['knny_reshaped0'], name='knny_Reshape', allowzero=0, domain='') nodes.append(node) node = make_node( 'Transpose', ['knny_reshaped0'], ['knny_transposed0'], name='knny_Transpose', perm=[1, 0, 2], domain='') nodes.append(node) node = make_node( 'Cast', ['knny_transposed0'], ['Ca_output0'], name='Ca_Cast', to=TensorProto.FLOAT, domain='') nodes.append(node) node = make_node( 'ReduceMean', ['Ca_output0'], ['variable'], name='Re_ReduceMean', axes=[2], keepdims=0, domain='') nodes.append(node) # graph graph = make_graph(nodes, 'KNN regressor', inputs, outputs, initializers) # model onnx_model = make_model(graph) onnx_model.ir_version = 8 onnx_model.producer_name = 'skl2onnx' onnx_model.producer_version = '' onnx_model.domain = 'ai.onnx' onnx_model.model_version = 0 onnx_model.doc_string = '' set_model_props(onnx_model, {}) # opsets del onnx_model.opset_import[:] for dom, value in opsets.items(): op_set = onnx_model.opset_import.add() op_set.domain = dom op_set.version = value check_model(onnx_model) with open("knnr.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) print(onnx_model) ``` Visually it looks like the following: ```{image} images/dot_scan_py.png ``` The subgraph is executed by operator {ref}`l-onnx-doc-Scan`. In this case, there is one *scan* input meaning the operator only builds one output. ``` node = make_node( 'Scan', ['X1', 'X2'], ['Y1', 'Y2'], name='Sc_Scan', body=graph, num_scan_inputs=1, domain='') ``` At the first iteration, the subgraph gets *X1* and the first row of *X2*. The graph produces two outputs. The first one replaces *X1* in the next iteration, the second one is store in a container to form *Y2*. At the second iteration, second input of the subgraph is the second row of *X2*. Here is a short summary. Green is the first iteration, blue the second. ```{image} images/scanop.png :width: 400 ``` ## Functions As mentioned in previous chapter, functions can be used to shorten the code to build the model and offer more possibilities to the runtime running predictions to be faster if there exists a specific implementation of this function. If it is not the case, the runtime can still use the default implementation based on existing operators. Function `make_function` is used to define a function. It works like a graph with less types. It is more like a template. This API may evolve. It does not include initializers either. ### A function with no attribute That's the more simple case. Every input of the function is a dynamic object known at execution time. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info, make_opsetid, make_function) from onnx.checker import check_model new_domain = 'custom' opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)] # Let's define a function for a linear regression node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) linear_regression = make_function( new_domain, # domain name 'LinearRegression', # function name ['X', 'A', 'B'], # input names ['Y'], # output names [node1, node2], # nodes opset_imports, # opsets []) # attribute names # Let's use it in a graph. X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) graph = make_graph( [make_node('LinearRegression', ['X', 'A', 'B'], ['Y1'], domain=new_domain), make_node('Abs', ['Y1'], ['Y'])], 'example', [X, A, B], [Y]) onnx_model = make_model( graph, opset_imports=opset_imports, functions=[linear_regression]) # functions to add) check_model(onnx_model) # the work is done, let's display it... print(onnx_model) ``` ### A function with attributes ```{index} ref_attr_name ``` The following functions are equivalent to the previous one except one input, *B*, was converted into an argument named *bias*. The code is almost the same except the bias is now a constant. Inside the function definition, a node *Constant* is created to insert the argument as a result. It is linked to the argument with the attribute `ref_attr_name`. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto, AttributeProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info, make_opsetid, make_function) from onnx.checker import check_model new_domain = 'custom' opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)] # Let's define a function for a linear regression # The first step consists in creating a constant # equal to the input parameter of the function. cst = make_node('Constant', [], ['B']) att = AttributeProto() att.name = "value" # This line indicates the value comes from the argument # named 'bias' the function is given. att.ref_attr_name = "bias" att.type = AttributeProto.TENSOR cst.attribute.append(att) node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) linear_regression = make_function( new_domain, # domain name 'LinearRegression', # function name ['X', 'A'], # input names ['Y'], # output names [cst, node1, node2], # nodes opset_imports, # opsets ["bias"]) # attribute names # Let's use it in a graph. X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) graph = make_graph( [make_node('LinearRegression', ['X', 'A'], ['Y1'], domain=new_domain, # bias is now an argument of the function and is defined as a tensor bias=make_tensor('former_B', TensorProto.FLOAT, [1], [0.67])), make_node('Abs', ['Y1'], ['Y'])], 'example', [X, A], [Y]) onnx_model = make_model( graph, opset_imports=opset_imports, functions=[linear_regression]) # functions to add) check_model(onnx_model) # the work is done, let's display it... print(onnx_model) ``` ## Parsing Module onnx provides a faster way to define a graph and is lot easier to read. That's easy to use when the graph is built in a single function, less easy when the graph is built from many different functions converting each piece of a machine learning pipeline. ``` import onnx.parser from onnx.checker import check_model input = ''' < ir_version: 8, opset_import: [ "" : 15] > agraph (float[I,J] X, float[I] A, float[I] B) => (float[I] Y) { XA = MatMul(X, A) Y = Add(XA, B) } ''' onnx_model = onnx.parser.parse_model(input) check_model(onnx_model) print(onnx_model) ``` ``` ir_version: 8 graph { node { input: "X" input: "A" output: "XA" op_type: "MatMul" domain: "" } node { input: "XA" input: "B" output: "Y" op_type: "Add" domain: "" } name: "agraph" input { name: "X" type { tensor_type { elem_type: 1 shape { dim { dim_param: "I" } dim { dim_param: "J" } } } } } input { name: "A" type { tensor_type { elem_type: 1 shape { dim { dim_param: "I" } } } } } input { name: "B" type { tensor_type { elem_type: 1 shape { dim { dim_param: "I" } } } } } output { name: "Y" type { tensor_type { elem_type: 1 shape { dim { dim_param: "I" } } } } } } opset_import { domain: "" version: 15 } ``` This way is used to create small models but it is rarely used in converting libraries. ## Checker and Shape Inference onnx provides a function to check the model is valid. It checks input type or shapes whenever it can detect inconsistency. The following example adds two matrices of different types which is not allowed. ```{eval-rst} .. exec_code:: import onnx.parser import onnx.checker input = ''' < ir_version: 8, opset_import: [ "" : 15] > agraph (float[I,4] X, float[4,2] A, int[4] B) => (float[I] Y) { XA = MatMul(X, A) Y = Add(XA, B) } ''' try: onnx_model = onnx.parser.parse_model(input) onnx.checker.check_model(onnx_model) except Exception as e: print(e) ``` `check_model` raises an error due to that inconsistency. This work for all operators defined in the main domain or the ML domain. It remains silent for any custom operator not defined in any specification. Shape inference serves one purpose: estimate the shape and the type of intermediate results. If known, the runtime can estimate the memory consumption beforehand and optimize the computation. It can fuse some operators, it can do the computation inplace... ```{eval-rst} .. exec_code:: import onnx.parser from onnx import helper, shape_inference input = ''' < ir_version: 8, opset_import: [ "" : 15] > agraph (float[I,4] X, float[4,2] A, float[4] B) => (float[I] Y) { XA = MatMul(X, A) Y = Add(XA, B) } ''' onnx_model = onnx.parser.parse_model(input) inferred_model = shape_inference.infer_shapes(onnx_model) print(inferred_model) ``` There is a new attribute `value_info` which stores the inferred shapes. Letter `I` in `dim_param: "I"` can be seen as a variable. It depends on the inputs but the function is able to tell which intermediate result will share the same dimension. Shape inference does not work all the time. For example, a Reshape operator. Shape inference only works if the shape is constant. If not constant, the shape cannot be easily inferred unless the following nodes expect specific shape. ## Evaluation and Runtime The ONNX standard allows frameworks to export trained models in ONNX format, and enables inference using any backend that supports the ONNX format. *onnxruntime* is one efficient option. It is available in many platforms. It is optimized for fast inference. Its coverage can be tracked on [ONNX Backend Dashboard](https://onnx.ai/backend-scoreboard/). *onnx* implements a python runtime useful to help understand a model. It is not intended to be used for production and performance is not a goal. ### Evaluation of a linear regression Full API is described at {ref}`l-reference-implementation`. It takes a model (a *ModelProto*, a filename, ...). Method `run` returns the outputs for a given set of inputs specified in a dictionary. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info) from onnx.checker import check_model from onnx.reference import ReferenceEvaluator X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) graph = make_graph([node1, node2], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) sess = ReferenceEvaluator(onnx_model) x = numpy.random.randn(4, 2).astype(numpy.float32) a = numpy.random.randn(2, 1).astype(numpy.float32) b = numpy.random.randn(1, 1).astype(numpy.float32) feeds = {'X': x, 'A': a, 'B': b} print(sess.run(None, feeds)) ``` ### Evaluation of a node The evaluator can also evaluate a simple node to check how an operator behaves on a specific input. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import make_node from onnx.reference import ReferenceEvaluator node = make_node('EyeLike', ['X'], ['Y']) sess = ReferenceEvaluator(node) x = numpy.random.randn(4, 2).astype(numpy.float32) feeds = {'X': x} print(sess.run(None, feeds)) ``` Similar code would also work on *GraphProto* or *FunctionProto*. ### Evaluation Step by Step A converting library takes an existing model trained with a machine learning framework (*pytorch*, *scikit-learn*, ...) and converts the model into an ONNX graph. Complex models usually do not work on the first try and seeing intermediate results may help to find the part incorrectly converted. Parameter `verbose` displays information about intermediate results. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info) from onnx.checker import check_model from onnx.reference import ReferenceEvaluator X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'B'], ['Y']) graph = make_graph([node1, node2], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) for verbose in [1, 2, 3, 4]: print() print(f"------ verbose={verbose}") print() sess = ReferenceEvaluator(onnx_model, verbose=verbose) x = numpy.random.randn(4, 2).astype(numpy.float32) a = numpy.random.randn(2, 1).astype(numpy.float32) b = numpy.random.randn(1, 1).astype(numpy.float32) feeds = {'X': x, 'A': a, 'B': b} print(sess.run(None, feeds)) ``` ### Evaluate a custom node The following example still implements a linear regression but adds the identity matrix to *A*: $Y = X(A + I) + B$. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info) from onnx.checker import check_model from onnx.reference import ReferenceEvaluator X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node0 = make_node('EyeLike', ['A'], ['Eye']) node1 = make_node('Add', ['A', 'Eye'], ['A1']) node2 = make_node('MatMul', ['X', 'A1'], ['XA1']) node3 = make_node('Add', ['XA1', 'B'], ['Y']) graph = make_graph([node0, node1, node2, node3], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph) check_model(onnx_model) with open("linear_regression.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) sess = ReferenceEvaluator(onnx_model, verbose=2) x = numpy.random.randn(4, 2).astype(numpy.float32) a = numpy.random.randn(2, 2).astype(numpy.float32) / 10 b = numpy.random.randn(1, 2).astype(numpy.float32) feeds = {'X': x, 'A': a, 'B': b} print(sess.run(None, feeds)) ``` What if we combine operators *EyeLike* and *Add* into *AddEyeLike* to make it more efficient. Next example replaces these two operators by a single one from domain `'optimized'`. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info, make_opsetid) from onnx.checker import check_model X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None]) A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None]) B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None]) Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None]) node01 = make_node('AddEyeLike', ['A'], ['A1'], domain='optimized') node2 = make_node('MatMul', ['X', 'A1'], ['XA1']) node3 = make_node('Add', ['XA1', 'B'], ['Y']) graph = make_graph([node01, node2, node3], 'lr', [X, A, B], [Y]) onnx_model = make_model(graph, opset_imports=[ make_opsetid('', 18), make_opsetid('optimized', 1) ]) check_model(onnx_model) with open("linear_regression_improved.onnx", "wb") as f: f.write(onnx_model.SerializeToString()) ``` We need to evaluate this model is equivalent to the first one. This requires an implementation for this particular node. ```{eval-rst} .. exec_code:: import numpy from onnx.reference import ReferenceEvaluator from onnx.reference.op_run import OpRun class AddEyeLike(OpRun): op_domain = "optimized" def _run(self, X, alpha=1.): assert len(X.shape) == 2 assert X.shape[0] == X.shape[1] X = X.copy() ind = numpy.diag_indices(X.shape[0]) X[ind] += alpha return (X,) sess = ReferenceEvaluator("linear_regression_improved.onnx", verbose=2, new_ops=[AddEyeLike]) x = numpy.random.randn(4, 2).astype(numpy.float32) a = numpy.random.randn(2, 2).astype(numpy.float32) / 10 b = numpy.random.randn(1, 2).astype(numpy.float32) feeds = {'X': x, 'A': a, 'B': b} print(sess.run(None, feeds)) # Let's check with the previous model. sess0 = ReferenceEvaluator("linear_regression.onnx",) sess1 = ReferenceEvaluator("linear_regression_improved.onnx", new_ops=[AddEyeLike]) y0 = sess0.run(None, feeds)[0] y1 = sess1.run(None, feeds)[0] print(y0) print(y1) print(f"difference: {numpy.abs(y0 - y1).max()}") ``` Predictions are the same. Let's compare the performance on a matrix big enough to see a significant difference. ```{eval-rst} .. exec_code:: import timeit import numpy from onnx.reference import ReferenceEvaluator from onnx.reference.op_run import OpRun class AddEyeLike(OpRun): op_domain = "optimized" def _run(self, X, alpha=1.): assert len(X.shape) == 2 assert X.shape[0] == X.shape[1] X = X.copy() ind = numpy.diag_indices(X.shape[0]) X[ind] += alpha return (X,) sess = ReferenceEvaluator("linear_regression_improved.onnx", verbose=2, new_ops=[AddEyeLike]) x = numpy.random.randn(4, 100).astype(numpy.float32) a = numpy.random.randn(100, 100).astype(numpy.float32) / 10 b = numpy.random.randn(1, 100).astype(numpy.float32) feeds = {'X': x, 'A': a, 'B': b} sess0 = ReferenceEvaluator("linear_regression.onnx") sess1 = ReferenceEvaluator("linear_regression_improved.onnx", new_ops=[AddEyeLike]) y0 = sess0.run(None, feeds)[0] y1 = sess1.run(None, feeds)[0] print(f"difference: {numpy.abs(y0 - y1).max()}") print(f"time with EyeLike+Add: {timeit.timeit(lambda: sess0.run(None, feeds), number=1000)}") print(f"time with AddEyeLike: {timeit.timeit(lambda: sess1.run(None, feeds), number=1000)}") ``` It seems worth adding an optimized node in this case. This kind of optimization is usually called *fusion*. Two consecutive operators are fused into an optimized version of both. Production usually relies on *onnxruntime* but since the optimization uses basic matrix operation, it should bring the same performance gain on any other runtime. ## Implementation details ### Python and C++ onnx relies on protobuf to define its type. You would assume that a python object is just a wrapper around a C pointer on the internal structure. Therefore, it should be possible to access internal data from a function receiving a python object of type `ModelProto`. But it is not. According to [Protobuf 4, changes](https://developers.google.com/protocol-buffers/docs/news/2022-05-06), this is no longer possible after version 4 and it is safer to assume the only way to get a hold on the content is to serialize the model into bytes, give it to the C function, then deserialize it. Functions like `check_model` or `shape_inference` are calling `SerializeToString` then `ParseFromString` before checking the model with a C code. ### Attributes and inputs There is a clear distinction between the two. Inputs are dynamic and may change at every execution. Attributes never changes and an optimizer can improve the execution graph assuming it never changes. Therefore, it is impossible to turn an input into an attribute. And the operator *Constant* is the only operator changing an attribute into an input. ### Shape or no shape onnx usually expects a shape for every input or output assuming the rank (or the number of dimensions) is known. What if we need to create a valid graph for every dimension? This case is still puzzling. ```{eval-rst} .. exec_code:: import numpy from onnx import numpy_helper, TensorProto, FunctionProto from onnx.helper import ( make_model, make_node, set_model_props, make_tensor, make_graph, make_tensor_value_info, make_opsetid, make_function) from onnx.checker import check_model from onnxruntime import InferenceSession def create_model(shapes): new_domain = 'custom' opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)] node1 = make_node('MatMul', ['X', 'A'], ['XA']) node2 = make_node('Add', ['XA', 'A'], ['Y']) X = make_tensor_value_info('X', TensorProto.FLOAT, shapes['X']) A = make_tensor_value_info('A', TensorProto.FLOAT, shapes['A']) Y = make_tensor_value_info('Y', TensorProto.FLOAT, shapes['Y']) graph = make_graph([node1, node2], 'example', [X, A], [Y]) onnx_model = make_model(graph, opset_imports=opset_imports) # Let models runnable by onnxruntime with a released ir_version onnx_model.ir_version = 8 return onnx_model print("----------- case 1: 2D x 2D -> 2D") onnx_model = create_model({'X': [None, None], 'A': [None, None], 'Y': [None, None]}) check_model(onnx_model) sess = InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) res = sess.run(None, { 'X': numpy.random.randn(2, 2).astype(numpy.float32), 'A': numpy.random.randn(2, 2).astype(numpy.float32)}) print(res) print("----------- case 2: 2D x 1D -> 1D") onnx_model = create_model({'X': [None, None], 'A': [None], 'Y': [None]}) check_model(onnx_model) sess = InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) res = sess.run(None, { 'X': numpy.random.randn(2, 2).astype(numpy.float32), 'A': numpy.random.randn(2).astype(numpy.float32)}) print(res) print("----------- case 3: 2D x 0D -> 0D") onnx_model = create_model({'X': [None, None], 'A': [], 'Y': []}) check_model(onnx_model) try: InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) except Exception as e: print(e) print("----------- case 4: 2D x None -> None") onnx_model = create_model({'X': [None, None], 'A': None, 'Y': None}) try: check_model(onnx_model) except Exception as e: print(type(e), e) sess = InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) res = sess.run(None, { 'X': numpy.random.randn(2, 2).astype(numpy.float32), 'A': numpy.random.randn(2).astype(numpy.float32)}) print(res) print("----------- end") ``` onnx-onnx-bca0315/docs/docsgen/source/onnx-favicon.png000066400000000000000000000161331511334557700230600ustar00rootroot00000000000000‰PNG  IHDRÃÃ?ĨĒtEXtSoftwareAdobe ImageReadyqÉe<ũIDATxÚė][ŒUē^öôƒ‚f:áĸ’M0âå&­Ŗ 6¨qÔfƒŽĄûAIė1ĐQđáŒéî˜CGŌÍ'QŖė~0xAmTNTh=@hŧ†dLDÉ`cԇŠoS‹ŠŽũ¯UĢĒÖĒĩĒöú’Ęn6ŊwīK}õũßY‹11~į?‚Ščėėė˜3gÎĨ§N:į?æÂ%ūäīl nÖĮšāhüČ0ŖG÷§Š'CåU ¸Ųm ŋ:ĸߟ.>Ljv"ˇĄS{:íö§Œ'CCŖcŠD‗ø> ÄĮū´Š&Zšô}¯OIŽĘx2T k2>Ž-P••ū´ņd¨Ús<ļ; Dģ?uLJĻË&…Æų˜†§Bęu"8ö‡ˇGô§”'CŲĖķŋ =Ŋ'ˆ'Céņ¯ŒÚēĄrdØÜôX| ž ĸĩIßwĻžŖyķæą“'OęøûPĨŽđāõņĘPŊbÅ ļzõęúĪ_}õU8žųæ]ņ âÉP8@„ŽŦDÁēĄlDfĒĪJ¸îēëXww7[´hQĻŋį âÉā*ŸīKú=(ŧÁŒ3ØĖ™3ĩŋOOÛDāyíIŋûōË/ūú8–.]ęAR-đd°GH99•†ēÁwß}GšØ*Ã"AÚņ}ę0áÉ`;¨¸ļĢĢ‹ĩˇˇŗO?ũ´á(°5 $HWč7< V…A*ĶqÕUWąÛoŋŊūķäädÃãæÎËQßc¯'CvUXɈ5¨*3"ĄāE™†yx•$H“ ų1$enŧņ†ā¸‘}öŲgņ_í žĪmŽÍ8´–„Â*3 s´ĘŦęĘв55ŠCG($Ã×_-õ 0Ķâ–{2¤‡°ĘŒyæ$P~ÁåĘsÚ*1Žū=ôPęÔhüđà ÷M›6Ŋá>¨B´ņņņ›‡–{—æŖ'C¸>ĪĘ,áQ™üŽü AšÔ¨¨J\(e‰O™%‚ Ü;x2(Ą=”Ķ<øāƒd•9x—‡yĸ4ĒĄ%ȓ5Ą " ö QgÚ4\W2<’U™UTÁ"dIĸ6€Ŧ™i?Ē' FY†žž5ėđáÃd›Fpx2$¨Â #–z‰Î2Ģ€ō 6C¤´4ĻRŖē‘TÉGĻIÔρ†Ë€`#ž ô ­2Ģ„G2e(Ú<§ ŅU%.R,HVęģīž›íŲķĸ&>ëëŗļ:Ha5ŠĘLÁfF–ø"RŖēũ•IĸÔŠÖZ­!*rĸMÃEe€"tä lĒBŪץZc €PiĪž=N6ņ9E†pŠ˛ĘŒėQZP~ÁTąMįë ”aöėYʏ—4ņ!sØÛôdHZę%ŠĘlKL ДM>UŅ4ë~Q›Æ­&>—”ÜP'otŠ—ŧdĐ加)ƒ*¤!‡¤MÃZŸd›đrU™UŒ3ķōķ=@SU˜={vęįIhâĩŅĻŅęڙdŠ—4iTSĒ`k€ÆE¨´nĢ…¸ 6ŠÔĄųČĀ$Uæ<ņŊŽæ<Û4.BĨu[Č@šÔÄg• ˛ EŌĻQUÂ$Uepe€Ļ<ĘОųų$M|…orŌj‘¨% ëŅJÛ˛ahĘĸ *7`žAA›FĄM|6•AXeÎsĨIë\ )‹Îę8dmŦĀ&>+dEĐReŽíÚmTvæ)͋ RĢY=Côņ’&žÁ@+IY•9kØŋ?ûøãfŖaPŲhĘ  I­ÛiÔÁöZK­AiC‘´j0::Jæ(@*pe€Ļ Ē  PÛk-ĩüy*m(’8Á“ˆ  ¤Fą´É;īŧÞ|ōIO„衞´=?üĸ šŊ.ŦGUƒ ˛ E˛4áˆ÷uėF‰čšįžĢ“ Ę5ȓIĸĐÛÛCŨŨÆķ-Ĩ#CŌ†"Y͍”QVNxølžĨ›ĄF`Bōfūâ¸ųæ›E>¤Į´:åČŗąĖKž*sU€ xĐëë_¸YŌć Ēąĩ–Œ+ƒŦĘ,Z1[T-!­2x¸§ ܇@!([f#ËGŗĖqÄ+ĖfĄĢu;§w`&ŊƒieČŊÔ E‚×^{M9MJĄ™Ú'tAWëļ*øZKuXY*2dŲP$ (ŦÁėæÍ ĄŲėŊ÷Ūķg¸ÃĘ î ¨n›ø{ŋ3í¤ūīá‡f—_~yjŖüĘ+¯ÔoûíˇÜ¯ī—_~ŠwJĸŽāUB ˜ę‹{†?üáfmhڜOgŋūúk`Ļ?o°œÁww"¸¨i]ÅģÅ„Š@ŌT™ĄēŒ°Č„G@mhhȟéŠjZ´2h͍Ã@xŽ9& 7Q­2ŖÅ!ŅļmÛ¤YŖųķįŗüãŋØ˛eË2ŋXÔžzę)Ŗ­UŊÛlã—7ņĀ9ļ^įßēDŗ*Ŧ¤TY#l“ǞÂÅÄÄD2%ĀlÂ_ū˛ŠŨu×]ī;pāŖēŸ 5zúé§ëŗ ģvíšj<Ū§\iÜwß} „¨ÕväîXUÅã÷QM|8IčjâkŅHi•YeC„DČɈlÛ62…želø]x“_|‘íÜšŗ^āÃļJ …([?CeM<ôŽ{f5Ķ´ļič “„ŠČĒˉ>øāƒúUY]ũõlĶĻ˙f<ō7r…‹/žøĸážkŽšĻ~ÄÉ4<ŕ#šizá…<Xē]zL‚¯ĩDņ$Lį[Wå*3N| i!ŅČČ[ĩj•ōĸ_*ķ˔§€BˆH2øÔ+s*͆&>†­’!͆"…!4’…DũũũõhÖŦtÛŌž9Ķ8ÆšpáBeBÄ 9R¯͚9õJ5čŨpÍV^ ß0‘˛y›øZrAŠ !H“, ‰xĒtdd˜ŨtSļYc"í–ĒŌ,˛Mĸ“ĄŲ ŅčĻYûۂĖRnuhÉH„Ä ExCB"YĒ'˙ĻM›ę!QPū"? 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""" ), autoescape=True, ) def _get_ops_template(): return jinja2.Template( """\ {% for sch in schemas %} .. tag-diff-insert. (l-onnx-op{{sch.domain.lower().replace(".", "-")}}-{{sch.name.lower()}}-{{str(sch.since_version)}})= ## {{format_name_with_domain(sch)}} ### Version - **name**: [{{sch.name}} (GitHub)]({{build_doc_url(sch)}}{{sch.name}}) - **domain**: `{% if sch.domain == '' %}main{% else %}{{sch.domain}}{% endif %}` - **since_version**: `{{sch.since_version}}` - **function**: `{{sch.has_function or sch.has_context_dependent_function}}` - **support_level**: `{{sch.support_level}}` - **shape inference**: `{{sch.has_type_and_shape_inference_function}}` {% if sch.support_level == OpSchema.SupportType.EXPERIMENTAL %} No versioning maintained for experimental ops. {% else %} This version of the operator has been {% if sch.deprecated %}deprecated{% else %}available{% endif %} **since version {{sch.since_version}}{% if sch.domain %} of domain {{sch.domain}}{% endif %}**. {% if len(sch.versions) > 1 %} Other versions of this operator: {% for v in sch.version[:-1] %} {{v}} {% endfor %} {% endif %} {% endif %} ### Summary {{process_documentation(sch.doc)}} {% if sch.has_function %} #### Function Body The function definition for this operator. ``` {{get_function_body(sch)}} ``` {% endif %} {% if sch.attributes %} ### Attributes {% for _, attr in sorted(sch.attributes.items()) %}* **{{attr.name}} - {{str(attr.type).split('.')[-1]}}**{% if attr.required %} (required){% endif %} {% if attr.default_value %}{{clean_default_value(attr)}}{% endif %}: {{text_indent(attr.description, 2)}} {% endfor %} {% endif %} {% if sch.inputs %} ### Inputs {% if sch.min_input != sch.max_input %}Between {{sch.min_input }} and {{sch.max_input}} inputs. {% endif %} {% for ii, inp in enumerate(sch.inputs) %} - **{{getname(inp, ii)}}**{{format_option(inp)}} - **{{inp.type_str}}**: {{text_indent(inp.description, 2)}}{% endfor %} {% endif %} {% if sch.outputs %} ### Outputs {% if sch.min_output != sch.max_output %}Between {{sch.min_output }} and {{sch.max_output}} outputs. {% endif %} {% for ii, out in enumerate(sch.outputs) %} - **{{getname(out, ii)}}**{{format_option(out)}} - **{{out.type_str}}**: {{text_indent(out.description, 2)}}{% endfor %} {% endif %} {% if sch.type_constraints %} ### Type Constraints {% for ii, type_constraint in enumerate(sch.type_constraints) %}* {{get_constraint(type_constraint, ii)}}: {{text_indent(type_constraint.description, 2)}} {% endfor %} {% endif %} {% if examples and is_last_schema(sch): %} ### Examples {% for example, code in examples.items(): %} #### {{ example }} ```python {{ format_example(code) }} ``` {% endfor %} {% endif %} {% endfor %}""", autoescape=False, ) def _get_main_template(): return jinja2.Template( textwrap.dedent( """ .. _l-onnx-operators: {{ title }} {{ "=" * len(title) }} Lists out all the ONNX operators. For each operator, lists out the usage guide, parameters, examples, and line-by-line version history. This section also includes tables detailing each operator with its versions, as done in `Operators.md `_. All examples end by calling function `expect`. which checks a runtime produces the expected output for this example. One implementation based on `onnxruntime `_ can be found at :ref:`l-function-expect`. .. toctree:: :hidden: ../expect_onnxruntime {% for p in pages %}{{ os.path.split(p)[-1] }} {% endfor %} .. tabs:: {% for t in tabs %}.. tab:: {{ t.domain_name }} {{ t.render(indent=" ") }} {% endfor %} """ ), autoescape=True, ) def _clean_unicode(text): text = text.replace(""", '"') text = text.replace("—", "-") text = text.replace(" ", " ") text = text.replace("'", "'") text = text.replace(">", ">") text = text.replace("<", "<") return text _template_diff = _get_diff_template() _template_operator = _get_ops_template() _template_main = _get_main_template() _all_schemas_with_history = None _attribute_conversion_functions = { onnx.AttributeProto.FLOAT: lambda att: np.float32(att.f), onnx.AttributeProto.FLOATS: lambda att: [np.float32(f) for f in att.floats], # AttributeProto.GRAPH(5) # AttributeProto.GRAPHS(10) onnx.AttributeProto.INT: lambda att: int(att.i), onnx.AttributeProto.INTS: lambda att: [int(i) for i in att.ints], # AttributeProto.SPARSE_TENSOR(11) # AttributeProto.SPARSE_TENSORS(12) onnx.AttributeProto.STRING: lambda att: att.s.decode("utf-8"), onnx.AttributeProto.STRINGS: lambda att: [s.decode("utf-8") for s in att.strings], onnx.AttributeProto.TENSOR: lambda att: onnx.numpy_helper.to_array(att.t), # AttributeProto.TENSORS(9) # onnx.AttributeProto.TYPE_PROTO: lambda att: OnnxType(att.tp), # AttributeProto.TYPE_PROTOS(14) } def _populate_all_schemas_with_history(): res: dict[str, Any] = {} for schema in onnx.defs.get_all_schemas_with_history(): domain = schema.domain version = schema.since_version name = schema.name if domain not in res: res[domain] = {} if name not in res[domain]: res[domain][name] = {} res[domain][name][version] = schema return res def _get_all_schemas_with_history(): global _all_schemas_with_history if _all_schemas_with_history is None: _all_schemas_with_history = _populate_all_schemas_with_history() return _all_schemas_with_history def get_operator_schemas(op_name, version=None, domain=None): """Returns all schemas mapped to an operator name. :param op_name: name of the operator :param version: version :param domain: domain :return: list of schemas """ if version == "last" and op_name is not None: if domain is not None: return [onnx.defs.get_schema(op_name, domain=domain)] all_schemas = _get_all_schemas_with_history() if domain is None: domains = [] for dom, ops in all_schemas.items(): if op_name is None or op_name in ops: domains.append(dom) else: domains = [domain] # schemas sch = [] for dom in domains: ops = all_schemas[dom] if op_name is None: for op, v in ops.items(): if version is None: sch.extend(v.values()) elif version == "last" and (dom == "" or "onnx" in dom): try: sch.append(onnx.defs.get_schema(op, domain=dom)) except onnx.defs.SchemaError: sch.append(v[max(v)]) elif version == "last": sch.append(v[max(v)]) else: sch.append(v[version]) elif op_name in ops: if version is None: sch.extend(ops[op_name].values()) elif version in ops[op_name]: sch.append(ops[op_name][version]) # sort vals = [(s.domain, s.name, -s.since_version, s) for s in sch] vals.sort() return [v[-1] for v in vals] def get_markdown_doc( folder, op_name=None, domain=None, version="last", clean=True, diff=False, example=False, ): """Returns a documentation in Markdown format for all :class:`OnnxOperator`. :param op_name: operator name of None for all :param domain: domain :param version: version, None for all, `'last'` for the most recent one :param clean: clean empty lines :param diff: highlights differences between two versions :param example: add example to the documentation :return: string """ schemas = get_operator_schemas(op_name, domain=domain, version=version) def format_name_with_domain(sch): if version == "last": if sch.domain: return f"{sch.name} ({sch.domain})" return sch.name if sch.domain: return f"{sch.name} - {sch.since_version} ({sch.domain})" return f"{sch.name} - {sch.since_version}" def format_option(obj): opts = [] if OpSchema.FormalParameterOption.Optional == obj.option: opts.append("optional") elif OpSchema.FormalParameterOption.Variadic == obj.option: opts.append("variadic") if getattr(obj, "is_homogeneous", False): opts.append("heterogeneous") if opts: return f" ({', '.join(opts)})" return "" def format_example(code): return code def get_constraint(const, ii): if const.type_param_str: name = const.type_param_str else: name = str(ii) name = f"**{name}** in (" if const.allowed_type_strs: types = [f"`{type_str}`" for type_str in sorted(const.allowed_type_strs)] text = ", ".join(types) name += " " + text + " )" return name def getname(obj, i): name = obj.name if len(name) == 0: return str(i) return name def process_documentation(doc): if doc is None: doc = "" if not isinstance(doc, str): raise TypeError(f"doc must be a string not {type(doc)!r} - {doc + 42!r}.") main_docs_url = "https://github.com/onnx/onnx/blob/main/" rep = { "[the doc](IR.md)": f"[ONNX IR]({main_docs_url}docs/IR.md)", "[the doc](Broadcasting.md)": f"[Broadcasting in ONNX]({main_docs_url}docs/Broadcasting.md)", } for key, value in rep.items(): doc = doc.replace(key, value) return textwrap.dedent(doc) def build_doc_url(sch): doc_url = "https://github.com/onnx/onnx/blob/main/docs/Operators" if "ml" in sch.domain: doc_url += "-ml" doc_url += ".md" doc_url += "#" if sch.domain not in (None, "", "ai.onnx"): doc_url += sch.domain + "." return doc_url def format_default_value(value): if isinstance(value, float): formatted = str(np.round(value, 5)) # use default formatting, unless too long. if len(formatted) > 10: formatted = f"({value:e})" return formatted if isinstance(value, (bytes, bytearray)): return value.decode("utf-8") return str(value) def clean_default_value(attr): if not attr.default_value.name: return "" default_value = onnx.helper.get_attribute_value(attr.default_value) if isinstance(default_value, onnx.AttributeProto) and hasattr( default_value, "default_value" ): if attr.type in _attribute_conversion_functions: sval = _attribute_conversion_functions[attr.type](default_value) return f"(default is `{sval!r}`)" if isinstance(default_value, list): sval = [format_default_value(val) for val in default_value] else: sval = format_default_value(default_value) return f"(default is `{sval!r}`)" def text_indent(text: str, indent: int) -> str: s = " " * indent return textwrap.indent(text, s) def get_function_body(schema: OpSchema) -> str: return onnx.printer.to_text(schema.function_body) examples = get_onnx_example(op_name, domain) if example else {} docs = _template_operator.render( schemas=schemas, OpSchema=OpSchema, len=len, getattr=getattr, sorted=sorted, format_option=format_option, get_constraint=get_constraint, getname=getname, enumerate=enumerate, format_name_with_domain=format_name_with_domain, process_documentation=process_documentation, build_doc_url=build_doc_url, text_indent=text_indent, str=str, clean_default_value=clean_default_value, examples=examples, format_example=format_example, is_last_schema=is_last_schema, get_function_body=get_function_body, ) d_links = {} for schema in schemas: sdom = schema.domain.replace(".", "-") d_links[ schema.since_version ] = f"l-onnx-op{sdom}-{schema.name.lower()}-{schema.since_version}" if diff: lines = docs.split("\n") new_lines = [""] for line in lines: line = line.rstrip("\r\t ") if len(line) == 0 and len(new_lines[-1]) == 0: continue new_lines.append(line) docs = "\n".join(new_lines) docs, d_links_diff = _insert_diff( folder, docs, ".. tag-diff-insert.", op_name=op_name, version=version, domain=domain, ) d_links.update(d_links_diff) if clean: lines = docs.split("\n") new_lines = [""] for line in lines: line = line.rstrip("\r\t ") if len(line) == 0 and len(new_lines[-1]) == 0: continue new_lines.append(line) docs = "\n".join(new_lines) return docs, d_links, len(examples) def _insert_diff( folder, docs, split=".. tag-diff-insert.", op_name=None, version=None, domain=None ): """Splits a using `split`, insert HTML differences between pieces. The function relies on package `pyquickhelper`. """ doc_parts = docs.split(split) if len(doc_parts) <= 1: return docs reg = re.compile("([A-Z][A-Za-z0-9_]*) - ([0-9]+)") d_links = {} pieces = [doc_parts[0]] mds = [] for i in range(1, len(doc_parts)): spl1 = doc_parts[i - 1].strip("\n ") spl2 = doc_parts[i].strip("\n ") vers1 = reg.findall(spl1) vers2 = reg.findall(spl2) spl1 = spl1.split("### Examples")[0].replace("`", "") spl2 = spl2.split("### Examples")[0].replace("`", "") spl1 = spl1.split("### Summary")[-1].strip("\n ") spl2 = spl2.split("### Summary")[-1].strip("\n ") if len(spl1) < 5 or len(spl2) < 5: pieces.append(doc_parts[i]) continue if not vers1: raise ValueError(f"Unable to find version {version!r} in\n{spl1}") if not vers2: raise ValueError(f"Unable to find version {version!r} in\n{spl2}") v2 = vers2[0][1] v1 = vers1[0][1] if not mds: mds.append( (v1, textwrap.dedent(spl1.strip(" \n\r\t")).splitlines(keepends=True)) ) mds.append( (v2, textwrap.dedent(spl2.strip(" \n\r\t")).splitlines(keepends=True)) ) if len(mds) > 1: show_diff_toc = True else: show_diff_toc = False if show_diff_toc: pieces.append("```{toctree}") for di in range(len(mds) - 1): dj = len(mds) - 1 v1, s1 = mds[di] v2, s2 = mds[dj] differ = difflib.Differ() result = list(differ.compare(s2, s1)) raw = "".join(result) diff = _template_diff.render( op_name=op_name, version1=v2, version2=v1, div_name=f"div_{op_name}_{i}", diff_content=raw, ) diff = _clean_unicode(diff) title = f"{op_name} - {v2} vs {v1}" name = f"text_diff_{op_name}_{v2}_{v1}" domain_str = domain.replace(".", "-") link = f"l-onnx-op{domain_str}-{op_name.lower()}-d{v2}-{v1}" d_links[int(v2), int(v1)] = link content = "\n".join( [ "", f".. _{link}:", "", title, "=" * len(title), "", "Next section compares an older to a newer version of the same operator ", "after both definition are converted into markdown text.", "Green means an addition to the newer version, red means a deletion.", "Anything else is unchanged.", "", ".. raw:: html", "", textwrap.indent(diff, " "), ] ) filename = os.path.join(folder, name + ".rst") pathlib.Path(filename).write_text(content, encoding="utf-8") # Add diff page to the toctree using myst syntax pieces.append(name) if show_diff_toc: # End the toctree pieces.append("```") pieces.extend(["", doc_parts[i]]) return "\n".join(pieces), d_links def pascal_to_snake_case(name: str) -> str: """Switches from *AaBb* into *aa_bb*. :param name: name to convert :return: converted name """ s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) s2 = re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower() return s2 if not keyword.iskeyword(s2) else s2 + "_" def _process_example(code: str) -> str: """Add necessary imports to make the example work.""" code = code.replace("", "") missing_imports = ["import numpy as np", "import onnx"] elements = [*missing_imports, "", "", code.strip("\n")] return "\n".join(elements) def get_onnx_example(op_name, domain): """Retrieves examples associated to one operator stored in onnx packages. :param op_name: operator name :param domain: operator domain :param fmt: rendering format :return: dictionary """ if domain in (None, "ai.onnx"): modules = [ f"onnx.backend.test.case.node.{op_name.lower()}", f"onnx.backend.test.case.node.{pascal_to_snake_case(op_name)}", ] else: domain_ = domain.replace(".", "_") modules = [ f"onnx.backend.test.case.node.{domain_}.{op_name.lower()}", f"onnx.backend.test.case.node.{domain_}.{pascal_to_snake_case(op_name)}", ] module = None for m in modules: try: mod = importlib.import_module(m) module = m except ImportError: continue if module is None: # Unable to find an example for 'op_name'. return {} results: dict[str, Any] = {} for v in mod.__dict__.values(): if not isinstance(v, _Exporter): continue code_cls = inspect.getsource(v) codes = code_cls.split("@staticmethod") for me in v.__dict__: if not me.startswith("export"): continue sub = f" {me}()" found = None for code in codes: if sub in code: found = code if found is None: raise RuntimeError(f"Unable to find {sub!r} in\n{code_cls}") found = textwrap.dedent(found) lines = found.split("\n") first = 0 for i in range(len(lines)): if lines[i].startswith("def "): first = i + 1 found = textwrap.dedent("\n".join(lines[first:])) key = me[len("export") :] if key == "": key = "default" if key in results: key = f"example {len(results) + 1}" results[key] = _process_example(found) return results def is_last_schema(sch: OpSchema) -> bool: """Tells if this is the most recent schema for this operator. :param sch: schema :return: True """ try: last = onnx.defs.get_schema(sch.name, domain=sch.domain) except onnx.defs.SchemaError: return True return last.since_version == sch.since_version def onnx_documentation_folder( folder, title="ONNX Operators", flog=None, max_opsets=None ): """Creates documentation in a folder for all known ONNX operators or a subset. :param folder: folder where to write the documentation :param title: index title :param flog: logging function :param max_opsets: included operator definition up to this opsets :return: list of creates files """ class _Table: def __init__(self, ops, domain, title=None): self.title = title or domain self.domain = domain self.ops = ops @property def domain_name(self): title = self.domain if title == "": title = "ai.onnx" return title def render(self, indent=""): table_dom = [""] table_dom.extend( [ ".. list-table::", " :widths: 10 10 10", " :header-rows: 1", "", " * - operator", " - versions", " - differences", ] ) for op in self.ops: name = op["name"] dom = self.domain.replace(".", "-") table_dom.append(f" * - :ref:`{name} `") versions = sorted( [(k, v) for k, v in op["links"].items() if isinstance(k, int)], reverse=True, ) col1 = ", ".join(f":ref:`{k} <{v}>`" for k, v in versions) diffs = sorted( [(k, v) for k, v in op["links"].items() if isinstance(k, tuple)], reverse=True, ) col2 = ", ".join(f":ref:`{k[1]}/{k[0]} <{v}>`" for k, v in diffs) table_dom.append(f" - {col1}") table_dom.append(f" - {col2}") table_dom.append("") if indent != "": for i in range(len(table_dom)): table_dom[i] = indent + table_dom[i] res = "\n".join(table_dom) return res all_schemas_available = _get_all_schemas_with_history() if len(all_schemas_available) < 3: raise RuntimeError( f"At least three domains are expected, found {list(all_schemas_available)}." ) # filter out operator under development all_schemas = {} for domain, opset in all_schemas_available.items(): max_version = None if max_opsets is None else max_opsets.get(domain, None) d = {} for op, schemas in opset.items(): vers = {} for version, schema in schemas.items(): if max_version is not None and version > max_version: continue vers[version] = schema d[op] = vers all_schemas[domain] = d if len(all_schemas) < 3: raise RuntimeError( f"At least three domains are expected, found {list(all_schemas)} in all_schemas." ) if not os.path.exists(folder): os.makedirs(folder) pages = [] tables = [] # loop on domains for dom in sorted(all_schemas): sdom = "ai.onnx" if dom == "" else dom dom_pages = [] do = all_schemas[dom] if len(do) == 0: raise RuntimeError(f"No operator for domain={dom!r}.") # loop on operators for op in sorted(do): if flog is not None: flog(f"generate page for onnx {dom!r} - {op!r}") page_name = f"onnx_{dom.replace('.', '')}_{op}" doc, d_links, n_examples = get_markdown_doc( folder, op, domain=dom, version=None, example=True, diff=True ) if flog is not None and n_examples == 0: flog(f"{' '* 14}no_example for {op} from domain {domain}") if dom == "": main = op else: main = f"{dom} - {op}" sdom = dom.replace(".", "-") # Target in MyST https://myst-parser.readthedocs.io/en/v0.15.1/syntax/syntax.html?highlight=role#extra-markdown-syntax ref_link = f"(l-onnx-doc{sdom}-{op})=" rows = [ "", ref_link, "", f"# {main}", "", doc, ] full = os.path.join(folder, page_name + ".md") content = "\n".join(rows) pathlib.Path(full).write_text(content, encoding="utf-8") pages.append(full) dom_pages.append({"name": op, "links": d_links}) tables.append(_Table(dom_pages, dom, sdom)) # final if len(tables) < 3: raise RuntimeError(f"At least three domain are expected not {len(tables)}.") index = _template_main.render(pages=pages, tabs=tables, os=os, len=len, title=title) index = _clean_unicode(index) page_name = os.path.join(folder, "index.rst") pathlib.Path(page_name).write_text(index, encoding="utf-8") pages.append(page_name) return pages def _generate_op_doc(app): logger = logging.getLogger(__name__) folder = app.config.onnx_doc_folder max_opsets = app.config.max_opsets onnx_documentation_folder(folder, flog=logger.info, max_opsets=max_opsets) def _copy_repo_docs(app): logger = logging.getLogger(__name__) dest_name = app.config.onnx_md_folder docs_dir = pathlib.Path(__file__).parent.parent.parent # docs dest_folder = docs_dir / "docsgen" / "source" / dest_name dest_folder.mkdir(parents=True, exist_ok=True) # Copy all the markdown files from the folder except for the blocklisted ones logger.info("Copying Markdown files from '%s' to '%s'", docs_dir, dest_folder) for file in docs_dir.glob("*.md"): if file.name in REPO_DOCS_EXCLUDE: continue shutil.copy(file, dest_folder) logger.info("Copying '%s'", file.name) def setup(app): """Sphinx extension `onnx_sphinx` displays documentation on ONN Operators. """ import sphinx app.add_config_value("onnx_doc_folder", "operators", "env") # Folder for storing the Markdown documentation from the repository app.add_config_value("onnx_md_folder", "repo-docs", "env") app.add_config_value("max_opsets", {}, "env") app.connect("builder-inited", _generate_op_doc) app.connect("builder-inited", _copy_repo_docs) return {"version": sphinx.__display_version__, "parallel_read_safe": True} if "debug" in sys.argv: print("DEBUG") onnx_documentation_folder("_debug", flog=print) print("END") onnx-onnx-bca0315/docs/docsgen/source/repo-docs/000077500000000000000000000000001511334557700216345ustar00rootroot00000000000000onnx-onnx-bca0315/docs/docsgen/source/repo-docs/.gitignore000066400000000000000000000000301511334557700236150ustar00rootroot00000000000000* !index.md !.gitignore onnx-onnx-bca0315/docs/docsgen/source/repo-docs/index.md000066400000000000000000000004011511334557700232600ustar00rootroot00000000000000 # ONNX Repository Documentation Markdown documentation from the [ONNX repository](https://github.com/onnx/onnx/tree/main/docs). ```{toctree} :maxdepth: 1 :glob: * ``` onnx-onnx-bca0315/docs/docsgen/source/requirements.txt000066400000000000000000000003221511334557700232220ustar00rootroot00000000000000 furo markupsafe matplotlib myst-parser[linkify] onnx onnxruntime<1.20;python_version<"3.13" onnxruntime;python_version>="3.13" pillow pydot sphinx-copybutton sphinx-exec-code sphinx-gallery sphinx-tabs sphinx onnx-onnx-bca0315/docs/docsgen/source/technical/000077500000000000000000000000001511334557700216735ustar00rootroot00000000000000onnx-onnx-bca0315/docs/docsgen/source/technical/InPlaceKVCache.png000066400000000000000000002517201511334557700251100ustar00rootroot00000000000000‰PNG  IHDRį`Ļū͟sRGBŽÎégAMAą üa pHYs,J,JwztM˙ĨIDATx^ėũipTįšīë…w‘Ę‹§¨ėS•x'ĩCÅU{“ōSą+ŲûWžüĪ““'Ū{ŸDįø#06˜I`b0 Xf˜b“˜ŲLļ13Ŗ0`f°lŒ1ķx=úŨZ-ĩZĢ%ĩPˇVw?UWŲZŊē[¨‡ĩÖoŨëē3 $á< F8@‚ΐ`„ķ$á< 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N…5÷UQ=>ŖęqšĶOwĐų!TđC€viY’¯;† ™ëķ?žĢ^j-įõ4Ëδë“#ūįVÕķÂ˙<C>p|ú5âyO9b‰rŪã‰}9/Rš%°ewø¸c9¯.•HūĐ ņĨjąÕNRģeHÖÜbšä°UŽ”•B-¨MiíŸÛ'[ orXƯō’Ī͒ôNší:¤JúĐ|)ąû§ jTĄfÎ?SŌ‚§Š;—œX(Ĩ‘k^ÔrYËmaXķüĘ÷åKFđ4§éB>ÕkÎ=\ Ų}ĨdģNéūÛĻD.9,)R~¸PrúÛyz]ŗũ?[nO§ÆmS.ĨsĢ/Wî.;Ü(įã—č•ķ•K¤ ôšĐ.U|ũ˛$¯¨TƜžģU%úQ9ē2ģęy™Ú'Kōˇ‡=)­˛ūĮô _Õ]SģeJNŅŅęb^ÕQÎëc=ôŧÚĨúĒž¯•Ÿ^§B×Ī|Z |ŪS΀Xĸœ÷xĸZÎ#†ĒËy§Ę1QPÎĮ/Ņ.į­ÎõĄœ÷x(įŊ‚ržD7”ķŪF9īņPÎ{å<‰n(įŧrŪãĄœ÷ ĘyŨPÎxåŧĮC97Ąœ_Zŧœ@ŗPÎ{<”ķpĘųø…rĀÛ(į=Ęy¸ å|üB9ām”ķå<܄r>~Ąœđ6Ęy‡rnB9ŋPÎxåŧĮC97Ąœ_(įŧrŪãĄœ‡›PÎĮ/”ķŪF9īņPÎÃM(įãĘyoŖœ÷x(įá&”ķņ å<€ˇQÎ{<”ķp“āsŲ˛eŽ…2‰^ŌŌŌäõ×_ˇˇ<ŧ†rŪãĄœ‡›TTT˜Įãøņã eœ;wÎÜΓ'Oļˇ<ŧ†rŪãĄœ‡ÛŒ1BúôéãX*“čdīŪŊæyŋråJ{ĢĀk(į=Ęy¸Í‚ Ėcōũ÷ßw,–Iķ“ŸŸonã;vØ[^C9īņPÎÃmžūúkéŪŊģtėØQ.]ēäX.“ĻgĪž=æ9?}út{‹Ā‹(į=Ęy¸ŅĮl—ƒv,˜IĶĶĨKsÛ^ŋ~ŨŪÚđ"Ęy‡rn5eĘķØœ:uĒcÉLŨb^ŋ‘ ˇiiiŠŊ•āU”ķå<ÜlÕĒUæņ9`Ā)))q,Iũ™={ļš5Ÿ|ō‰Ŋuāe1-įĩ8&ąå<Üėȑ#Ōŋķ8}å•W$++K&Ož,ķįĪ'ĩDwø:vėXķĄ†Īį3ˇŨ›ož)/^´ˇ*ŧ.Ļå<‰_7ģvíš,X°@Ūzë-éŅŖ‡ãc˜Dæõ×_7d,_žÜŪ’H1)į•ô$>Ņe/đ˙ˇcĮ61û/Ō¸G&€đSNÍH׀åhų’î>ū˛ŨĢĒ™įZüŅkq8į Lœ€0qÂÄyį Lœ€0qÂÄyˆÚŊuļYm9/•¤IENDŽB`‚onnx-onnx-bca0315/docs/docsgen/source/technical/float4.md000066400000000000000000000060521511334557700234110ustar00rootroot00000000000000 (onnx-detail-float4)= # Float stored in 4 bits ## Papers 4 bit floating point formats have emerged as a solution to the rising cost and deployment challenges of large language models. The S1E2M1 format has been part of the [Open Compute Project (OCP)](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) standard. As a result, a new data type was introduced in `onnx==1.18.0` to support a limited set of operators to enable computation with float4. - `FLOAT4E2M1`: 1 bit for the sign, 2 bits for the exponents, and 1 bit for the mantissa. No nan or infinities. ## E2M1 $S$ stands for the sign. $10_2$ describe a number base 2. ```{eval-rst} .. list-table:: Float4 type :widths: 10 10 :header-rows: 1 * - - E2M1 * - Exponent bias - 1 * - Infinities - * - NaN - * - Zeros - :math:`S.00.0_2` * - Max - :math:`S.11.1_2` * - Min - :math:`S.00.1_2 = 2^{-1}` ``` Let's denote the bit representation as $S.b_2 b_1 b_0$. The float value is defined by the following expressions: ```{eval-rst} .. list-table:: Float4 type values :widths: 10 10 :header-rows: 1 * - - E2M1 * - exponent :math:`\neq` 0 - :math:`(-1)^S 2^{\sum_{i=1}^2 b_i 2^{i-1} - 1} \left( 1 + b_0 2^{-1} \right)` * - exponent :math:`=` 0 - :math:`(-1)^S b_0 2^{-1}` ``` The following table lists all the representable values by float4 E2M1, ignoring the sign bit: ```{eval-rst} .. list-table:: Float4 type values :widths: 10 10 :header-rows: 1 * - bits (ignoring sign bit) - E2M1 * - 000 - 0 * - 001 - 0.5 * - 010 - 1 * - 011 - 1.5 * - 100 - 2 * - 101 - 3 * - 110 - 4 * - 111 - 6 ``` ## Cast Upcasting from float4 to float32, float16, bfloat16, and float8 is exact. The behavior for downcasting to float 4 is summarized below | x | E2M1 | | ----------------- | ------------------------------------------------- | | -6<=x<=6 | E2M1 converted value of x. Round to nearest even. | | x=+/-0 | +/-0 | | x>6 | 6 | | x<-6 | -6 | | +Inf | 6 | | -Inf | -6 | | NaN | 6 | ## Packing and Unpacking Float4 is stored as 2x4bit in a single byte. The first element is stored in the 4 LSB and the second element is stored in the 4 MSB, i.e. for elements `x` and `y` that are consecutive elements in the array: ``` pack(x,y): y << 4 | x & 0x0F unpack(z): x = z & 0x0F, y = z >> 4 ``` In case the total number of elements is odd, padding of 4 bits will be appended. The storage size of a 4 bit tensor of size `N` is `ceil(N/2)`. onnx-onnx-bca0315/docs/docsgen/source/technical/float8.md000066400000000000000000000176671511334557700234330ustar00rootroot00000000000000 (onnx-detail-float8)= # Float stored in 8 bits ## Papers Two papers have been published in 2022 to introduce floats stored on a byte as opposed to float 32 stored on 4 bytes. The float precision is much lower but the training accuracy does not suffer too much. [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433) from NVIDIA, Intel and ARM introduces two types following [IEEE specification](https://en.wikipedia.org/wiki/IEEE_754). First one is E4M3, 1 bit for the sign, 4 bits for the exponents and 3 bits for the mantissa. Second one is E5M2, 1 bit for the sign, 5 bits for the exponents and 2 for the mantissa. The first types is mostly used for the weights, the second one for the gradient. Second paper [8-bit Numerical Formats For Deep Neural Networks](https://arxiv.org/pdf/2206.02915.pdf) introduces similar types. IEEE standard gives the same value to `+0` (or integer 0) and `-0` (or integer 128). They chose to give distinct float values to these two numbers. The paper experiments different split between exponent and mantissa and shows and E4M3 and E5M2 are the best ones. As a result, four new types were introduced in `onnx==1.15.0` to support a limited set of operators to enable computation with float 8. - `E4M3FN`: 1 bit for the sign, 4 bits for the exponents, 3 bits for the mantissa, only nan values and no infinite values (FN), - `E4M3FNUZ`: 1 bit for the sign, 4 bits for the exponents, 3 bits for the mantissa, only nan values and no infinite values (FN), no negative zero (UZ) - `E5M2`: 1 bit for the sign, 5 bits for the exponents, 2 bits for the mantissa, - `E5M2FNUZ`: 1 bit for the sign, 5 bits for the exponents, 2 bits for the mantissa, only nan values and no infinite values (FN), no negative zero (UZ) The implementation is usually hardware dependent. NVIDIA, Intel and Arm implement `E4M3FN` and `E5M2` is its latest graphical processor. GraphCore does the same only with `E4M3FNUZ` and `E5M2FNUZ`. ## E4M3FN and E5M2 $S$ stands for the sign. $10_2$ describe a number base 2. ```{eval-rst} .. list-table:: Float8 types :widths: 10 10 10 :header-rows: 1 * - - E4M3FN - E5M2 * - Exponent bias - 7 - 15 * - Infinities - - :math:`S.11111.00_2` * - NaN - :math:`S.1111.111_2` - :math:`S.11111.\{01, 10, 11\}_2` * - Zeros - :math:`S.0000.000_2` - :math:`S.00000.00_2` * - Max - :math:`S.1111.110_2` - :math:`1.75 \times 2^{15}= 57344` * - Min - :math:`S.0000.001_2 = 2^{-9}` - :math:`S.00000.01_2 = 2^{-16}` ``` Let's denote the bit representation as $S.b_6 b_5 b_4 b_3 b_2 b_1 b_0$. The float value is defined by the following expressions: ```{eval-rst} .. list-table:: Float8 types values :widths: 10 10 10 :header-rows: 1 * - - E4M3FN - E5M2 * - exponent :math:`\neq` 0 - :math:`(-1)^S 2^{\sum_{i=3}^6 b_i 2^{i-3} - 7} \left( 1 + \sum_{i=0}^2 b_i 2^{i-3} \right)` - :math:`(-1)^S 2^{\sum_{i=2}^6 b_i 2^{i-2} - 15} \left( 1 + \sum_{i=0}^1 b_i 2^{i-2} \right)` * - exponent :math:`=` 0 - :math:`(-1)^S 2^{-6} \sum_{i=0}^2 b_i 2^{i-3}` - :math:`(-1)^S 2^{-14} \sum_{i=0}^1 b_i 2^{i-2}` ``` ## E4M3FNUZ and E5M2FNUZ The previous types support positive and negative zero, positive and negative nan. Another type definition was introduced by GraphCore to make a better use of these four values. Every type including UZ in its name have only one zero and one nan (= negative zero). The other difference comes from the exponent bias. As a result, a float 8 *FLOAT8E4M3FN*, not null, not nan, cannot be simply converted into *FLOAT8E4M3FNUZ* due to this exponent bias difference. Even if the mantissa is the same, the exponent is not. ```{eval-rst} .. list-table:: Float8 types :widths: 10 10 10 :header-rows: 1 * - - E4M3FNUZ - E5M2FNUZ * - Exponent bias - 8 - 16 * - Infinities - - * - NaN - :math:`1.0000.000_2` - :math:`1.00000.00_2` * - Zeros - :math:`0.0000.000_2` - :math:`0.00000.00_2` * - Max - :math:`S.1111.111_2` - :math:`S.11111.11_2` * - Min - :math:`S.0000.001_2 = 2^{-10}` - :math:`S.00000.01_2 = 2^{-17}` ``` The float value is defined by the following expressions: ```{eval-rst} .. list-table:: Float8 types values :widths: 10 10 10 :header-rows: 1 * - - E4M3FNUZ - E5M2FNUZ * - exponent :math:`\neq` 0 - :math:`(-1)^S 2^{\sum_{i=3}^6 b_i 2^{i-3} - 8} \left( 1 + \sum_{i=0}^2 b_i 2^{i-3} \right)` - :math:`(-1)^S 2^{\sum_{i=2}^6 b_i 2^{i-2} - 16} \left( 1 + \sum_{i=0}^1 b_i 2^{i-2} \right)` * - exponent :math:`=` 0 - :math:`(-1)^S 2^{-7} \sum_{i=0}^2 b_i 2^{i-3}` - :math:`(-1)^S 2^{-15} \sum_{i=0}^1 b_i 2^{i-2}` ``` ## Cast Cast from float 8 to [float 16](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) (or E5M10), [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) (or E8M7), [float32](https://en.wikipedia.org/wiki/Single-precision_floating-point_format) (or E8M23) is easier. The cast is exact. The conversion does not necessarily preserve the sign for specific values such as `-0` or `-NaN`. Cast to float 8 consists in finding the closest float 8 to the original float 32 value. It is usually done by shifting and truncating. The conversion may with saturation, every value out of range becomes the highest available value. Next table summarizes all the case. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | NaN | FLT_MAX | NaN | | -Inf | -FLT_MAX | NaN | -FLT_MAX | NaN | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The conversion may also be defined without any saturation. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | ## E8M0 The E8M0 data type serves as the common scale type for all [OCP Microscaling (MX) Formats](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf). It has eight bits for the exponent, and no sign or mantissa bits. ```{eval-rst} .. list-table:: E8M0 :widths: 10 10 :header-rows: 1 * - - E8M0 * - Exponent bias - 127 * - Infinities - * - NaN - :math:`11111111_2` * - Zeros - * - Max - :math:`11111110_2 = 2^{127}` * - Min - :math:`00000000_2 = 2^{-127}` ``` When computing scale factors in MX formats, there are different casting choices one can make. For this reason, the ONNX spec for the Cast operator has introduced an additional "round_mode" attribute, which accepts the following: - "up": round to nearest value away from zero - "down": round to nearest value towards zero - "nearest": round to nearest value and ties round up It has been [shown](https://arxiv.org/abs/2506.08027) that rounding up with saturation achieves superior accuracy in LLM pretraining compared to other rounding modes. onnx-onnx-bca0315/docs/docsgen/source/technical/index.md000066400000000000000000000004441511334557700233260ustar00rootroot00000000000000 (onnx-technical)= # Technical Details This section enters into implementation details, technical descriptions, deeper than the code documentation. ```{toctree} :maxdepth: 2 float8 int4 float4 int2 ``` onnx-onnx-bca0315/docs/docsgen/source/technical/int2.md000066400000000000000000000030221511334557700230660ustar00rootroot00000000000000 (onnx-detail-int2) = # 2 bit integer types ## Papers [T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge](https://arxiv.org/abs/2407.00088) T-MAC, an innovative lookup table(LUT)-based method designed for efficient low-bit LLM (i.e., weight-quantized LLM) inference on CPUs. T-MAC directly supports mpGEMM without dequantization, while simultaneously eliminating multiplications and reducing additions required. Specifically, T-MAC transforms the traditional data-type-centric multiplication to bit-wise table lookup, and enables a unified and scalable mpGEMM solution. ## Cast Cast from 2 bit to any higher precision type is exact. Cast to a 2 bit type is done by rounding to the nearest-integer (with ties to even) nearest-even integer and truncating. ## Packing and Unpacking (2-bit) All 2-bit types are stored as 4×2-bit values in a single byte. The elements are packed from least significant bits (LSB) to most significant bits (MSB). That is, for consecutive elements x0, x1, x2, x3 in the array: Packing: ``` pack(x0, x1, x2, x3): (x0 & 0x03) | ((x1 & 0x03) << 2) | ((x2 & 0x03) << 4) | ((x3 & 0x03) << 6) ``` Unpacking: ``` x0 = z & 0x03 x1 = (z >> 2) & 0x03 x2 = (z >> 4) & 0x03 x3 = (z >> 6) & 0x03 ``` In case the total number of elements is not divisible by 4, zero-padding will be applied in the remaining higher bits of the final byte. The storage size of a 2-bit tensor of size N is: ceil(N / 4) bytes onnx-onnx-bca0315/docs/docsgen/source/technical/int4.md000066400000000000000000000055751511334557700231070ustar00rootroot00000000000000 (onnx-detail-int4)= # 4 bit integer types ## Papers Several papers have been published in 2023 to introduce 4 bit integers and their usage in LLMs. Although their range is limited, with careful selection of scaling parameters, good accuracy is obtained when used for compression of weights (weight-only quantization), and in some cases for quantization of activations as well. [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://arxiv.org/abs/2306.00978) Activation-aware Weight Quantization (AWQ) focuses on the quantization of weights in LLMs by considering the observation that not all weights are equally important. The method aims to protect salient weights based on the activation, rather than relying on backpropagation or reconstruction techniques. By searching for the optimal per-channel scaling that preserves the crucial weights, AWQ aims to minimize quantization errors. [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://arxiv.org/abs/2210.17323) GPTQ proposes a one-shot weight quantization method based on approximate second-order information. GPTQ achieves significant compression gains, reducing the bit-width to 3 or 4 bits per weight with negligible accuracy degradation compared to the uncompressed baseline. [Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases](https://arxiv.org/abs/2301.12017) This paper discusses quantization of both weights and activations to 4 bit (W4A4). Results indicate that W4A4 quantization leads to little to no accuracy degradation for encoder-only and encoder-decoder models but results in a significant accuracy drop for decoder-only models. To realize the performance gains using W4A4, the study introduces a highly optimized end-to-end W4A4 encoder inference pipeline that supports various quantization strategies. As a result, two new types were introduced in `onnx==1.17.0` supporting a limited set of operators to enable compression using 4 bit data-types: - `UINT4`: 4 bit unsigned integer, values in range [0, 15] - `INT4`: 4 bit signed integer, using two's complement representation. Values in range [-8, 7]. ## Cast Cast from 4 bit to any higher precision type is exact. Cast to a 4 bit type is done by rounding to the nearest-integer (with ties to even) nearest-even integer and truncating. ## Packing and Unpacking All 4 bit types are stored as 2x4bit in a single byte. The first element is stored in the 4 LSB and the second element is stored in the 4 MSB. i.e. for elements x, y, that are consecutive elements in the array: ``` pack(x,y): y << 4 | x & 0x0F unpack(z): x = z & 0x0F, y = z >> 4 ``` In case the total number of elements is odd, padding of 4 bits will be appended. The storage size of a 4 bit tensor of size `N` is `ceil(N/2)`. onnx-onnx-bca0315/docs/docsgen/source/technical/kv_cache.md000066400000000000000000000056531511334557700237710ustar00rootroot00000000000000 (onnx-detail-kvcache)= # In-place KV Cache for Attention KV caching in attention-based models refers to a mechanism for storing previously computed Key and Value tensors during autoregressive generation. In decoder-only transformers, each new token must attend to all previous tokens using the attention mechanism. Normally, this would require recomputing the Key and Value projections for every prior token at each time step, which is inefficient. Instead, the KV cache stores these projections after they are first computed, allowing the model to reuse them for future tokens without recomputation. This significantly speeds up the generation process. Updating the KV cache in place means writing new Key and Value tensors directly into pre-allocated memory at the index corresponding to the current position in the sequence. This has several advantages: it avoids repeated memory allocation or copying, reducing computational overhead; it also allows better performance on hardware accelerators by enabling the use of fused kernels and reducing memory bandwidth usage. In-place updates are essential for achieving high throughput and low latency during inference, particularly for large language models deployed in real-time applications. ONNX opset-24 has introduced new features to facilitate the representation of in-place KV cache updates. This diagram shows an example use case: ![InPlace KV Cache](InPlaceKVCache.png) - The `K` and `V` inputs to the `Attention` op contain the entire KV cache tensors with the sequence length dimension being max_sequence_length, hence the size of these inputs do not grow between autoregressive iterations. For this reason an optional `nonpad_kv_seqlen` input can be used to indicate the number of valid (non-padding) tokens in each sample to skip unnecessary computations. - The logic for KV cache update is separated out of the `Attention` op. The `TensorScatter` op can be used to update the cache tensors, where the incoming key and value tokens for the current iteration are scattered into the cache tensors according to `write_indices`. - As an optimization, the backend is free to alias the past and present key/value tensors to avoid duplicating the cache tensors and achieve in-place update. For this optimization to be valid, the backend will need to ensure that the input to `TensorScatter` is not subsequently reused by other ops. Only then is it safe to reuse the memory allocated to the `past_k/v` input of the op for the `present_k/v` output. - The same computational graph can be used for both the prefill and decode stages of the autoregressive model. As a reminder, the ONNX representation is still a functional representation, with ops that are pure functions. The graph layout described above is a useful common pattern to express in-place KV cache update, and the input/output aliasing is entirely up to backend implementations. onnx-onnx-bca0315/docs/images/000077500000000000000000000000001511334557700162645ustar00rootroot00000000000000onnx-onnx-bca0315/docs/images/onnx_hub_arch.svg000066400000000000000000000300711511334557700216230ustar00rootroot00000000000000Git LFS StorageONNX Hub ArchitectureM1 v2M2 v3MN v1M1 v1M1 v2M1 v2â€Ļonnx/models RepoMANIFEST.jsonM1 v2 PointerM2 v3 PointerMN v1 Pointeronnx/onnxRepoONNX Hub Python ClientLocal MachineLocal Processimport onnxmodel = onnx.hub.load(‘modelN’)Model CacheMN v1onnx-onnx-bca0315/docs/onnx-horizontal-color.png000066400000000000000000002113031511334557700220120ustar00rootroot00000000000000‰PNG  IHDR ¤Ģ18 pHYs.#.#xĨ?v IDATxœėŨol\į}'úG’úÚŽ¨¤ŲÅÚYWL‘E‘äxNîÚNK&ÍĢĸbķÍžXŅ{_ôÅ&]ÜÚīf¨—.VôÖ¸]€2…ģ¸loCŨânTĀ ØcJ.‚kEÃą°1ĩvÅڐėnoĪÅa:´øwΘ3ķų„“39sf8Îų>ŋī4M´ë—ėAŠ @!Ō(„@…H iB €B¤P4 !@!Ō(„@…H iB €B¤P4 !@!Ō(„@…H iB €B¤P4 !@!Ō(„@…H iB €B¤P4 !@!Ō(„@…H iB €B¤P4 !@!Ō(„@…H íF,IR;B8ŋßåÆß !\ÉžšÍÅ[šK` Ō4Ŋû%@ßH’ÚXa4„ũ÷Čn×ÕÂ|aĻŲ\ŧ’ģ6Hƒ>'ĄM„Æ÷BÛJ+ Ļ…ĻLNānŌ Īl ĸe_C¸uk1”6™ģ€&}$IjŖqŠYŅv’MLo6įwø>Ä/šŖĄ?$I-›XöJ—Âh!ūžWâīŌ ębEg6íDoĘųfsq<ˇ€"ÃhYeæą܊Ģ!„ŅfsņVî‚ĘN¨ļ˛„ŅBŧ3𭠁4¨¨$ŠÍ”(ŒļáDŧ^ 4¨ $Š‡N–ôšŸŒ×€s MS÷9TH’Ô†CWBC%žÖk!„ãÍæâJîú– iP=S%Ŗ…xũĻr[čk&¤A…$Im4„đJ…Žō—›ÍÅųÜVú’ iP-“Ž/eeBTD’ÔŽ‡–*x}ēŲ\\Ém ī˜Õ1^Ņûj"ˇ€ž$ÕQÕ@ÚXn }I * ÖuUôž:’$ĩáÜVúŽ@TÃhÅī'ĩ@ ĒĄę´SqĘ}L ĒápÜOķBiũí@šĻîb(š$ŠõËęZa*„0Ķl.Žä. ŌŌ ú(ļY+„…ŌæCW˛/!5€jHƒ čĶ@ÚŨŦÅpš@ ¤A P ín„Ô*B *`Āiw#¤PBiPiģ"¤ĐciPIRËBV#îĢ=Rč"4¨€$ŠÍ„Nē¯ !¤Đ!iPIR›!œu_uŒ@Ō ’¤v<„°äžę*!5€=HƒŠH’Z†:âūę)!5€m¤AE$Im*„pĘũU:BjŅA;*ãŠģĒ”†B#ņk]’Ô„Ô€dBTD’ÔFC¯¸ŋ*KH č{&¤Au ģ¯*Í$5 ī ¤Auw_õ!5 ¯¨ė„ŠH’ZJ:RÁûëjáXn+{!¤T‚@T@’Ô&Bg+t_] !L4›‹W66$Im4„0'Ŋ¯h¸ŽL„Ô€ŌHƒ’K’Úp  Uäž:Ũl.Næļ~H’ÔĮ`šZq„Ô€žHƒ’K’Ú• U^žo6Į_{íĩ,d6ÃfC1(5ģ’:zôč|î_ ŠuАĐ5iPbIRË&5*r]͂dĶĶį˛ë|*wiŪՍ€”Z× Š!%•$ĩ,xĩT‘ûg#Œ6B8›ģto?GH­7„Ô€ļ ¤A ÅĀÕ|EĒ:įBãĶĶįÆBĪį.mŸZīŠ{"%”$ĩŠ]Ö^öÚSÍæâÔk¯Ŋv<†–†ēt}„ÔzGH ؒ@”L’Ô˛đÔ+%ŋ_.fSҞ Ōk¯Ŋ6ŪĄÉh{%¤Ö;BjĀ:4(‘–Zé⤹Ŋʂh“ÍæâzØëĩ×^+û$7!ĩŪR€$%’$ĩŲ‰’Ũ'Y-ģ^ŗĸ×^{- nea´cšī.?!ĩŪR€>'%‘$ĩ^W_ÎÅĐ­˙6›‹W6C ĸM†Fr˙ēڄÔzGH úˆ@”@’Ô†c§UY hôÃáŗ ¯ŊöZvŨÆB㝈ļ_BjŊ#¤%%$ĩųN{˛Ų\œyíĩ×6ÂTÃqûhüßÂUŋ ¤Ö;BjPiĐcIRË*0=ēkĶĶį†cČgĻŸIH­w„Ô dŌ ‡’¤–…’–zxæĻ§Īîátļ~%¤Ö;BjĐCiĐ#1ŒÔĶÉd_üâ˙üŋ˙ÁüÁ˙–ģ€NR띞Š%ImxSÕîf+Âxt›@ôH’ÔĻB§zš˙ëõúų#G†Oæ. [„Ôz§˛!ĩ$ŠÅĮÂč.­7nkŗš8›ģ $=$ĩ,HōJ¯÷ũôôš‹ę:KGH­wJR‹õž!„,Œ6”û†ŨËncJ›j6¯tûvĐ˙Ō ËbhĨÍPI.NOŸFĢ!ĩŪéiH-†W';ÍŠ‚iI ē,IjŲtĸŊŪī###į˙åŋ<ŠŽŗē„Ôz§ã!ĩxŋeĩžŨø}6 Ŋ5›‹ˇr—Ā ¤A%I-ĢÛûAöųÄÄSį>˙ųĪ˙ĢÜT™ZīR‹SŅfģ~­R능ÂDÁ”4vdB,Ijc!„øSŗéDĮëõÆäßüÍߌũû˙ åžcϧĪ] !Œ¸ß)ą\HínWUH­ĢLI`WLHƒ%Im8NG+ŌdŊŪȂ6'õWĩŨ+ŒF˜¤V>YvŦĪoô4(ÖL neŽ^od?s%ûy+++mũØ/|á ¯ ¤QQBjŊ7.ĀNŌ  IR›(8ėĩ ŗ!ˇviŋõ[#Ím„ęR뮑lŋŠí`;iP€$Šea–ŗīËņzŊ1ž9äÖjĩHûėg?ûOrĄŋŠuÖh ÉĀ] ¤A1ŠŽą{ļ^oŦlūšīŊ÷^¸uĢ­ÁDW<øšÜVčE…Ô†ã˙>6Ā™ãilG ڔ$ĩɂ*­Âd Å mllˇŽķĄ‡úëÂ×sĀ`ÚwH-üüī~ķĩA ŠæļĀ&iІJiŧŗĒΉ\Ú ¤}õĢ_mkŧ €]…Ô˛Íæĸ܅@ėSŦō+ēĒķtŊŪ¸uˇ[ĢÕ^ í‘Gžp8ˇØIQ!ĩąÂČŋĢ úá6ĐAi°S!„#îŋĢõzc*†\rVWWsÛö uß}÷}Æ} …ØOHm*Ijc1Ä:än _ ¤Á>Ä`ÉÉ÷ŨZVÕB˜ŧ[Č­ÕjåūÁ^:tč'!„ßq_CĮlRûĢŋúĢ˙ī?ūĮ?Ë.{Ôî ß ¤Áu¨Ēs˛^o ‡Nå. !ŦŦ´W×ų•¯üv[ãՀ}YŠ}ūķŸŲ×ĩk×… ? ö&}K önļāĘŊ‹õz# ¸m™:kĩļŧhWjĩÚ˙p?CīÜšs'\ģöfX^^ŽúŊĐŪ¸Fúž@ėA’Ô&B#îŗŦĒs,N\Û2äÖxāáÜF ãnŪŧ.… .„Ûˇo÷ÃoīÉ€ž'ģ”$ĩãYĩfÁûkŧ^oŒ‡Nä.‰Ž_ŋžÛļG Ņ;Č&ĄeA´>ŦįH`[i°{ÛN1ۇšzŊq%ūÜ-ĩ;mdddE ēãōå…péŌBX^~Ŋ_÷ø•ÜØD v!IjŲd´cîĢV6-„0ŋSČ­Õj;–ÛįΝ;á…­OCģqãFŋīŲųÜØD vĢ:ÛמeUģ š­ŽŽæļíŧ>õO˙‘ûŠwķæÍ077–––ÂíÛˇaˇšÍEŌØ–@l#Ij‡Cŗ[Įž<[¯7ní&äļļļnŨē•ÛžW<ø„ûŠŗŧŧæææúš–s+E?ЇŌ`{YUį‘mŋcoŽÖëÉŨÖŪ­Ŧ´W×yôčҟ\5 )Ģå\Zzu=ˆ6ĩœ[H`Gi°…$Š…NŨũŌ}!ˇ]…ÄÚ ¤=öØã7s]Ëj9.… . 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€B¤P4zBø˙pt"ĒZmíŒIENDŽB`‚onnx-onnx-bca0315/docs/proposals/000077500000000000000000000000001511334557700170415ustar00rootroot00000000000000onnx-onnx-bca0315/docs/proposals/ArchiveFileFormatProposal.md000066400000000000000000000077251511334557700244500ustar00rootroot00000000000000 # ONNX File Format Proposal ## Summary We propose a new file format for ONNX models that is a specific application of the [zip](https://en.wikipedia.org/wiki/Zip_(file_format)) file format. We would like to address issues with capacity limits as well as (de)serialization inefficiencies[0][1]. We aim to design a file format that is simple, widely applicable, and efficient. By storing Tensor values (i.e. values typically contained in `TensorProto` messages) as files within a zip archive, we avoid these size limitations and—with special constraints—allow for direct memory-mapping of an ONNX file such that weights can be used directly from the memory-mapped region. Using zip as our base file format allows us to create a design that is conceptually simple as well as well-supported on various platforms. ## Design We propose to treat a .zip file as a key-value store, mapping string keys (filenames) to binary data files. For ONNX model serialization, we will have the following entries: * Data files - Files mapping a unique string identifier to a raw binary data file. These files shall be referenced from the appropriate fields within the base `ModelProto` * `__MODEL_PROTO` - File that contains the `ModelProto` describing the file Note that the order is significant here. We place the model definition file at the end of the archive to allow for the common case of net manipulations while keeping the weights invariant. This way, tools that manipulate the archive do not need to repack or realign all weights when only touching the model file. Within the ONNX protobuf definition, we propose the following changes: * Add `optional string external_data` to `TensorProto`. This can be treated as a data field similar to `float_data`, `int_data`, etc in that there must be exactly one of those fields specified. If a `TensorProto` specifies `external_data`, the implementation shall resolve this reference by string key in the containing zip archive. All values of `external_data` must be unique (under down-casing) and conform to the C identifier specification. Raw data files referenced by `TensorProto`s shall conform to the following specification: * The data shall be equivalent to that stored within the `raw_data` field in `TensorProto`. * Raw data files within the zip archive shall reside on an alignment boundary of 64 bytes. That is, the byte offset within the file of the first byte of a raw data tensor must be divisible by 64. This requirement can be fulfilled by packing bytes into the `extra` field of each local file record in the zip archive. (example: [2]). This constraint facilitates the direct memory-mapping of data files within the archive, and allows for architectures with both strict alignment requirements (e.g. SIMD instructions on aligned data) to operate and give architectures that operate more efficiently on cache line-aligned data to take full advantage. ## File Extension In keeping with other domain-specific zip applications, we propose to use a custom file extension rather than the `.zip` extension. A custom file extension makes it clear to the user that this is not a general zip file, but rather a file that should be emitted by ONNX tools to conform to the spec. ## Future-Proofing Considerations This file format represents a generic key-value store that is scalable to many entries as well as large values. Further improvements to the format may come in the form of supporting different or multiple model definitions within the same model, or modifying the way in which weight files are stored. Building off of a proven archival format allows us the reliability as well as flexibility of zip. [0] https://github.com/onnx/onnx/issues/251 [1] https://stackoverflow.com/questions/34128872/google-protobuf-maximum-size [2] https://developer.android.com/studio/command-line/zipalign.html implementation https://github.com/aosp-mirror/platform_build/blob/master/tools/zipalign/ZipAlign.cpp onnx-onnx-bca0315/docs/proposals/FunctionsProposal.md000066400000000000000000000030531511334557700230540ustar00rootroot00000000000000 ## Proposal Adding Function into ONNX Motivation: 1. Reduce number of primitive operators in ONNX To make it easier for hardware vendors to follow ONNX, we want to make it possible to define composite operators in terms of more primitive operators, reducing the number of kernels which must be directly implemented. For example, FC should be declared to be a composition MatMul and Add. 2. Expose customize function capability for graph optimization. To provide a mechanism of doing graph optimization, say, kernel fusion (merge a subgraph into one node with generated efficient kernel codes). This will in turn help HW acceleration, since common-patterns of kernel fusion may be pre-defined as common functions in ONNX and no sub-graph (function) finding needed for kernel fusion anymore. For example, subgraph having "Add", "Sigmoid", "Tanh", "Mul" nodes could be merged into one fusion node with generated cuda kernel containing "+", "sigmoidf", "tanhf", "*". 3. Provide a flexible RNN implementation. To define a library of RNN cells and allow the user to write a custom one. MAJOR CHANGES: 1. FunctionProto added to represent a function. 2. FunctionSetProto added to represent a function set. 3. AttributeProto updated to support function attribute type and allow attribute reference. 4. ModelProto updated to contain customized function set. Prototype details can be found [here](https://github.com/linkerzhang/onnx/blob/kezhan/add_function_private/onnx/onnx.in.proto) onnx-onnx-bca0315/docs/proposals/NLPinONNXproposal.md000066400000000000000000000200651511334557700226310ustar00rootroot00000000000000 ## Background Modern NLP (Natural Language Processing) is an important domain in which deep learning is applied. In addition, modern NLP networks are often non-trivial to implement and even more difficult to transfer between frameworks. These networks are handled fairly non-uniformly across the landscape of frameworks. The ability for ONNX to interchange these networks can be a very compelling feature. NLP networks, including recurrent networks, are often built on dynamic control structures. Standardizing the handling of these structures can lead to better collaboration with backends to expose network semantics and achieve better performance. A tradition has developed within the Computer Vision field for optimizing hardware backends for canonical vision models, such as ResNet-50. There is not really such as tradition in the NLP field, however. Through standardizing the representation of NLP networks, we can give vendors a common representation and push forward the performance of NLP models. ## Ultimate Goal and Challenges We should work toward being able to represent major classes of NLP model architectures. One example of such an architecture is the seq2seq with attention model (e.g. https://arxiv.org/abs/1409.0473). This architecture is used for many use cases, including neural machine translation, speech processing, summarization, dialog systems, image captioning, and syntax parsing, among many others. At the same time, seq2seq with attention is sufficiently complex that supporting it will push forward the state of the art in ONNX, but not so complex that we'd need to define a full programming language. seq2seq with attention can roughly be broken down into these constituent parts: * An Encoder network * This network takes a sequence of tokens and yields a sequence of embeddings representing the context found at each time-step * Major classes of encoders: recurrent network (e.g. LSTM[1]), convolutional[2], attention[3]. * Requirements from an ONNX representation * Recurrent network - general recurrent network structures preserving outputs at every timestep. Handling of padding and hidden states for batches with different sequence lengths). * Convolutional - 1d convolution, position embeddings * Attention - sinusoid position encodings, layer normalization * A Decoder network * This network generates a sequence token by token, parameterized by the context provided from the encoder. * Yields a probability distribution over possible tokens given previous context and encoder context. * Major classes of decoders: recurrent network (e.g. LSTM), convolutional (causal, temporal for generation), attention. * Generation requires dynamic control flow. Often, this is done as a beam search, so this is distinct from regular recurrent networks. * Model-specific requirements * Recurrent network - Recurrent network cell that can be used within the context of beam search * Convolutional - 1d causal convolution (only see previous timesteps) * Attention - sinusoid position encodings, masking along diagonal * An Attention mechanism * This network weights the Encoder contexts based on the Decoder's generation state, and provides a focused Encoder context to the decoder. The Decoder “focuses” on a certain part of the input sequence at each timestep via this mechanism. * Many classes of attention mechanism: some examples are here https://arxiv.org/pdf/1508.04025.pdf Vanilla seq2seq with attention and non-backtracking beam search does NOT include things such as auxiliary data-structures (e.g. stacks), thus it does not require us to implement the full semantics of a programming language. It is an architecture that we can break down into incremental improvements to ONNX without compromising ONNX's fundamental goal. [1] https://arxiv.org/abs/1409.0473 [2] https://arxiv.org/abs/1705.03122 [3] https://arxiv.org/abs/1706.03762 ## Standard Recurrent Network Constructs Standard recurrent network architectures such as LSTM or GRU are very common, and we can get very far supporting these. We already have the [LSTM](/docs/Operators.md#LSTM) and [GRU](/docs/Operators.md#GRU) operators, which execute the standard LSTM and GRU[4] operations over a sequence of inputs. These high-level operators are great, since they give backends a semantic view of the computation to be performed, and thus backends can make informed decisions about optimization. Many NLP use cases can get away with using just these operators. [4] http://colah.github.io/posts/2015-08-Understanding-LSTMs/ ## Generic Control Flow Once we move beyond the domain of standard LSTM and GRU operations, we need a more generic abstraction onto which we can map NLP architectures. A simple example is how one can implement Multiplicative Integration LSTM (https://arxiv.org/pdf/1606.06630.pdf) in ONNX. We can expose a standard LSTMCell via the proposed Function abstraction (https://github.com/onnx/onnx/issues/481). Building on top of this, we can construct a MI-LSTM by applying the required second-order transformations to the inputs to the LSTMCell. Once we have this aggregated implementation, we can use the generic control flow operators (https://github.com/onnx/onnx/pull/436) to apply this “composite” MI-LSTM cell over a sequence. Of course, the dynamic control flow constructs can be used for more general use cases. For example, consider the [beam search](https://en.wikipedia.org/wiki/Beam_search) used often in NLP for sequence generation. This algorithm has several tricky aspects: a (potentially) dynamic stopping condition, a desired maximum trip count (so we don't fall into an infinite loop), loop-carried dependencies, and the desire to preserve the outputs at every time-step, not just the final time-step. Inherently, this is an imperative algorithm that operates on mutable state. The proposed control flow operators in ONNX, however, fulfill all of these requirements, and thus we can represent many instances of sequence generation in ONNX graphs. Note that there are more general forms of beam search, such as those including backtracking, but we are not considering these forms for this focused proposal. ## End-to-end Example : seq2seq with attention We should endeavor to have full support for seq2seq with attention models in ONNX. Facebook is currently working on this internally and creating a pytorch→ONNX→caffe2 pathway. An example of such a model we'd like to represent in ONNX is [fairseq](https://github.com/facebookresearch/fairseq). We would love to engage with the community and collaborate on anything that will help make this a reality. Additionally, if the community has any other suggestions for prominent NLP models we should be able to represent, we would love to hear your ideas. ## Further Challenges Beyond the constructs used in seq2seq with attention, there are NLP models that exist today that contain more non-trivial features, such as mutable data structures that are manipulated at runtime. Examples of this include back-tracking beam search and parser models such as RNNG (https://arxiv.org/abs/1602.07776). These will present further challenges for ONNX, and the representation of these models will likely remain tied to application code for the time being. We may want to revisit this class of models in the future. Another thing we should consider is how to handle preprocessing and postprocessing routines for NLP models. For example, do we defer tokenization, normalization, and index lookup to application code? And how do we, for example, distribute dictionaries that map tokens to indices. Initially this will probably remain out of the scope of ONNX unless there is a good story for standardizing text processing. ## Conclusion We have presented a proposal for a strategy for representing NLP models in ONNX, using seq2seq with attention as a canonical example that covers many use cases. We would like to hear your thoughts about this proposal and to explore opportunities for collaboration with the ONNX community for making ONNX a pleasure to use for NLP. Please feel free to voice your opinions! onnx-onnx-bca0315/docs/proposals/ONNXIFIproposal.md000066400000000000000000000221461511334557700222620ustar00rootroot00000000000000 # ONNX Interface for Framework Integration: API Proposal ## Background Leading hardware and systems vendors offer highly optimized software to run neural network graphs. These software can deliver order-of-magnitude speedups compared to generic implementations, but their integration with deep learning frameworks and applications is complicated by large variety in vendor-specific interfaces, and subtle incompatibilities with the software stack of high-level applications. So far, ONNX format targets the problem of offline conversion of neural network models between different high-level frameworks and vendor-specific libraries through offline translation. In this proposal, we suggest that ONNX ecosystem could be enriched to enable runtime discovery and selection of high-performance graph execution backends, and online (in runtime) conversion of ONNX graph to internal representations of these implementations. ## Ultimate Goal We should strive for consensus on a library API to interface with optimized backends and offload parts of ONNX graphs to these high-performance hardware and software implementation. The API should enable wide interoperability between high-level deep learning frameworks, software implementations of optimized graph runtimes, and existing and upcoming neural network acceleration hardware. The standardized API should reduce friction in deploying neural network models for all involved parties: - Applications would be able to ship only one version of a neural network model (either in ONNX format, or in the format of their deep learning framework, and convert it on the fly to ONNX). - Deep learning frameworks would be able to integrate with many hardware vendors by using only a single interface. - Hardware vendors would be able to implement only one interface and get integration with many deep learning frameworks. ## Design Choices - Interface must use only highly portable aspects of C ABI. - Neural network graphs are passed as serialized ONNX ModelProto messages. To avoid serialization overhead, weights can be passed as raw memory blobs. - Input and output tensors are allocated by the caller and use NCHW layout. - Intermediate tensors are allocated by the vendor implementation, and can use any layout. - Backends (software implementations and hardware accelerators) are discovered, selected, and initialized on-demand in run-time. Multiple backends can be used in the same application simultaneously. - There is no minimal set of ONNX operators to implement. The implementer and the user (a deep learning framework) of the API decide which operators can and will be offloaded in runtime. - The proposal includes the minimal functionality to let deep learning frameworks and vendor libraries work together. Several extension mechanisms can be used for more efficient vendor- or platform-specific functionality. ## Proposed Interface We propose a small C-based API, which includes the following functionality: * Discover (`onnxGetNumBackends`) and query information (`onnxGetBackendInfo`) about high-performance backends * Initialize (`onnxInitBackend`) and deinitialize (`onnxReleaseBackend`) high-performance backends * Query if a backend supports an ONNX operator with particular parameters and input shapes (`onnxGetBackendCompatibility`) * Convert an ONNX graph to opaque vendor-specific representation of a backend (`onnxInitGraph`) * Specify memory locations and metadata about graph inputs and outputs (`onnxSetGraphIO`) * Run an ONNX graph, converted to vendor-specific representation (`onnxRunGraph`) * Release the vendor-specific representation of a graph and associated resources (`onnxReleaseGraph`) ## General Use Pattern for Deep Learning Frameworks 1. The user (deep learning framework) iterates operators in a model graph one-by-one, convert them to ONNX, and calls `onnxGetBackendCompatibility` to check which of the operators can be offloaded to the backend. 2. The user constructs connected subgraphs of operators that can be offloaded to the backend. 3. (Optional) For each subgraph, the user estimates if it is beneficial to offload it to the optimized backend: a. The user queries the backend about it high-level performance characteristics using `ONNX_BACKEND_MACS_*` and `ONNX_BACKEND_MEMORY_BANDWIDTH` information queries. These data let the user build a simple roofline model of backend performance. b. For every subgraph the user estimates time to do inference using the roofline model. c. The user additionally estimates time to transfer subgraph inputs to the backend using `ONNX_BACKEND_CPU_MEMORY_READ_BANDWIDTH` information query and to transfer subgraph outputs from the backend using `ONNX_BACKEND_CPU_MEMORY_WRITE_BANDWIDTH`. d. If predicted time to transfer inputs to the backend, do inference, and transfer outputs from the backend exceeds predicted time to do the inference on default engine (e.g. CPU), the user falls back to a different ONNX backend, or to the default engine. 4. The user initialized the backend, and offloads the subgraph execution to the ONNX backend by calling `onnxInitGraph`, `onnxSetGraphIO` and `onnxRunGraph` ## Implementation Notes ### Backend object Backend is a combination of software library and hardware device. The same device (e.g. "NVIDIA Tesla P100 on CUDA index #0" accessed though different software libraries would be seen as different backends. A single software library can expose multiple backends, one per device (e.g. each CUDA GPU in a system is exposed as a separate backend, or CPU, GPU, and DSP on a mobile chipset are exposed as three different backends). We recommend that vendors make the backend object reference-counted, and use `uint32_t magic` as the first data field of the object: ```c struct MyBackend { uint32_t magic; uint64_t referenceCount; ... }; /* This line won't compile, but gives you an idea of relation between MyBackend structure and onnxBackend type. */ typedef MyBackend* onnxBackend; ``` Magic is an arbitrary 32-bit integer unique for a library implementing the API. It should be used to verify that the backend object passed to `onnxInitGraph` was created by `onnxInitBackend` in the same library. ### Graph object Graph object is a vendor-specific representation of ONNX ModelProto message. Graph is logically related to the backend used to create it, and a typical implementation of a graph object would hold a reference to its backend object. We recommend that vendors use `uint32_t magic` as the first data field of the graph object: ```c struct MyGraph { uint32_t magic; struct MyBackend* backend; ... }; /* This line won't compile, but gives you an idea of relation between MyGraph structure and onnxGraph type. */ typedef MyGraph* onnxGraph; ``` Magic is an arbitrary 32-bit integer unique for a library implementing the API. It should be used to verify that the backend object passed to `onnxInitGraph` was created by `onnxInitBackend` in the same library. Magic for a graph object should be different from magic of a backend object of the same library. ### Library initialization During one-time library initialization, the implementation of the API would detect `n` supported devices and map them to backend indices in `0...(n-1)` range. The implementation of device discovery and checking required device characteristics is highly vendor- and platform-specific, e.g.: - A CPU implementation may always expose 1 device. - A CUDA-based implementation may call `cudaGetDeviceCount` to get the number of CUDA-enabled devices, then call `cudaGetDeviceProperties` for each device, and map CUDA devices which satisfy the minimum required functionality, such as compute capability, to backend indices. - An OpenCL-based implementation for a mobile GPU would try to load OpenCL library, call `clGetPlatformIDs` and `clGetPlatformInfo` to find a supported platform, then call `clGetDeviceIDs` and `clGetDeviceInfo` to find a supported GPU device, and map it to the only exposed backend if such device exists, or expose 0 devices otherwise. - An implementation for hardware neural network accelerators would call vendor-specific driver API to discover accelerator devices installed in the system and map them to backend indices. We recommend that library initialization is triggered on the first call to `onnxGetNumBackends`, `onnxGetBackendInfo`, or `onnxInitBackend`. Using a global static C++ object for initialization may hurt portability if library initialization involves loading other shared libraries (DLLs): on Windows `LoadLibrary` function can't be used in initializers of global static objects. ### onnxGetNumBackends Implementation would [initialize the library](#library-initialization), if it wasn't initialized already, and return the number `n` of available backends. ### onnxGetBackendInfo Implementation would [initialize the library](#library-initialization), if it wasn't initialized already, and query information about the backend using vendor- or platform-specific API (e.g. `cudaGetDeviceProperties`, `clGetDeviceInfo`, CPUID instruction). Implementation can cache this information when it is first queried or during initialization, and return the cached value. onnx-onnx-bca0315/docs/proposals/ONNXMultiDeviceProposal.md000066400000000000000000000140641511334557700240250ustar00rootroot00000000000000 # ONNX Multi-Device Proposal ## Background The recent trend in increasingly larger models has spurred an interest in distributed inference. A key performance bottleneck for inference for these large models has been the memory limits of GPUs and other accelerators as well as communication bandwidth. Thus, efficient distributed inference typically requires parallelization of the computation across multiple devices taking memory and bandwidth into account. Our goal is to extend ONNX so that it can serve as a representation of a parallelized model. This is driven by the current state-of-the-art techniques used for distributed inference (eg., see [GSPMD: General and Scalable Parallelization for ML Computation Graphs](https://arxiv.org/pdf/2105.04663.pdf)). In particular, two techniques of interest are tensor parallelism and pipelining. In tensor parallelism (also known as horizontal parallelism or operator parallelism), the computation of a single operator (node) in the graph is parallelized across multiple devices by sharding its inputs, In pipeline parallelism, different subgraphs are assigned to different devices. ## Design See [this commit](https://github.com/kevinch-nv/onnx/commit/07e97452096b28ba7c46fec6927d195907431e07) for the proposed additions to the ONNX spec. The key point of this design is that all multi-device specific annotations are at the node level, and do not affect the main computational graph. This means: - All communication operations required for multi-device execution are implicit - A backend may choose to ignore the annotations if the provided configurations are either not supported or not available ### Sharding Specification Sharding refers to modifying a tensor into multiple parts to be sent across multiple devices. A tensor may be sharded across any of its axis. Modification of a tensor generally falls into two categories: splitting and duplication. A formal description of the sharding rules can be found [here](ShardingFormalism.md). #### Sharding as a Split For example, consider the following 2x2 tensor: `[[1, 2], [3, 4]]` If a sharding across axis 0 is specified over two devices, then: - Device 0 will receive a tensor of shape 1x2 with data `[[1, 2]]` - Device 1 will receive a tensor of shape 1x2 with data `[[3, 4]]` The corresponding ShardingSpecProto for the above will look like: ``` { device = [0, 1] sharded_dim =[ { axis = 0 simple_sharding = [ { num_shards = 2 } ] } ] } ``` If a sharding across axis 1 is specified over two devices, then: - Device 0 will receive a tensor of shape 2x1 with data `[[1], [3]]` - Device 1 will receive a tensor of shape 2x1 with data `[[2], [4]]` The corresponding ShardingSpecProto for the above will look like: ``` { device = [0, 1] sharded_dim =[ { axis = 1 simple_sharding = [ { num_shards = 2 } ] } ] } ``` If a sharding across axis 0 and axis 1 is specified over four devices, then: - Device 0 will receive a tensor of shape 1x1 with data `[[1]]` - Device 1 will receive a tensor of shape 1x1 with data `[[2]]` - Device 2 will receive a tensor of shape 1x1 with data `[[3]]` - Device 3 will receive a tensor of shape 1x1 with data `[[4]]` The corresponding ShardingSpecProto for the above will look like: ``` { device = [0, 1, 2, 3] sharded_dim =[ { axis = 0 simple_sharding = [ { num_shards = 2 } ] } { axis = 1 simple_sharding = [ { num_shards = 2 } ] } ] } ``` A key observation in the above example shows how indexing is performed when multiple sharding axes are provided. In general, the splitting is done as: ``` split_tensors = [] for a in range(num_shards_a): a_width = input.shape[axis0] / num_shards_a a_index = a * a_width for b in range(num_shards_b): b_width = input.shape[axis1] / num_shards_b b_index = b * b_width split = input[a_index : a_index + a_width, b_index : b_index + b_width] split_tensors.append(split) ``` Note that the above examples assume that the num_shards are evenly divisible into the axis that's being sharded. While this is not a hard restriction, it is up to the backend on how to handle non-evenly divisble cases. #### Sharding as a Broadcast There may be cases where data in a tensor must be duplicated across multiple devices to ensure that operations stay functionally correct. For example consider replicating the same 2x2 tensor across two devices. We can do so by providing the following ShardingSpecProto: ``` { device = [-1] // keys into device_map device_map = {-1: [0, 1]} sharded_dim =[] } ``` It is also possible to mix splitting and broadcasting, consider the following ShardingSpecProto: ``` { device = [-1, -2] // keys into device_map device_map = {-1: [0, 1], -2: [2, 3]} sharded_dim =[ { axis = 0 simple_sharding = [ { num_shards = 2 } ] } ] } ``` On device 0 and 1, the following 1x2 tensor is produced: `[[1,2]]` On device 2 and 3, the following 1x2 tensor is produced: `[[2,3]]` #### Pipeline Parallelism Pipeline stages are represented as an optional integer value in a node's NodeConfigurationProto. It is a hint to the backend on how to run a model in a pipelined fashion across multiple devices. For example, consider the following diagram: ``` Nodes below have a pipeline id of 1: A -> B -> C -> D -> E | Nodes below have a pipeline id of 2: F -> G -> H -> I -> J -> K ``` It is possible to have both pipeline and tensor parallel annotations in the same ONNX graph. onnx-onnx-bca0315/docs/proposals/ShardingFormalism.md000066400000000000000000000276531511334557700230110ustar00rootroot00000000000000 # Sharding Formalism In this section, we address the following aspects of a sharding specification: the semantics of a sharding specification, checking a sharding specification for validity, and inferring a complete sharding specification given a partial one. **Semantics of the sharding spec**: We start with an informal description of the intended behavior of a sharding spec. Operationally, the execution of an annotated node proceeds as below: first, the input data is partitioned or repartitioned, as necessary, to ensure that it is in the sharded form specified in the node. This potentially involves communication operations among the different devices. Next, a parallelized implementation of the operation is applied to the sharded data. Finally, the output is produced in the sharded form specified in the node. This too may involve the use of communication collective ops. **Validity of a sharding spec**: Note that not all input sharding specs make sense. For example, consider the addition operator `Add(A,B)`, where both inputs are two dimensional tensors of shapes `[32, 1024]`. Sharding the first input between two devices along axis 0 and the second input between the same two devices along axis 1 does not make sense. In fact, we typically expect both inputs to be sharded the same way. A sharding-checker to check if a given input sharding spec makes sense would be useful and we recommend building one. The correctness requirements, however, vary from operator to operator, though they mostly fall into one of a few different groups, described in more detail below. Note that the output sharding spec for a node does not have to be consistent with the input sharding spec of the node. This is useful when we want to reshard the output to be more suitable for the consumers of the output. However, even if a given sharding spec makes sense, a particular implementation may not support it. The implementation should ideally provide feedback to the user indicating this, but may choose to use an alternative impcccccbkvgevnrbllementation or abort. Different users and scenarios may have different requirements (on whether an alternative parallel or sequential implementation is preferable or not.) Thus, a particular implementation may have stricter requirements on the set of sharding specs that it supports. **Inference of missing elements of a sharding spec**: A validity checker can be extended to automatically infer some missing elements of a sharding spec, as we outline below. * If no input sharding spec is provided for a node's input X, it is assumed to be the same as the sharding spec specified for X at the node that produces the value X. * If X is a model input, then X is assumed to be unsharded. If no output sharding spec is provided for a node's output, it is inferred from the node's input sharding spec and the node's operation. In general, this may vary from operator to operator. The inference scheme is outlined for a few core groups of operators below. **Extensions**: Currently, the sharding spec does not allow a way of specifying a sharding for the model inputs. Sharded model inputs could be useful in an execution setting where the model input already exists in sharded form, making it easier to compose sharded execution. Extensions to the sharding spec to enable this is future work. ## Restrictions on Sharding Specs Informally, constraints on sharding follow from parallelizability of the computation along the different axes of the input and output tensors. Often the computation of the output can be expressed in terms of loops (iterations) over the different axes of the input and/or output tensors. If the iteration over a specific axis can be expressed as a parallel loop, sharding along that axis makes sense. If that iteration is a reduction loop, sharding along that axis may still work, but require a subsequent collective (multi-device) reduction after the local reductions on each device. ### Unary elementwise ops List of operations: _Abs, Acos, Acosh, Asin, Asinh, Atan, Atanh, Cast, Ceil, Cos, Cosh, Dropout, Erf, Exp, Floor, Identity, IsInf, IsNaN, Log, Max, Min, Neg, Not, Reciprocal, Round, Sigmoid, Sign, Sin, Sinh, Tan, Tanh, ConstantOfShape_. **Constraints on input sharding** * No constraints on input sharding. **Inference of output sharding** * If not specified, the output sharding is the same as input sharding ### Broadcast n-ary elementwise ops List of operations: _Add, And, BitShift, BitwiseAnd, BitwiseNot, BitwiseOr, BitwiseXor, Equal, Greater, Less, Mod, Mul, Or, Pow, Sub, Sum, Where, Xor_. **Constraints on input sharding** * For any non-broadcast axis, the sharding spec of the two (or more) inputs must be identical * Any broadcast axis of size 1 (in the unsharded original tensor) must be replicated across all devices that participate in the parallel computation (that is, all devices identified in the node's sharding spec). * The case where there are two or more broadcast axes is more involved. Some conditions must be satisfied to ensure that the natural output (without extra communication ops) has a proper (complete) sharding. The constraint is that the sharding specs of the multiple broadcast axes must be *composable*, which is illustrated down below. **Inference of output sharding** * The sharding spec for any axis of the output is the same as the sharding spec for the corresponding input axes in the case of non-broadcast. * In the case of a single broadcast axis, the output axis derives the sharding spec from the corresponding input axes with a size other than 1, if any. * In the special case where all corresponding input axes have a size of 1, the output axis inherits the same sharding (that is, replicated across all devices of the node op). * In the case of two or more broadcast axes, the output axis derives the sharding spec from the corresponding input axes with a size other than 1, if any. However, the device assignment is inferred by composing the sharding specs of all broadcast axes (where each output shard resides in the intersection of the sets of devices that contain the corresponding input shards used to compute that output shard). See below for an illustration of this. **Composing Sharding Specs on Different Axes** Consider the example of an `Add (Input1, Input2)` op. Consider the case where `Input1` has shape `[M, 1]` and `Input2` has shape `[1, N]`. The output has shape `[M, N]`, as a result of broadcasting. The figure below shows how we can use sharding for both the `M` and `N` axes: ![Composing sharding specs on different axes](images/composing_broadcast_axes.png) Note that in this example, both the `M` and `N` axes are split into two shards each. This means that the output itself has 4 shards, as shown in the figure. In this example, we want each output-shard to be on one device, as described by the sharding spec ```python { device = [0, 1, 2, 3] sharded_dim =[ { axis = 0 simple_sharding = [ { num_shards = 2 } ] } { axis = 1 simple_sharding = [ { num_shards = 2 } ] } ] } ``` To produce this output, however, we need to ensure that the input-shards are each available in two devices each, as shown in the figure above. In particular, the first shard of `Input1` is needed by both devices 0 and 1, as it is used to compute the first two output shards. Likewise, the first shard of `Input2` is needed by both devices 0 and 2. Thus, the sharding spec for `Input1` is as below: ```python { device = [-1, -2] // keys into device_map device_map = {-1: [0, 1], -2: [2, 3]} sharded_dim =[ { axis = 0 simple_sharding = [ { num_shards = 2 } ] } ] } ``` The sharding spec for `Input2` is analogous, as explained and shown in figure above. This leads to the following constraint for input-sharding and inference rule for output-sharding in the presence of two broadcast axes: * The (inferred) devices for `output-shard[i,j]` is the intersection of the set of devices for `input-1-shard[i]` and `input-2-shard[j]`. If this set is empty, then the input sharding specs are not compatible (for broadcast composition). This rule is extended to the case of more than two broadcast axes accordingly. ### Reduction ops **Constraints on input sharding** * No constraints on input sharding. * Sharding along non-reduction axes is straightforward. It indicates parallelization of the iteration over the non-reduction axes. * Sharding along reduction axes is permitted. It indicates parallelization of the reduction loop, but this involves performing the reduction in two steps. In the first step, the reduction is done locally on the shard, and in the second step the reduction is done across the different shards. This can be typically mapped to a collective-reduce operation. **Inference of output sharding** * Non-reduction axes inherit the sharding of the corresponding axes of the input. * Since the size of the reduction axis is one after the reduction, it can't be used for any meaningful sharding. The axis may even be omitted from the output shape, depending on the value of the attribute `keep_dims`. If the axis is retained, it is treated as having no sharding. In the case where the inputs are only sharded along one or more reduction axes, there will be no sharded axis in the inferred output sharding specification. However, there is still a choice as to whether the computed output is replicated on all the devices that participate in this operation, or whether it is stored only in some distinguished node. Collective-reduce operations typically support both variations. The default inferred output specification is to broadcast the computed result to all devices that participate in the particular reduction (the first option). ### MatMul-like ops List of operations: MatMul, Gemm, quantized variations of these ops, special cases of EinSum The constraints for these ops follow analogous cases above. Consider the simple case of matrix multiplication of two matrices of dimensions `[M, K]` and `[K, N]` producing an output matrix of dimension `[M, N]`. This operation is essentially a broadcast-reduction operation, where the first input is interpreted to have the shape `[M, K, 1]` and the second input is interpreted to have the shape `[1, K, N]`, and we perform a broadcast element-wise multiplication, followed by a reduce-sum along the `K` axis. The constraints and inference for the operation follows from the corresponding rules for broadcast and reduction described above. Axis 0 of the first input (with value `M`) is conceptually broadcast to the second input. Hence, its constraints and handling are similar to the treatment of broadcast axes for n-ary elementwise ops. Specifically, since only the first input has this axis, the partitioning of this axis is not constrained by the partitioning of the second input. Furthermore, the output matrix will inherit the partitioning for the corresponding axis from the partitioning of axis 0 of the first input. Axis 1 of the second input (with value `N`) is also handled similarly. The two axes with size value (the _reduction_ axes) are both required to have the same sharding (similar to non-broadcast axes in a binary operation above). The output device assignment follows the rules described above for broadcast axes. ### Unsupported ops The following ops are not supported in this version: * Operations on sequences and optional values. * Control-flow ops, such as _If, Loop, Scan_. * _GRU, LSTM, RNN, DFT, STFT, MelWeightMatrix, TfidVectorizer_ * Convolution / Pooling ops, such as: * _AveragePool, GlobalAveragePool, GlobalLpPool, GlobalMaxPool, LpPool, MaxPool, MaxRoiPool,_ * _Conv, ConvInteger, ConvTranspose, DeformConv,_ * _InstanceNorm, LpNormalization, LayerNormalization_ onnx-onnx-bca0315/docs/proposals/SymbolicShapeInfProposal.md000066400000000000000000000261031511334557700243040ustar00rootroot00000000000000 # Proposal - Symbolic Shape Inference And Partial Data Propagation *Note: This proposal was accepted and implemented in ONNX 1.10. Following PRs implemented this proposal: 3518, 3551, 3593, 3580* ## Introduction ONNX provides an implementation of shape inference on ONNX graphs. Shape inference is computed using the operator level shape inference functions. The inferred shape of an operator is used to get the shape information without having to launch the model in a session. Such static shape inference can be used to catch obvious errors before runtime, eliminate run-time checks which are otherwise guaranteed to pass, improve static memory planning and improve model visualization experience. For pytorch exporter and compiler-based execution providers like Nuphar, shape inference is required (rank inference is minimum requirement), and they cannot work with unknown shapes. This document explains the limitations of shape inference and lays out a proposal for addressing these limitations. ## Current onnx shape inference limitations (Pre ONNX 1.10) Today, ONNX shape inference is not guaranteed to be complete. Wherever possible we fall back to rank inference however, there are scenarios when rank inference is not possible either. Here are the various limitations which block the completion of shape inference: 1. Some dynamic behaviors block the flow of shape inference, and the shape inference stops. For example, reshape to a dynamically computed shape. 2. Shape inference works only with constants and simple variables. It does not support arithmetic expressions containing variables nor does it support symbol generation. For example, concatenation on tensors of shapes (5, 2) and (7, 2) can be inferred to produce a result of shape (12, 2), but concatenation on tensors of shapes (5, 2) and (N, 2) will simply produce (?, 2), where “?” represents a dimension with neither dim value nor dim param, rather than containing a representation of N+5 or generating a new symbol (M, 2). In such scenarios shape propagation stops. 3. All operators are not required to have a shape inference implementation. When such an op is encountered the shape inference stops. There are also cases when rank inference is not done as a fallback mechanism. (Note: We are working on an ongoing basis to identify and fix such issues. The current document does not focus on this limitation) ## Goals and Non-Goals Our **goal** is to fix the shape inference gap in scenarios where: * Shape computations are done in branches (refer to limitation 1) * Symbolic dimensions are present (refer to limitation 2) By fixing these gaps we aim to: * Unblock pytorch exporter from exporting models when exporting stops because of absence of shape information. * Improve static memory planning in the runtimes. * Enable pre-allocating output buffers outside of the runtimes so that its lifetime can be managed by the caller itself. ### Non-goals * Add symbolic expressions to ONNX standard: This is not necessary for accomplishing our goals. There are advantages to having this capability, for example this can significantly reduce the number of symbols introduced and it can also provide more deterministic shape calculations in certain special cases. However, the tradeoff is the added complexity. So, at this point we are not considering it. This can be considered in future iterations. * Enable data computation and propagation for older operator sets. (details in the proposal section) Note: This work will benefit Nuphar as well but right now there is no plan to move Nuphar to use this solution. ## Terminology Shape inference can be broken into 2 parts: * Node level shape inference: This refers to operator specific shape inference functions. They are defined with the operator schema itself. * Graph-level shape inference: This refers to the higher-level logic which walks through the entire graph, gets the inferred shape from node level shape inference functions and then makes decisions on merging these inferred shapes with existing shapes so that they are available for downstream nodes. ## Proposal Extend current shape inference to allow: * Symbol generation and propagation * Partial data computation and propagation * Extend shape op to generate slice of the shape to facilitate simplifying shape computations. ## Extend shape inference ### Symbol generation and propagation Extend graph level shape inference to maintain a graph level view of symbols and generate new symbols where necessary. This will enable us to continue the shape inference of the downstream nodes. Example: For an op like “Concat” if its inputs have shapes “[M]” and “[N]” current shape-inference returns “[?]” where “?” is to indicate a dimension with neither dim-value nor dim-param set. Now, suppose the output X of “Concat” is input to a unary-op Op1() whose output Y is then input to another unary-op Op2() whose output is Z, etc. The shape “[?]” is propagated further. We infer that Y and Z have shape “[?]”. However, we do not infer that X, Y, and Z have the same shape because two “?” cannot be considered equal. Per the current proposal, “[?]” in inferred shapes will be replaced by a new unique symbol by the graph level shape inference so the downstream nodes can use the symbolic shapes to carry out shape inference. In the current example, “Concat” will produce “[?]” as the shape which will then be replaced by “[K]”, then subsequent shape inference will infer that X, Y, and Z all have the same shape “[K]”. Runtimes can use this information to reuse memory for these tensors. ### Partial data computation and propagation When shape inputs are computed dynamically, shape inference post a reshape node stops. This can be prevented by making this data available to the reshape node during shape inference. We propose computation and propagation of data for operators which are used in shape computation. It is called “partial” data computation and propagation because this will only be done for shape computations. It is not meant to be a full-fledged kernel for the operator. For the same reasons data computations will be implemented for a limited set of operators. While we will increase the coverage in the future iterations it is important to note that for some operators like LSTM, convolution ops, pooling ops etc. data propagation function will never be added because such ops are not used in shape computations. The following operators will be picked in the first phase. (These operators are generally used for shape computations.) | Ops | | --------| | Add | | Sub | | Mul | | Cast | | Concat | | Gather | | Reshape | | Shape | | Slice | | Size | | Squeeze | | UnSqueeze | The OpSchema class will be extended to include an optional “PartialDataPropagationFunction” like the existing TypeAndShapeInferenceFunction. This function will provide data computation for the operators which will then be propagated to the downstream operators by the graph level shape inference. PartialDataPropagationFunction will be called by the graph level shape inference after TypeAndShapeInference runs for the node because the output shape is required for partial data computation. A new interface "DataPropagationContext” will be added to allow PartialDataPropagationFunction to access all the information required to propagate shape data for the given node and allow writing of the computed data. Example: ``` using DataPropagationFunction = std::function class OpSchema final { public: . . . OpSchema& PartialDataPropagationFunction(DataPropagationFunction dataPropagationFunction)  {    partial_data_propagation_function_ = std::move(dataPropagationFunction);    return *this; } DataPropagationFunction GetDataPropagationFunction() const {     return partial_data_propagation_function_ ? partial_data_propagation_function_ : dummyDataPropagator; } } // Operator schema example ONNX_OPERATOR_SET_SCHEMA(     Shape,     13,     OpSchema()         .SetDoc(“”)         .Input(0, "data", "An input tensor.", "T", . . .)         .Output(0, "shape", "Shape of the input tensor", "T1", . . .)         .TypeConstraint("T", OpSchema::all_tensor_types())         .TypeConstraint("T1", {"tensor(int64)"})         .TypeAndShapeInferenceFunction([](InferenceContext& ctx) { . . .         })         .PartialDataPropagationFunction([](DataPropagationContext& ctx) { TensorShapeProto tp; // compute output data for shape operator // add computed data to DataPropagationContext for propagating it downstream           ctx.addOutputData(0, std::move(tp));         })); ``` The symbol generation will happen at the graph level shape inference, therefore all the models (older opsets as well as the latest opset versions) can benefit from this enhancement. However, the data computation and propagation are tied to the OpScehma and will happen at node level. To begin with these functions will only be added to the latest op schemas. Older schemas can be extended to support data computation later, on a case by case basis to support some high priority scenarios. What this means is that older opset models will not benefit from shape inference improvements because of this enhancement. ## Special Cases This section considers some edge cases and proposes a solution to handle them. ### Broadcasting with symbolic dims If we have a broadcast between two unknown dimensions “M” and “N” we cannot infer that both M and N should have the same value. The runtime semantics allows for one of the two symbols to have the value 1 and the other to have a value different from 1. So, merging M and N and treating them as the same value is potentially unsound. In this case, a new symbol will be generated for the output shape and the shape inference will continue. ### Inferred shape does not match output shape Inferred and existing shapes can be mismatched. Although failing shape inference in such cases seems like the correct approach it may not always be practical. By default, shape inference will fail when such a case is encountered however callers will have an option to override existing types with inferred types. When this option is enabled, shape inference will continue with the inferred type. ### Handling symbolic dimensions with data propagation When the shape contains symbolic dimensions, we try and propagate them downstream, however in cases where some arithmetic operations are performed on these symbolic dims we create new symbols and propagate them instead. ### Output shape is dependent on input data There are certain nodes like NonZero where the output shape depends on the input data. In this case it is not possible to infer the shape completely hence a new symbolic shape will be created using the inferred rank and shape inference will continue. onnx-onnx-bca0315/docs/proposals/images/000077500000000000000000000000001511334557700203065ustar00rootroot00000000000000onnx-onnx-bca0315/docs/proposals/images/composing_broadcast_axes.png000066400000000000000000000153671511334557700260700ustar00rootroot00000000000000‰PNG  IHDR”˛1/¸ÅzTXtRaw profile type exifxÚmP[à ûį;B^ĨÉq`mĨŨ`Į_ ŠÔVŗ„ãbjBĘūũå5@(E–UĢÕ 11j.m2‚Lž0I…÷ũŌ)å¨ė•ÃĐĪũüáŦØ\-— }§Ņī†ITŌGP^ÄŖ#rąeeS˜-žÕtŊ>Ąīp‡Æ*ƒ:ŒCËÏoY}zÛâ÷0ŅÎČāĖŦŅ%…›8Yũ rœŲ‰äߜN”–1ZÖ\ũƃiCCPICC profilexœ}‘=HÃ@Å_͊ĸ‡vuČPėĸ"ŽĨŠE°PÚ ­:˜\úM’GÁĩāāĮbÕÁÅYWWAüqvpRt‘˙—ZÄxp܏w÷wīĄYeĒˆĒféD\ĖåWÅŪW  €Q‰™z2ŗ˜…įøē‡¯wQžå}îĪ1¨LøDâĶ ‹xƒxvĶŌ9ī‡YYRˆĪ‰' ē ņ#×e—ß8—xfØČĻį‰ÃÄbŠ‹å.feC%ž!Ž(ĒFųBÎe…ķgĩZgí{ō ÚJ†ë4ĮĀ’HA„Œ:*¨ÂB”Viڏ{øGŠ\2š*`äX@ *$Įūŋģ5‹ĶSnR0ôŧØöĮ8Đģ ´ļũ}lÛ­Ā˙ \i­ Ė}’Ūčh‘#`h¸¸îhōpš ?é’!9’ŸĻP,īgôMy t ô¯šŊĩ÷qúdŠĢåāā˜(QöēĮģûē{û÷LģŋVWr›XR%æ viTXtXML:com.adobe.xmp 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/cô,šIENDŽB`‚onnx-onnx-bca0315/examples/000077500000000000000000000000001511334557700157055ustar00rootroot00000000000000onnx-onnx-bca0315/examples/Protobufs.ipynb000066400000000000000000000401721511334557700207370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:50.826940Z", "iopub.status.busy": "2023-04-04T17:07:50.826940Z", "iopub.status.idle": "2023-04-04T17:07:51.182950Z", "shell.execute_reply": "2023-04-04T17:07:51.182950Z" } }, "outputs": [], "source": [ "from onnx import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.186951Z", "iopub.status.busy": "2023-04-04T17:07:51.186951Z", "iopub.status.idle": "2023-04-04T17:07:51.309954Z", "shell.execute_reply": "2023-04-04T17:07:51.308951Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int attribute:\n", "\n", "name: \"this_is_an_int\"\n", "i: 1701\n", "type: INT\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# Int Attibute\n", "arg = helper.make_attribute(\"this_is_an_int\", 1701)\n", "print(\"\\nInt attribute:\\n\")\n", "print(arg)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.334952Z", "iopub.status.busy": "2023-04-04T17:07:51.334952Z", "iopub.status.idle": "2023-04-04T17:07:51.450951Z", "shell.execute_reply": "2023-04-04T17:07:51.449950Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Float attribute:\n", "\n", "name: \"this_is_a_float\"\n", "f: 3.140000104904175\n", "type: FLOAT\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# Float Attribute\n", "arg = helper.make_attribute(\"this_is_a_float\", 3.14)\n", "print(\"\\nFloat attribute:\\n\")\n", "print(arg)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.453950Z", "iopub.status.busy": "2023-04-04T17:07:51.453950Z", "iopub.status.idle": "2023-04-04T17:07:51.555948Z", "shell.execute_reply": "2023-04-04T17:07:51.555948Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "String attribute:\n", "\n", "name: \"this_is_a_string\"\n", "s: \"string_content\"\n", "type: STRING\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# String Attribute\n", "arg = helper.make_attribute(\"this_is_a_string\", \"string_content\")\n", "print(\"\\nString attribute:\\n\")\n", "print(arg)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.558950Z", "iopub.status.busy": "2023-04-04T17:07:51.558950Z", "iopub.status.idle": "2023-04-04T17:07:51.662949Z", "shell.execute_reply": "2023-04-04T17:07:51.662949Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Repeated int attribute:\n", "\n", "name: \"this_is_a_repeated_int\"\n", "ints: 1\n", "ints: 2\n", "ints: 3\n", "ints: 4\n", "type: INTS\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# Repeated Attribute\n", "arg = helper.make_attribute(\"this_is_a_repeated_int\", [1, 2, 3, 4])\n", "print(\"\\nRepeated int attribute:\\n\")\n", "print(arg)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.665950Z", "iopub.status.busy": "2023-04-04T17:07:51.665950Z", "iopub.status.idle": "2023-04-04T17:07:51.774949Z", "shell.execute_reply": "2023-04-04T17:07:51.774949Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "NodeProto:\n", "\n", "input: \"X\"\n", "output: \"Y\"\n", "op_type: \"Relu\"\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# node\n", "node_proto = helper.make_node(\"Relu\", [\"X\"], [\"Y\"])\n", "\n", "print(\"\\nNodeProto:\\n\")\n", "print(node_proto)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.778952Z", "iopub.status.busy": "2023-04-04T17:07:51.777951Z", "iopub.status.idle": "2023-04-04T17:07:51.883948Z", "shell.execute_reply": "2023-04-04T17:07:51.883948Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "NodeProto:\n", "\n", "input: \"X\"\n", "input: \"W\"\n", "input: \"B\"\n", "output: \"Y\"\n", "op_type: \"Conv\"\n", "attribute {\n", " name: \"kernel\"\n", " i: 3\n", " type: INT\n", "}\n", "attribute {\n", " name: \"pad\"\n", " i: 1\n", " type: INT\n", "}\n", "attribute {\n", " name: \"stride\"\n", " i: 1\n", " type: INT\n", "}\n", "\n", "\n", "More Readable NodeProto (no args yet):\n", "\n", "%Y = Conv[kernel = 3, pad = 1, stride = 1](%X, %W, %B)\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# node with args\n", "node_proto = helper.make_node(\n", " \"Conv\", [\"X\", \"W\", \"B\"], [\"Y\"],\n", " kernel=3, stride=1, pad=1)\n", "\n", "# This is just for making the attributes to be printed in order\n", "node_proto.attribute.sort(key=lambda attr: attr.name)\n", "print(\"\\nNodeProto:\\n\")\n", "print(node_proto)\n", "\n", "print(\"\\nMore Readable NodeProto (no args yet):\\n\")\n", "print(helper.printable_node(node_proto))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.886948Z", "iopub.status.busy": "2023-04-04T17:07:51.886948Z", "iopub.status.idle": "2023-04-04T17:07:51.992949Z", "shell.execute_reply": "2023-04-04T17:07:51.992949Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "graph proto:\n", "\n", "node {\n", " input: \"X\"\n", " input: \"W1\"\n", " input: \"B1\"\n", " output: \"H1\"\n", " op_type: \"FC\"\n", "}\n", "node {\n", " input: \"H1\"\n", " output: \"R1\"\n", " op_type: \"Relu\"\n", "}\n", "node {\n", " input: \"R1\"\n", " input: \"W2\"\n", " input: \"B2\"\n", " output: \"Y\"\n", " op_type: \"FC\"\n", "}\n", "name: \"MLP\"\n", "input {\n", " name: \"X\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "input {\n", " name: \"W1\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "input {\n", " name: \"B1\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "input {\n", " name: \"W2\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "input {\n", " name: \"B2\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "output {\n", " name: \"Y\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "\n", "More Readable GraphProto:\n", "\n", "graph MLP (\n", " %X[FLOAT, 1]\n", " %W1[FLOAT, 1]\n", " %B1[FLOAT, 1]\n", " %W2[FLOAT, 1]\n", " %B2[FLOAT, 1]\n", ") {\n", " %H1 = FC(%X, %W1, %B1)\n", " %R1 = Relu(%H1)\n", " %Y = FC(%R1, %W2, %B2)\n", " return %Y\n", "}\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# graph\n", "graph_proto = helper.make_graph(\n", " [\n", " helper.make_node(\"FC\", [\"X\", \"W1\", \"B1\"], [\"H1\"]),\n", " helper.make_node(\"Relu\", [\"H1\"], [\"R1\"]),\n", " helper.make_node(\"FC\", [\"R1\", \"W2\", \"B2\"], [\"Y\"]),\n", " ],\n", " \"MLP\",\n", " [\n", " helper.make_tensor_value_info(\"X\" , TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"W1\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"B1\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"W2\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"B2\", TensorProto.FLOAT, [1]),\n", " ],\n", " [\n", " helper.make_tensor_value_info(\"Y\", TensorProto.FLOAT, [1]),\n", " ]\n", ")\n", "\n", "print(\"\\ngraph proto:\\n\")\n", "print(graph_proto)\n", "\n", "print(\"\\nMore Readable GraphProto:\\n\")\n", "print(helper.printable_graph(graph_proto))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-04-04T17:07:51.995950Z", "iopub.status.busy": "2023-04-04T17:07:51.995950Z", "iopub.status.idle": "2023-04-04T17:07:52.102950Z", "shell.execute_reply": "2023-04-04T17:07:52.102950Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "NodeProto that contains a graph:\n", "\n", "input: \"Input\"\n", "input: \"W1\"\n", "input: \"B1\"\n", "input: \"W2\"\n", "input: \"B2\"\n", "output: \"Output\"\n", "op_type: \"graph\"\n", "attribute {\n", " name: \"graph\"\n", " graphs {\n", " node {\n", " input: \"X\"\n", " input: \"W1\"\n", " input: \"B1\"\n", " output: \"H1\"\n", " op_type: \"FC\"\n", " }\n", " node {\n", " input: \"H1\"\n", " output: \"R1\"\n", " op_type: \"Relu\"\n", " }\n", " node {\n", " input: \"R1\"\n", " input: \"W2\"\n", " input: \"B2\"\n", " output: \"Y\"\n", " op_type: \"FC\"\n", " }\n", " name: \"MLP\"\n", " input {\n", " name: \"X\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " input {\n", " name: \"W1\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " input {\n", " name: \"B1\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " input {\n", " name: \"W2\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " input {\n", " name: \"B2\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " output {\n", " name: \"Y\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " }\n", " }\n", " }\n", " }\n", " }\n", " type: GRAPHS\n", "}\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "# An node that is also a graph\n", "graph_proto = helper.make_graph(\n", " [\n", " helper.make_node(\"FC\", [\"X\", \"W1\", \"B1\"], [\"H1\"]),\n", " helper.make_node(\"Relu\", [\"H1\"], [\"R1\"]),\n", " helper.make_node(\"FC\", [\"R1\", \"W2\", \"B2\"], [\"Y\"]),\n", " ],\n", " \"MLP\",\n", " [\n", " helper.make_tensor_value_info(\"X\" , TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"W1\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"B1\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"W2\", TensorProto.FLOAT, [1]),\n", " helper.make_tensor_value_info(\"B2\", TensorProto.FLOAT, [1]),\n", " ],\n", " [\n", " helper.make_tensor_value_info(\"Y\", TensorProto.FLOAT, [1]),\n", " ]\n", ")\n", "\n", "# output = ThisSpecificgraph([input, w1, b1, w2, b2])\n", "node_proto = helper.make_node(\n", " \"graph\",\n", " [\"Input\", \"W1\", \"B1\", \"W2\", \"B2\"],\n", " [\"Output\"],\n", " graph=[graph_proto],\n", ")\n", "\n", "print(\"\\nNodeProto that contains a graph:\\n\")\n", "print(node_proto)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.11" }, "vscode": { "interpreter": { "hash": "f9fa6017a53cd3e89c2ae5d3938d7461048c25b2aa8e520267fca421440325a1" } } }, "nbformat": 4, "nbformat_minor": 1 } onnx-onnx-bca0315/examples/check_model.ipynb000066400000000000000000000057551511334557700212210ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-03-06T20:16:44.956148Z", "iopub.status.busy": "2023-03-06T20:16:44.956148Z", "iopub.status.idle": "2023-03-06T20:16:45.015963Z", "shell.execute_reply": "2023-03-06T20:16:45.015963Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The model is:\n", "ir_version: 3\n", "producer_name: \"backend-test\"\n", "graph {\n", " node {\n", " input: \"x\"\n", " output: \"y\"\n", " name: \"test\"\n", " op_type: \"Relu\"\n", " }\n", " name: \"SingleRelu\"\n", " input {\n", " name: \"x\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " }\n", " }\n", " }\n", " }\n", " output {\n", " name: \"y\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " }\n", " }\n", " }\n", " }\n", "}\n", "opset_import {\n", " version: 6\n", "}\n", "\n", "The model is valid!\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "import onnx\n", "import os\n", "\n", "\n", "# Preprocessing: load the ONNX model\n", "model_path = os.path.join(\"resources\", \"single_relu.onnx\")\n", "onnx_model = onnx.load(model_path)\n", "\n", "print(\"The model is:\\n{}\".format(onnx_model))\n", "\n", "# Check the model\n", "try:\n", " onnx.checker.check_model(onnx_model)\n", "except onnx.checker.ValidationError as e:\n", " print(\"The model is invalid: %s\" % e)\n", "else:\n", " print(\"The model is valid!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.11" }, "vscode": { "interpreter": { "hash": "f9fa6017a53cd3e89c2ae5d3938d7461048c25b2aa8e520267fca421440325a1" } } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/examples/load_model.ipynb000066400000000000000000000051241511334557700210510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-03-06T20:16:24.630453Z", "iopub.status.busy": "2023-03-06T20:16:24.630453Z", "iopub.status.idle": "2023-03-06T20:16:24.689518Z", "shell.execute_reply": "2023-03-06T20:16:24.689518Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ir_version: 3\n", "producer_name: \"backend-test\"\n", "graph {\n", " node {\n", " input: \"x\"\n", " output: \"y\"\n", " name: \"test\"\n", " op_type: \"Relu\"\n", " }\n", " name: \"SingleRelu\"\n", " input {\n", " name: \"x\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " }\n", " }\n", " }\n", " }\n", " output {\n", " name: \"y\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " }\n", " }\n", " }\n", " }\n", "}\n", "opset_import {\n", " version: 6\n", "}\n", "\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "import onnx\n", "import os\n", "\n", "\n", "# Load the ONNX model\n", "onnx_model = onnx.load(os.path.join(\"resources\", \"single_relu.onnx\"))\n", "print(onnx_model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.11" }, "vscode": { "interpreter": { "hash": "f9fa6017a53cd3e89c2ae5d3938d7461048c25b2aa8e520267fca421440325a1" } } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/examples/make_model.ipynb000066400000000000000000000107731511334557700210550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-03-06T20:16:09.220355Z", "iopub.status.busy": "2023-03-06T20:16:09.219355Z", "iopub.status.idle": "2023-03-06T20:16:09.481504Z", "shell.execute_reply": "2023-03-06T20:16:09.480694Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The producer_name in model: onnx-example\n", "\n", "The graph in model:\n", "node {\n", " input: \"X\"\n", " input: \"Pads\"\n", " output: \"Y\"\n", " op_type: \"Pad\"\n", " attribute {\n", " name: \"mode\"\n", " s: \"constant\"\n", " type: STRING\n", " }\n", "}\n", "name: \"test-model\"\n", "initializer {\n", " dims: 4\n", " data_type: 7\n", " int64_data: 0\n", " int64_data: 0\n", " int64_data: 1\n", " int64_data: 1\n", " name: \"Pads\"\n", "}\n", "input {\n", " name: \"X\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " }\n", " }\n", " }\n", "}\n", "input {\n", " name: \"Pads\"\n", " type {\n", " tensor_type {\n", " elem_type: 7\n", " shape {\n", " dim {\n", " dim_value: 4\n", " }\n", " }\n", " }\n", " }\n", "}\n", "output {\n", " name: \"Y\"\n", " type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 1\n", " }\n", " dim {\n", " dim_value: 4\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "The model is checked!\n" ] } ], "source": [ "# NBVAL_SKIP\n", "# Protobuf 4 and Protobuf 3 might output different order of protobuf fields\n", "\n", "import onnx\n", "from onnx import helper\n", "from onnx import AttributeProto, TensorProto, GraphProto\n", "\n", "\n", "# The protobuf definition can be found here:\n", "# https://github.com/onnx/onnx/blob/main/onnx/onnx.proto\n", "\n", "\n", "# Create one input (ValueInfoProto)\n", "X = helper.make_tensor_value_info(\"X\", TensorProto.FLOAT, [1, 2])\n", "\n", "# Create second input (ValueInfoProto)\n", "Pads = helper.make_tensor_value_info(\"Pads\", TensorProto.INT64, [4])\n", "\n", "# Create one output (ValueInfoProto)\n", "Y = helper.make_tensor_value_info(\"Y\", TensorProto.FLOAT, [1, 4])\n", "\n", "# Create a node (NodeProto)\n", "node_def = helper.make_node(\n", " \"Pad\", # node name\n", " [\"X\", \"Pads\"], # inputs\n", " [\"Y\"], # outputs\n", " mode=\"constant\", # Attributes\n", ")\n", "\n", "# Create the graph (GraphProto)\n", "graph_def = helper.make_graph(\n", " [node_def],\n", " \"test-model\",\n", " [X, Pads],\n", " [Y],\n", " [helper.make_tensor(\"Pads\", TensorProto.INT64, [4,], [0, 0, 1, 1,])],\n", ")\n", "\n", "# Create the model (ModelProto)\n", "model_def = helper.make_model(graph_def,\n", " producer_name=\"onnx-example\")\n", "\n", "print(\"The producer_name in model: {}\\n\".format(model_def.producer_name))\n", "print(\"The graph in model:\\n{}\".format(model_def.graph))\n", "onnx.checker.check_model(model_def)\n", "print(\"The model is checked!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.11 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.11" }, "vscode": { "interpreter": { "hash": "f9fa6017a53cd3e89c2ae5d3938d7461048c25b2aa8e520267fca421440325a1" } } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/examples/np_array_tensorproto.ipynb000066400000000000000000000065271511334557700232530ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Numpy array:\n", "[[1. 2. 3.]\n", " [4. 5. 6.]]\n", "\n" ] } ], "source": [ "import numpy\n", "import onnx\n", "import os\n", "from onnx import numpy_helper\n", "\n", "\n", "# Preprocessing: create a Numpy array\n", "numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float)\n", "print(\"Original Numpy array:\\n{}\\n\".format(numpy.array2string(numpy_array)))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorProto:\n", "dims: 2\n", "dims: 3\n", "data_type: 11\n", "raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n", "\n" ] } ], "source": [ "# Convert the Numpy array to a TensorProto\n", "tensor = numpy_helper.from_array(numpy_array)\n", "print(\"TensorProto:\\n{}\".format(tensor))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After round trip, Numpy array:\n", "[[1. 2. 3.]\n", " [4. 5. 6.]]\n", "\n" ] } ], "source": [ "# Convert the TensorProto to a Numpy array\n", "new_array = numpy_helper.to_array(tensor)\n", "print(\"After round trip, Numpy array:\\n{}\\n\".format(numpy.array2string(numpy_array)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Save the TensorProto\n", "with open(os.path.join(\"resources\", \"tensor.pb\"), \"wb\") as f:\n", " f.write(tensor.SerializeToString())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After saving and loading, new TensorProto:\n", "dims: 2\n", "dims: 3\n", "data_type: 11\n", "raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n", "\n" ] } ], "source": [ "# Load the TensorProto\n", "new_tensor = onnx.TensorProto()\n", "with open(os.path.join(\"resources\", \"tensor.pb\"), \"rb\") as f:\n", " new_tensor.ParseFromString(f.read())\n", "print(\"After saving and loading, new TensorProto:\\n{}\".format(new_tensor))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/examples/resources/000077500000000000000000000000001511334557700177175ustar00rootroot00000000000000onnx-onnx-bca0315/examples/resources/single_relu.onnx000066400000000000000000000001401511334557700231260ustar00rootroot00000000000000 backend-test:J  xytest"Relu SingleReluZ x   b y   Bonnx-onnx-bca0315/examples/resources/single_relu_new.onnx000066400000000000000000000001401511334557700237770ustar00rootroot00000000000000 backend-test:J  xytest"Relu SingleReluZ x   b y   Bonnx-onnx-bca0315/examples/resources/tensor.pb000066400000000000000000000000701511334557700215510ustar00rootroot00000000000000 J0đ?@@@@@onnx-onnx-bca0315/examples/resources/two_transposes.onnx000066400000000000000000000002421511334557700237130ustar00rootroot00000000000000 onnx-examples:Š " XY" Transpose* perm@@@  " YZ" Transpose* perm@@@ two-transposesZ X    b Z    Bonnx-onnx-bca0315/examples/save_model.ipynb000066400000000000000000000022451511334557700210710ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The model is saved.\n" ] } ], "source": [ "import onnx\n", "import os\n", "\n", "\n", "# Preprocessing: load the old model\n", "old_model_path = os.path.join(\"resources\", \"single_relu.onnx\")\n", "onnx_model = onnx.load(old_model_path)\n", "\n", "# Preprocessing: get the path to the saved model\n", "new_model_path = os.path.join(\"resources\", \"single_relu_new.onnx\")\n", "\n", "# Save the ONNX model\n", "onnx.save(onnx_model, new_model_path)\n", "\n", "print(\"The model is saved.\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/examples/shape_inference.ipynb000066400000000000000000000054041511334557700220710ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before shape inference, the shape info of Y is:\n", "[]\n" ] } ], "source": [ "import onnx\n", "from onnx import helper, shape_inference\n", "from onnx import TensorProto\n", "\n", "\n", "# Preprocessing: create a model with two nodes, Y's shape is unknown\n", "node1 = helper.make_node(\"Transpose\", [\"X\"], [\"Y\"], perm=[1, 0, 2])\n", "node2 = helper.make_node(\"Transpose\", [\"Y\"], [\"Z\"], perm=[1, 0, 2])\n", "\n", "graph = helper.make_graph(\n", " [node1, node2],\n", " \"two-transposes\",\n", " [helper.make_tensor_value_info(\"X\", TensorProto.FLOAT, (2, 3, 4))],\n", " [helper.make_tensor_value_info(\"Z\", TensorProto.FLOAT, (2, 3, 4))],\n", ")\n", "\n", "original_model = helper.make_model(graph, producer_name=\"onnx-examples\")\n", "\n", "# Check the model and print Y's shape information\n", "onnx.checker.check_model(original_model)\n", "print(\n", " \"Before shape inference, the shape info of Y is:\\n{}\".format(\n", " original_model.graph.value_info\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After shape inference, the shape info of Y is:\n", "[name: \"Y\"\n", "type {\n", " tensor_type {\n", " elem_type: 1\n", " shape {\n", " dim {\n", " dim_value: 3\n", " }\n", " dim {\n", " dim_value: 2\n", " }\n", " dim {\n", " dim_value: 4\n", " }\n", " }\n", " }\n", "}\n", "]\n" ] } ], "source": [ "# Apply shape inference on the model\n", "inferred_model = shape_inference.infer_shapes(original_model)\n", "\n", "# Check the model and print Y's shape information\n", "onnx.checker.check_model(inferred_model)\n", "print(\n", " \"After shape inference, the shape info of Y is:\\n{}\".format(\n", " inferred_model.graph.value_info\n", " )\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 2 } onnx-onnx-bca0315/onnx/000077500000000000000000000000001511334557700150515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/__init__.py000066400000000000000000000355571511334557700172010ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations __all__ = [ # Constants "ONNX_ML", "IR_VERSION", "IR_VERSION_2017_10_10", "IR_VERSION_2017_10_30", "IR_VERSION_2017_11_3", "IR_VERSION_2019_1_22", "IR_VERSION_2019_3_18", "IR_VERSION_2019_9_19", "IR_VERSION_2020_5_8", "IR_VERSION_2021_7_30", "IR_VERSION_2023_5_5", "IR_VERSION_2024_3_25", "EXPERIMENTAL", "STABLE", # Modules "checker", "compose", "defs", "gen_proto", "helper", "hub", "numpy_helper", "parser", "printer", "shape_inference", "utils", "version_converter", # Proto classes "AttributeProto", "DeviceConfigurationProto", "FunctionProto", "GraphProto", "IntIntListEntryProto", "MapProto", "ModelProto", "NodeDeviceConfigurationProto", "NodeProto", "OperatorProto", "OperatorSetIdProto", "OperatorSetProto", "OperatorStatus", "OptionalProto", "SequenceProto", "SimpleShardedDimProto", "ShardedDimProto", "ShardingSpecProto", "SparseTensorProto", "StringStringEntryProto", "TensorAnnotation", "TensorProto", "TensorShapeProto", "TrainingInfoProto", "TypeProto", "ValueInfoProto", "Version", # Utility functions "convert_model_to_external_data", "load_external_data_for_model", "load_model_from_string", "load_model", "load_tensor_from_string", "load_tensor", "save_model", "save_tensor", "write_external_data_tensors", ] # isort:skip_file import os import typing from typing import IO, Literal from onnx import serialization from onnx.onnx_cpp2py_export import ONNX_ML from onnx.external_data_helper import ( load_external_data_for_model, write_external_data_tensors, convert_model_to_external_data, ) from onnx.onnx_pb import ( AttributeProto, DeviceConfigurationProto, EXPERIMENTAL, FunctionProto, GraphProto, IntIntListEntryProto, IR_VERSION, IR_VERSION_2017_10_10, IR_VERSION_2017_10_30, IR_VERSION_2017_11_3, IR_VERSION_2019_1_22, IR_VERSION_2019_3_18, IR_VERSION_2019_9_19, IR_VERSION_2020_5_8, IR_VERSION_2021_7_30, IR_VERSION_2023_5_5, IR_VERSION_2024_3_25, ModelProto, NodeDeviceConfigurationProto, NodeProto, OperatorSetIdProto, OperatorStatus, STABLE, SimpleShardedDimProto, ShardedDimProto, ShardingSpecProto, SparseTensorProto, StringStringEntryProto, TensorAnnotation, TensorProto, TensorShapeProto, TrainingInfoProto, TypeProto, ValueInfoProto, Version, ) from onnx.onnx_operators_pb import OperatorProto, OperatorSetProto from onnx.onnx_data_pb import MapProto, OptionalProto, SequenceProto import onnx.version # Import common subpackages so they're available when you 'import onnx' from onnx import ( checker, compose, defs, gen_proto, helper, hub, numpy_helper, parser, printer, shape_inference, utils, version_converter, ) if typing.TYPE_CHECKING: from collections.abc import Sequence __version__ = onnx.version.version # Supported model formats that can be loaded from and saved to # The literals are formats with built-in support. But we also allow users to # register their own formats. So we allow str as well. _SupportedFormat = Literal["protobuf", "textproto", "onnxtxt", "json"] | str # noqa: PYI051 # Default serialization format _DEFAULT_FORMAT = "protobuf" def _load_bytes(f: IO[bytes] | str | os.PathLike) -> bytes: if hasattr(f, "read") and callable(typing.cast("IO[bytes]", f).read): content = typing.cast("IO[bytes]", f).read() else: f = typing.cast("str | os.PathLike", f) with open(f, "rb") as readable: content = readable.read() return content def _save_bytes(content: bytes, f: IO[bytes] | str | os.PathLike) -> None: if hasattr(f, "write") and callable(typing.cast("IO[bytes]", f).write): typing.cast("IO[bytes]", f).write(content) else: f = typing.cast("str | os.PathLike", f) with open(f, "wb") as writable: writable.write(content) def _get_file_path(f: IO[bytes] | str | os.PathLike | None) -> str | None: if isinstance(f, (str, os.PathLike)): return os.path.abspath(f) if hasattr(f, "name"): assert f is not None return os.path.abspath(f.name) return None def _get_serializer( fmt: _SupportedFormat | None, f: str | os.PathLike | IO[bytes] | None = None ) -> serialization.ProtoSerializer: """Get the serializer for the given path and format from the serialization registry.""" # Use fmt if it is specified if fmt is not None: return serialization.registry.get(fmt) if (file_path := _get_file_path(f)) is not None: _, ext = os.path.splitext(file_path) fmt = serialization.registry.get_format_from_file_extension(ext) # Failed to resolve format if fmt is None. Use protobuf as default fmt = fmt or _DEFAULT_FORMAT assert fmt is not None return serialization.registry.get(fmt) def load_model( f: IO[bytes] | str | os.PathLike, format: _SupportedFormat | None = None, # noqa: A002 load_external_data: bool = True, ) -> ModelProto: """Loads a serialized ModelProto into memory. Args: f: can be a file-like object (has "read" function) or a string/PathLike containing a file name format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. load_external_data: Whether to load the external data. Set to True if the data is under the same directory of the model. If not, users need to call :func:`load_external_data_for_model` with directory to load external data from. Returns: Loaded in-memory ModelProto. """ model = _get_serializer(format, f).deserialize_proto(_load_bytes(f), ModelProto()) if load_external_data: model_filepath = _get_file_path(f) if model_filepath: base_dir = os.path.dirname(model_filepath) load_external_data_for_model(model, base_dir) return model def load_tensor( f: IO[bytes] | str | os.PathLike, format: _SupportedFormat | None = None, # noqa: A002 ) -> TensorProto: """Loads a serialized TensorProto into memory. Args: f: can be a file-like object (has "read" function) or a string/PathLike containing a file name format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. Returns: Loaded in-memory TensorProto. """ return _get_serializer(format, f).deserialize_proto(_load_bytes(f), TensorProto()) def load_model_from_string( s: bytes | str, format: _SupportedFormat = _DEFAULT_FORMAT, # noqa: A002 ) -> ModelProto: """Loads a binary string (bytes) that contains serialized ModelProto. Args: s: a string, which contains serialized ModelProto format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. Returns: Loaded in-memory ModelProto. """ return _get_serializer(format).deserialize_proto(s, ModelProto()) def load_tensor_from_string( s: bytes, format: _SupportedFormat = _DEFAULT_FORMAT, # noqa: A002 ) -> TensorProto: """Loads a binary string (bytes) that contains serialized TensorProto. Args: s: a string, which contains serialized TensorProto format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. Returns: Loaded in-memory TensorProto. """ return _get_serializer(format).deserialize_proto(s, TensorProto()) def save_model( proto: ModelProto | bytes, f: IO[bytes] | str | os.PathLike, format: _SupportedFormat | None = None, # noqa: A002 *, save_as_external_data: bool = False, all_tensors_to_one_file: bool = True, location: str | None = None, size_threshold: int = 1024, convert_attribute: bool = False, ) -> None: """Saves the ModelProto to the specified path and optionally, serialize tensors with raw data as external data before saving. Args: proto: should be a in-memory ModelProto f: can be a file-like object (has "write" function) or a string containing a file name or a pathlike object format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. save_as_external_data: If true, save tensors to external file(s). all_tensors_to_one_file: Effective only if save_as_external_data is True. If true, save all tensors to one external file specified by location. If false, save each tensor to a file named with the tensor name. location: Effective only if save_as_external_data is true. Specify the external file that all tensors to save to. Path is relative to the model path. If not specified, will use the model name. size_threshold: Effective only if save_as_external_data is True. Threshold for size of data. Only when tensor's data is >= the size_threshold it will be converted to external data. To convert every tensor with raw data to external data set size_threshold=0. convert_attribute: Effective only if save_as_external_data is True. If true, convert all tensors to external data If false, convert only non-attribute tensors to external data """ if isinstance(proto, bytes): proto = _get_serializer(_DEFAULT_FORMAT).deserialize_proto(proto, ModelProto()) if save_as_external_data: convert_model_to_external_data( proto, all_tensors_to_one_file, location, size_threshold, convert_attribute ) model_filepath = _get_file_path(f) if model_filepath is not None: basepath = os.path.dirname(model_filepath) proto = write_external_data_tensors(proto, basepath) serialized = _get_serializer(format, model_filepath).serialize_proto(proto) _save_bytes(serialized, f) def save_tensor( proto: TensorProto, f: IO[bytes] | str | os.PathLike, format: _SupportedFormat | None = None, # noqa: A002 ) -> None: """Saves the TensorProto to the specified path. Args: proto: should be a in-memory TensorProto f: can be a file-like object (has "write" function) or a string containing a file name or a pathlike object. format: The serialization format. When it is not specified, it is inferred from the file extension when ``f`` is a path. If not specified _and_ ``f`` is not a path, 'protobuf' is used. The encoding is assumed to be "utf-8" when the format is a text format. """ serialized = _get_serializer(format, f).serialize_proto(proto) _save_bytes(serialized, f) # For backward compatibility load = load_model load_from_string = load_model_from_string save = save_model def _model_proto_repr(self: ModelProto) -> str: if self.domain: domain = f", domain='{self.domain}'" else: domain = "" if self.producer_name: producer_name = f", producer_name='{self.producer_name}'" else: producer_name = "" if self.producer_version: producer_version = f", producer_version='{self.producer_version}'" else: producer_version = "" if self.graph: graph = f", graph={self.graph!r}" else: graph = "" if self.functions: functions = f", functions=<{len(self.functions)} functions>" else: functions = "" if self.opset_import: opset_import = f", opset_import={_operator_set_protos_repr(self.opset_import)}" else: opset_import = "" return f"ModelProto(ir_version={self.ir_version}{opset_import}{domain}{producer_name}{producer_version}{graph}{functions})" def _graph_proto_repr(self: GraphProto) -> str: if self.initializer: initializer = f", initializer=<{len(self.initializer)} initializers>" else: initializer = "" if self.node: node = f", node=<{len(self.node)} nodes>" else: node = "" if self.value_info: value_info = f", value_info=<{len(self.value_info)} value_info>" else: value_info = "" if self.input: input = f", input=<{len(self.input)} inputs>" else: input = "" if self.output: output = f", output=<{len(self.output)} outputs>" else: output = "" return f"GraphProto('{self.name}'{input}{output}{initializer}{node}{value_info})" def _function_proto_repr(self: FunctionProto) -> str: if self.domain: domain = f", domain='{self.domain}'" else: domain = "" if self.overload: overload = f", overload='{self.overload}'" else: overload = "" if self.node: node = f", node=<{len(self.node)} nodes>" else: node = "" if self.attribute: attribute = f", attribute={self.attribute}" else: attribute = "" if self.opset_import: opset_import = f", opset_import={_operator_set_protos_repr(self.opset_import)}" else: opset_import = "" if self.input: input = f", input=<{len(self.input)} inputs>" else: input = "" if self.output: output = f", output=<{len(self.output)} outputs>" else: output = "" return f"FunctionProto('{self.name}'{domain}{overload}{opset_import}{input}{output}{attribute}{node})" def _operator_set_protos_repr(protos: Sequence[OperatorSetIdProto]) -> str: opset_imports = {proto.domain: proto.version for proto in protos} return repr(opset_imports) # Override __repr__ for some proto classes to make it more efficient ModelProto.__repr__ = _model_proto_repr # type: ignore[method-assign,assignment] GraphProto.__repr__ = _graph_proto_repr # type: ignore[method-assign,assignment] FunctionProto.__repr__ = _function_proto_repr # type: ignore[method-assign,assignment] onnx-onnx-bca0315/onnx/_mapping.py000066400000000000000000000101571511334557700172210ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import NamedTuple import ml_dtypes import numpy as np from onnx.onnx_pb import TensorProto class TensorDtypeMap(NamedTuple): np_dtype: np.dtype storage_dtype: int name: str # tensor_dtype: (numpy type, storage type, string name) # The storage type is the type used to store the tensor in the *_data field of # a TensorProto. All available fields are float_data, int32_data, int64_data, # string_data, uint64_data and double_data. TENSOR_TYPE_MAP: dict[int, TensorDtypeMap] = { int(TensorProto.FLOAT): TensorDtypeMap( np.dtype("float32"), int(TensorProto.FLOAT), "TensorProto.FLOAT" ), int(TensorProto.UINT8): TensorDtypeMap( np.dtype("uint8"), int(TensorProto.INT32), "TensorProto.UINT8" ), int(TensorProto.INT8): TensorDtypeMap( np.dtype("int8"), int(TensorProto.INT32), "TensorProto.INT8" ), int(TensorProto.UINT16): TensorDtypeMap( np.dtype("uint16"), int(TensorProto.INT32), "TensorProto.UINT16" ), int(TensorProto.INT16): TensorDtypeMap( np.dtype("int16"), int(TensorProto.INT32), "TensorProto.INT16" ), int(TensorProto.INT32): TensorDtypeMap( np.dtype("int32"), int(TensorProto.INT32), "TensorProto.INT32" ), int(TensorProto.INT64): TensorDtypeMap( np.dtype("int64"), int(TensorProto.INT64), "TensorProto.INT64" ), int(TensorProto.BOOL): TensorDtypeMap( np.dtype("bool"), int(TensorProto.INT32), "TensorProto.BOOL" ), int(TensorProto.FLOAT16): TensorDtypeMap( np.dtype("float16"), int(TensorProto.INT32), "TensorProto.FLOAT16" ), int(TensorProto.BFLOAT16): TensorDtypeMap( np.dtype(ml_dtypes.bfloat16), int(TensorProto.INT32), "TensorProto.BFLOAT16", ), int(TensorProto.DOUBLE): TensorDtypeMap( np.dtype("float64"), int(TensorProto.DOUBLE), "TensorProto.DOUBLE" ), int(TensorProto.COMPLEX64): TensorDtypeMap( np.dtype("complex64"), int(TensorProto.FLOAT), "TensorProto.COMPLEX64" ), int(TensorProto.COMPLEX128): TensorDtypeMap( np.dtype("complex128"), int(TensorProto.DOUBLE), "TensorProto.COMPLEX128", ), int(TensorProto.UINT32): TensorDtypeMap( np.dtype("uint32"), int(TensorProto.UINT64), "TensorProto.UINT32" ), int(TensorProto.UINT64): TensorDtypeMap( np.dtype("uint64"), int(TensorProto.UINT64), "TensorProto.UINT64" ), int(TensorProto.STRING): TensorDtypeMap( np.dtype("object"), int(TensorProto.STRING), "TensorProto.STRING" ), int(TensorProto.FLOAT8E4M3FN): TensorDtypeMap( np.dtype(ml_dtypes.float8_e4m3fn), int(TensorProto.INT32), "TensorProto.FLOAT8E4M3FN", ), int(TensorProto.FLOAT8E4M3FNUZ): TensorDtypeMap( np.dtype(ml_dtypes.float8_e4m3fnuz), int(TensorProto.INT32), "TensorProto.FLOAT8E4M3FNUZ", ), int(TensorProto.FLOAT8E5M2): TensorDtypeMap( np.dtype(ml_dtypes.float8_e5m2), int(TensorProto.INT32), "TensorProto.FLOAT8E5M2", ), int(TensorProto.FLOAT8E5M2FNUZ): TensorDtypeMap( np.dtype(ml_dtypes.float8_e5m2fnuz), int(TensorProto.INT32), "TensorProto.FLOAT8E5M2FNUZ", ), int(TensorProto.UINT4): TensorDtypeMap( np.dtype(ml_dtypes.uint4), int(TensorProto.INT32), "TensorProto.UINT4" ), int(TensorProto.INT4): TensorDtypeMap( np.dtype(ml_dtypes.int4), int(TensorProto.INT32), "TensorProto.INT4" ), int(TensorProto.FLOAT4E2M1): TensorDtypeMap( np.dtype(ml_dtypes.float4_e2m1fn), int(TensorProto.INT32), "TensorProto.FLOAT4E2M1", ), int(TensorProto.FLOAT8E8M0): TensorDtypeMap( np.dtype(ml_dtypes.float8_e8m0fnu), int(TensorProto.INT32), "TensorProto.FLOAT8E8M0", ), int(TensorProto.UINT2): TensorDtypeMap( np.dtype(ml_dtypes.uint2), int(TensorProto.INT32), "TensorProto.UINT2" ), int(TensorProto.INT2): TensorDtypeMap( np.dtype(ml_dtypes.int2), int(TensorProto.INT32), "TensorProto.INT2" ), } onnx-onnx-bca0315/onnx/backend/000077500000000000000000000000001511334557700164405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/__init__.py000066400000000000000000000001221511334557700205440ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 onnx-onnx-bca0315/onnx/backend/base.py000066400000000000000000000113721511334557700177300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from collections import namedtuple from typing import TYPE_CHECKING, Any, NewType import onnx.checker import onnx.onnx_cpp2py_export.checker as c_checker from onnx import IR_VERSION, ModelProto, NodeProto if TYPE_CHECKING: from collections.abc import Sequence import numpy class DeviceType: """Describes device type.""" _Type = NewType("_Type", int) CPU: _Type = _Type(0) CUDA: _Type = _Type(1) class Device: """Describes device type and device id syntax: device_type:device_id(optional) example: 'CPU', 'CUDA', 'CUDA:1' """ def __init__(self, device: str) -> None: options = device.split(":") self.type = getattr(DeviceType, options[0]) self.device_id = 0 if len(options) > 1: self.device_id = int(options[1]) def namedtupledict( typename: str, field_names: Sequence[str], *args: Any, **kwargs: Any ) -> type[tuple[Any, ...]]: field_names_map = {n: i for i, n in enumerate(field_names)} # Some output names are invalid python identifier, e.g. "0" kwargs.setdefault("rename", True) data = namedtuple(typename, field_names, *args, **kwargs) # type: ignore # noqa: PYI024 def getitem(self: Any, key: Any) -> Any: if isinstance(key, str): key = field_names_map[key] return super(type(self), self).__getitem__(key) # type: ignore data.__getitem__ = getitem # type: ignore[assignment] return data class BackendRep: """BackendRep is the handle that a Backend returns after preparing to execute a model repeatedly. Users will then pass inputs to the run function of BackendRep to retrieve the corresponding results. """ def run(self, inputs: Any, **kwargs: Any) -> tuple[Any, ...]: # noqa: ARG002 """Abstract function.""" return (None,) class Backend: """Backend is the entity that will take an ONNX model with inputs, perform a computation, and then return the output. For one-off execution, users can use run_node and run_model to obtain results quickly. For repeated execution, users should use prepare, in which the Backend does all of the preparation work for executing the model repeatedly (e.g., loading initializers), and returns a BackendRep handle. """ @classmethod def is_compatible( cls, model: ModelProto, # noqa: ARG003 device: str = "CPU", # noqa: ARG003 **kwargs: Any, # noqa: ARG003 ) -> bool: # Return whether the model is compatible with the backend. return True @classmethod def prepare( cls, model: ModelProto, device: str = "CPU", # noqa: ARG003 **kwargs: Any, # noqa: ARG003 ) -> BackendRep | None: # TODO Remove Optional from return type onnx.checker.check_model(model) return None @classmethod def run_model( cls, model: ModelProto, inputs: Any, device: str = "CPU", **kwargs: Any ) -> tuple[Any, ...]: backend = cls.prepare(model, device, **kwargs) assert backend is not None return backend.run(inputs) @classmethod def run_node( cls, node: NodeProto, inputs: Any, # noqa: ARG003 device: str = "CPU", # noqa: ARG003 outputs_info: ( # noqa: ARG003 Sequence[tuple[numpy.dtype, tuple[int, ...]]] | None ) = None, **kwargs: dict[str, Any], ) -> tuple[Any, ...] | None: """Simple run one operator and return the results. Args: node: The node proto. inputs: Inputs to the node. device: The device to run on. outputs_info: a list of tuples, which contains the element type and shape of each output. First element of the tuple is the dtype, and the second element is the shape. More use case can be found in https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py kwargs: Other keyword arguments. """ # TODO Remove Optional from return type if "opset_version" in kwargs: special_context = c_checker.CheckerContext() special_context.ir_version = IR_VERSION special_context.opset_imports = {"": kwargs["opset_version"]} # type: ignore onnx.checker.check_node(node, special_context) else: onnx.checker.check_node(node) return None @classmethod def supports_device(cls, device: str) -> bool: # noqa: ARG003 """Checks whether the backend is compiled with particular device support. In particular it's used in the testing suite. """ return True onnx-onnx-bca0315/onnx/backend/sample/000077500000000000000000000000001511334557700177215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/sample/__init__.py000066400000000000000000000001211511334557700220240ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 onnx-onnx-bca0315/onnx/backend/sample/ops/000077500000000000000000000000001511334557700205225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/sample/ops/__init__.py000066400000000000000000000015361511334557700226400ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import importlib import inspect import pkgutil import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from types import ModuleType def collect_sample_implementations() -> dict[str, str]: dict_: dict[str, str] = {} _recursive_scan(sys.modules[__name__], dict_) return dict_ def _recursive_scan(package: ModuleType, dict_: dict[str, str]) -> None: pkg_dir = package.__path__ module_location = package.__name__ for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir): module_name = f"{module_location}.{name}" # Module/package module = importlib.import_module(module_name) dict_[name] = inspect.getsource(module) if ispkg: _recursive_scan(module, dict_) onnx-onnx-bca0315/onnx/backend/sample/ops/abs.py000066400000000000000000000003171511334557700216420ustar00rootroot00000000000000# SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np def abs(input: np.ndarray) -> np.ndarray: # noqa: A001 return np.abs(input) # type: ignore[no-any-return] onnx-onnx-bca0315/onnx/backend/test/000077500000000000000000000000001511334557700174175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/__init__.py000066400000000000000000000003501511334557700215260ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations __all__ = ["BackendTest"] # for backward compatibility from onnx.backend.test.runner import Runner as BackendTest onnx-onnx-bca0315/onnx/backend/test/case/000077500000000000000000000000001511334557700203325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/case/__init__.py000066400000000000000000000005521511334557700224450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import sys from onnx.backend.test.case.base import Snippets from onnx.backend.test.case.utils import import_recursive def collect_snippets() -> dict[str, list[tuple[str, str]]]: import_recursive(sys.modules[__name__]) return Snippets onnx-onnx-bca0315/onnx/backend/test/case/base.py000066400000000000000000000027401511334557700216210ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import inspect from collections import defaultdict from textwrap import dedent from typing import Any, ClassVar import numpy as np def process_snippet(op_name: str, name: str, export: Any) -> tuple[str, str]: snippet_name = name[len("export_") :] or op_name.lower() source_code = dedent(inspect.getsource(export)) # remove the function signature line lines = source_code.splitlines() assert lines[0] == "@staticmethod" assert lines[1].startswith("def export") return snippet_name, dedent("\n".join(lines[2:])) Snippets: dict[str, list[tuple[str, str]]] = defaultdict(list) class _Exporter(type): exports: ClassVar[dict[str, list[tuple[str, str]]]] = defaultdict(list) def __init__( cls, name: str, bases: tuple[type[Any], ...], dct: dict[str, Any] ) -> None: for k, v in dct.items(): if k.startswith("export"): if not isinstance(v, staticmethod): raise ValueError("Only staticmethods could be named as export.*") export = getattr(cls, k) Snippets[name].append(process_snippet(name, k, export)) # export functions should call expect and so populate # TestCases np.random.seed(seed=0) export() super().__init__(name, bases, dct) class Base(metaclass=_Exporter): pass onnx-onnx-bca0315/onnx/backend/test/case/model/000077500000000000000000000000001511334557700214325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/case/model/__init__.py000066400000000000000000000043121511334557700235430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import sys from typing import TYPE_CHECKING from onnx.backend.test.case.test_case import TestCase from onnx.backend.test.case.utils import import_recursive if TYPE_CHECKING: from collections.abc import Sequence import numpy as np from onnx import ModelProto _SimpleModelTestCases = [] def expect( model: ModelProto, inputs: Sequence[np.ndarray], outputs: Sequence[np.ndarray], name: str | None = None, ) -> None: name = name or model.graph.name _SimpleModelTestCases.append( TestCase( name=name, model_name=model.graph.name, url=None, model_dir=None, model=model, data_sets=[(inputs, outputs)], kind="simple", rtol=1e-3, atol=1e-7, ) ) # BASE_URL = "https://download.onnxruntime.ai/onnx/models" BASE_URL = "onnx/backend/test/data/light/light_%s.onnx" def collect_testcases() -> list[TestCase]: """Collect model test cases defined in python/numpy code.""" real_model_testcases = [] model_tests = [ ("test_bvlc_alexnet", "bvlc_alexnet", 1e-3, 1e-7), ("test_densenet121", "densenet121", 2e-3, 1e-7), ("test_inception_v1", "inception_v1", 1e-3, 1e-7), ("test_inception_v2", "inception_v2", 1e-3, 1e-7), ("test_resnet50", "resnet50", 1e-3, 1e-7), ("test_shufflenet", "shufflenet", 1e-3, 1e-7), ("test_squeezenet", "squeezenet", 1e-3, 1e-7), ("test_vgg19", "vgg19", 1e-3, 1e-7), ("test_zfnet512", "zfnet512", 1e-3, 1e-7), ] for test_name, model_name, rtol, atol in model_tests: url = BASE_URL % model_name real_model_testcases.append( TestCase( name=test_name, model_name=model_name, url=url, model_dir=None, model=None, data_sets=None, kind="real", rtol=rtol, atol=atol, ) ) import_recursive(sys.modules[__name__]) return real_model_testcases + _SimpleModelTestCases onnx-onnx-bca0315/onnx/backend/test/case/model/expand.py000066400000000000000000000063441511334557700232720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import TYPE_CHECKING import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect if TYPE_CHECKING: from collections.abc import Sequence class ExpandDynamicShape(Base): @staticmethod def export() -> None: def make_graph( node: onnx.helper.NodeProto, input_shape: Sequence[int], shape_shape: Sequence[int], output_shape: Sequence[int], ) -> onnx.helper.GraphProto: return onnx.helper.make_graph( nodes=[node], name="Expand", inputs=[ onnx.helper.make_tensor_value_info( "X", onnx.TensorProto.FLOAT, input_shape ), onnx.helper.make_tensor_value_info( "shape", onnx.TensorProto.INT64, shape_shape ), ], outputs=[ onnx.helper.make_tensor_value_info( "Y", onnx.TensorProto.FLOAT, output_shape ) ], ) node = onnx.helper.make_node("Expand", ["X", "shape"], ["Y"], name="test") input_shape = [1, 3, 1] x = np.ones(input_shape, dtype=np.float32) # 1st testcase shape = np.array([3, 1], dtype=np.int64) y = x * np.ones(shape, dtype=np.float32) graph = make_graph(node, input_shape, shape.shape, y.shape) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model1") # 2nd testcase shape = np.array([1, 3], dtype=np.int64) y = x * np.ones(shape, dtype=np.float32) graph = make_graph(node, input_shape, shape.shape, y.shape) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model2") # 3rd testcase shape = np.array([3, 1, 3], dtype=np.int64) y = x * np.ones(shape, dtype=np.float32) graph = make_graph(node, input_shape, shape.shape, y.shape) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model3") # 4th testcase shape = np.array([3, 3, 1, 3], dtype=np.int64) y = x * np.ones(shape, dtype=np.float32) graph = make_graph(node, input_shape, shape.shape, y.shape) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model4") onnx-onnx-bca0315/onnx/backend/test/case/model/gradient.py000066400000000000000000000076521511334557700236130ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN, ONNX_DOMAIN class Gradient(Base): @staticmethod def export_gradient_scalar_add() -> None: add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dc_da", "dc_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="c", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # dc / da = d(a+b) / da = 1 dc_da = np.array(1).astype(np.float32) # db / db = d(a+b) / db = 1 dc_db = np.array(1).astype(np.float32) graph = onnx.helper.make_graph( nodes=[add_node, gradient_node], name="GradientOfAdd", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add" ) @staticmethod def export_gradient_scalar_add_and_mul() -> None: add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add") mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul") gradient_node = onnx.helper.make_node( "Gradient", ["a", "b"], ["dd_da", "dd_db"], name="my_gradient", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, xs=["a", "b"], y="d", ) a = np.array(1.0).astype(np.float32) b = np.array(2.0).astype(np.float32) c = a + b # d = a * c = a * (a + b) d = a * c # dd / da = d(a*a+a*b) / da = 2 * a + b dd_da = (2 * a + b).astype(np.float32) # dd / db = d(a*a+a*b) / db = a dd_db = a graph = onnx.helper.make_graph( nodes=[add_node, mul_node, gradient_node], name="GradientOfTwoOperators", inputs=[ onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []), ], outputs=[ onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []), onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []), ], ) opsets = [ onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12), onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), ] model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=opsets ) expect( model, inputs=[a, b], outputs=[d, dd_da, dd_db], name="test_gradient_of_add_and_mul", ) onnx-onnx-bca0315/onnx/backend/test/case/model/sequence.py000066400000000000000000000372471511334557700236310ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import typing import numpy as np import onnx from onnx import TensorProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect def SequenceEmptyImpl() -> list[np.ndarray | None]: return [] def SequenceConstructImpl(*tensors: np.ndarray) -> list[np.ndarray]: return list(tensors) def SequenceInsertImpl( sequence: list[np.ndarray], tensor: np.ndarray, position: int | None = None ) -> list[np.ndarray]: if position is None: position = len(sequence) sequence.insert(position, tensor) return sequence def SequenceAtImpl(sequence: list[np.ndarray], position: int) -> np.ndarray: return sequence[position] def SequenceEraseImpl( sequence: list[np.ndarray], position: int | None = None ) -> list[np.ndarray | None]: if position is None: position = -1 del sequence[position] return sequence def SequenceLengthImpl(sequence: list[np.ndarray]) -> np.int64: return np.int64(len(sequence)) def SplitToSequenceImpl( tensor: np.ndarray, split: int | list[int] | None = None, axis: int = 0, keepdims: int = 1, ) -> list[np.ndarray]: dim_size = tensor.shape[axis] if split is None: split = 1 split_indices = [ i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size ] if not keepdims: results = np.array_split(tensor, split_indices, axis) return [np.squeeze(res, axis) for res in results] if np.isscalar(split): split_indices = [ i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size ] else: split_indices = np.cumsum(split) + 1 return np.array_split(tensor, split_indices, axis) def ConcatFromSequenceImpl( sequence: list[np.ndarray], axis: int, new_axis: int | None = 0 ) -> np.ndarray: if not new_axis: return np.concatenate(sequence, axis) return np.stack(sequence, axis) class Sequence(Base): @staticmethod def export() -> None: def make_graph( nodes: list[onnx.helper.NodeProto], input_shapes: list[typing.Sequence[str | int] | None], output_shapes: list[typing.Sequence[str | int] | None], input_names: list[str], output_names: list[str], input_types: list[TensorProto.DataType], output_types: list[TensorProto.DataType], initializers: list[TensorProto] | None = None, ) -> onnx.helper.GraphProto: return onnx.helper.make_graph( nodes=nodes, name="Sequence", inputs=[ onnx.helper.make_tensor_value_info(name, input_type, input_shape) for name, input_type, input_shape in zip( input_names, input_types, input_shapes, strict=False ) ], outputs=[ onnx.helper.make_tensor_value_info(name, output_type, output_shape) for name, output_type, output_shape in zip( output_names, output_types, output_shapes, strict=False ) ], initializer=initializers, ) # 1st testcase - insert and at. # 1. SequenceEmpty: -> [] # 2. SequenceInsert(x): -> [x] # 3. SequenceInsert(y): -> [x, y] # 4. SequenceInsert(z, 1): -> [x, z, y] # 5. SequenceAt(2): -> y seq_empty_node = onnx.helper.make_node("SequenceEmpty", [], ["Seq_empty"]) seq_insert_node = onnx.helper.make_node( "SequenceInsert", ["Seq_empty", "X"], ["Seq_1"] ) seq_insert_node2 = onnx.helper.make_node( "SequenceInsert", ["Seq_1", "Y"], ["Seq_2"] ) seq_insert_node3 = onnx.helper.make_node( "SequenceInsert", ["Seq_2", "Z", "pos"], ["Seq_3"] ) seq_at_node = onnx.helper.make_node("SequenceAt", ["Seq_3", "pos_at"], ["out"]) x_shape = [2, 3, 4] y_shape = [1, 3, 4] z_shape = [3, 3, 4] out_shape = [None, 3, 4] x = np.ones(x_shape, dtype=np.float32) y = np.zeros(y_shape, dtype=np.float32) z = np.ones(z_shape, dtype=np.float32) * 2 pos_val = 1 pos_at_val = 2 out = SequenceEmptyImpl() out = SequenceInsertImpl(out, x) out = SequenceInsertImpl(out, y) out = SequenceInsertImpl(out, z, pos_val) out = SequenceAtImpl(out, pos_at_val) assert np.array_equal(out, y) pos = onnx.helper.make_tensor("pos", TensorProto.INT64, (), (pos_val,)) pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,)) graph = make_graph( [ seq_empty_node, seq_insert_node, seq_insert_node2, seq_insert_node3, seq_at_node, ], [x_shape, y_shape, z_shape, [], []], [out_shape], ["X", "Y", "Z", "pos", "pos_at"], ["out"], [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2, [onnx.TensorProto.FLOAT], [pos, pos_at], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model1") # 2nd testcase - erase and at. # 1. SequenceConstruct(x, y, z): -> [x, y, z] # 2. SequenceErase(1): -> [x, z] # 3. SequenceAt(1): -> z seq_construct_node = onnx.helper.make_node( "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"] ) seq_erase_node = onnx.helper.make_node( "SequenceErase", ["seq_1", "pos_erase"], ["seq_2"] ) seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_2", "pos_at"], ["out"]) tensor_shape = [2, 3, 4] x = np.ones(tensor_shape, dtype=np.float32) y = np.zeros(tensor_shape, dtype=np.float32) z = np.ones(tensor_shape, dtype=np.float32) * 2 pos_erase_val = 1 pos_at_val = 1 out = SequenceConstructImpl(x, y, z) out = SequenceEraseImpl(out, pos_erase_val) out = SequenceAtImpl(out, pos_at_val) assert np.array_equal(out, z) pos_erase = onnx.helper.make_tensor( "pos_erase", TensorProto.INT64, (), (pos_erase_val,) ) pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,)) graph = make_graph( [seq_construct_node, seq_erase_node, seq_at_node], [tensor_shape, tensor_shape, tensor_shape, [], []], [tensor_shape], ["X", "Y", "Z", "pos_erase", "pos_at"], ["out"], [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2, [onnx.TensorProto.FLOAT], [pos_erase, pos_at], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model2") # 3rd testcase - erase, insert and at, with negative index value. # 1. SequenceConstruct(x, y, z): -> [x, y, z] # 2. SequenceErase(-3): -> [y, z] # 3. SequenceInsert(x, -1): -> [y, x, z] # 4. SequenceAt(-1): -> z seq_construct_node = onnx.helper.make_node( "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"] ) seq_erase_node = onnx.helper.make_node( "SequenceErase", ["seq_1", "pos_erase"], ["seq_2"] ) seq_insert_node = onnx.helper.make_node( "SequenceInsert", ["seq_2", "X", "pos_insert"], ["seq_3"] ) seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_3", "pos_at"], ["out"]) tensor_shape = [2, 3, 4] x = np.ones(tensor_shape, dtype=np.float32) y = np.zeros(tensor_shape, dtype=np.float32) z = np.ones(tensor_shape, dtype=np.float32) * 2 pos_erase_val = -3 pos_insert_val = -1 pos_at_val = -1 out = SequenceConstructImpl(x, y, z) out = SequenceEraseImpl(out, pos_erase_val) out = SequenceInsertImpl(out, x, pos_insert_val) out = SequenceAtImpl(out, pos_at_val) assert np.array_equal(out, z) pos_erase = onnx.helper.make_tensor( "pos_erase", TensorProto.INT64, (), (pos_erase_val,) ) pos_insert = onnx.helper.make_tensor( "pos_insert", TensorProto.INT64, (), (pos_insert_val,) ) pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,)) graph = make_graph( [seq_construct_node, seq_erase_node, seq_insert_node, seq_at_node], [tensor_shape, tensor_shape, tensor_shape, [], [], []], [tensor_shape], ["X", "Y", "Z", "pos_erase", "pos_insert", "pos_at"], ["out"], [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 3, [onnx.TensorProto.FLOAT], [pos_erase, pos_insert, pos_at], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model3") # 4th testcase - concat seq_construct_node = onnx.helper.make_node( "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"] ) seq_concat_node = onnx.helper.make_node( "ConcatFromSequence", ["seq_1"], ["out"], axis=1 ) tensor_shape = [2, 3, 4] concat_out_shape = [2, None, 4] x = np.ones(tensor_shape, dtype=np.float32) y = np.zeros(tensor_shape, dtype=np.float32) z = np.ones(tensor_shape, dtype=np.float32) * 2 out = SequenceConstructImpl(x, y, z) concat_out = ConcatFromSequenceImpl(out, 1) graph = make_graph( [seq_construct_node, seq_concat_node], [tensor_shape] * 3, [concat_out_shape], ["X", "Y", "Z"], ["out"], [onnx.TensorProto.FLOAT] * 3, [onnx.TensorProto.FLOAT], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect( model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model4" ) # 5th testcase - concat with new_axis = 1 seq_construct_node = onnx.helper.make_node( "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"] ) seq_concat_node = onnx.helper.make_node( "ConcatFromSequence", ["seq_1"], ["out"], axis=-1, new_axis=1 ) tensor_shape = [2, 3, 4] concat_out_shape = [2, 3, 4, 3] x = np.ones(tensor_shape, dtype=np.float32) y = np.zeros(tensor_shape, dtype=np.float32) z = np.ones(tensor_shape, dtype=np.float32) * 2 out = SequenceConstructImpl(x, y, z) concat_out = ConcatFromSequenceImpl(out, -1, 1) graph = make_graph( [seq_construct_node, seq_concat_node], [tensor_shape] * 3, [concat_out_shape], ["X", "Y", "Z"], ["out"], [onnx.TensorProto.FLOAT] * 3, [onnx.TensorProto.FLOAT], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect( model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model5" ) # 6th testcase - split and len seq_split_node = onnx.helper.make_node( "SplitToSequence", ["X"], ["seq_1"], axis=-1 ) seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"]) tensor_shape = [2, 3, 4] len_shape = [] x = np.ones(tensor_shape, dtype=np.float32) out = SplitToSequenceImpl(x, axis=-1) out = SequenceLengthImpl(out) assert np.array_equal(out, np.int64(4)) graph = onnx.helper.make_graph( nodes=[seq_split_node, seq_len_node], name="Sequence", inputs=[ onnx.helper.make_tensor_value_info( "X", onnx.TensorProto.FLOAT, tensor_shape ) ], outputs=[ onnx.helper.make_tensor_value_info( "len", onnx.TensorProto.INT64, len_shape ) ], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect(model, inputs=[x], outputs=[out], name="test_sequence_model6") # 7th testcase - split with keepdims=0, and SequenceAt seq_split_node = onnx.helper.make_node( "SplitToSequence", ["X"], ["seq_1"], axis=0, keepdims=0 ) seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_1", "pos_at"], ["out"]) tensor_shape = [2, 3, 4] out_shape = [3, 4] x = np.random.rand(*tensor_shape) pos_at_val = 1 out = SplitToSequenceImpl(x, axis=0, keepdims=0) out = SequenceAtImpl(out, pos_at_val) assert np.array_equal(out, x[pos_at_val]) pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,)) graph = make_graph( [seq_split_node, seq_at_node], [tensor_shape, []], [out_shape], ["X", "pos_at"], ["out"], [onnx.TensorProto.DOUBLE, onnx.TensorProto.INT64], [onnx.TensorProto.DOUBLE], [pos_at], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect(model, inputs=[x], outputs=[out], name="test_sequence_model7") # 8th testcase - split zero length seq_split_node = onnx.helper.make_node( "SplitToSequence", ["X", "Splits"], ["seq_1"] ) seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"]) tensor_shape = ["n"] splits_shape = [3] x = np.array([]).astype(np.float32) splits = np.array([0, 0, 0]).astype(np.int64) out_len = np.int64(3) graph = onnx.helper.make_graph( nodes=[seq_split_node, seq_len_node], name="Sequence", inputs=[ onnx.helper.make_tensor_value_info( "X", onnx.TensorProto.FLOAT, tensor_shape ), onnx.helper.make_tensor_value_info( "Splits", onnx.TensorProto.INT64, splits_shape ), ], outputs=[ onnx.helper.make_tensor_value_info( "len", onnx.TensorProto.INT64, len_shape ) ], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 12)], ) expect( model, inputs=[x, splits], outputs=[out_len], name="test_sequence_model8" ) onnx-onnx-bca0315/onnx/backend/test/case/model/shrink.py000066400000000000000000000022601511334557700233020ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect class ShrinkTest(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Shrink", ["x"], ["y"], lambd=1.5, bias=1.5, ) graph = onnx.helper.make_graph( nodes=[node], name="Shrink", inputs=[ onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [5]) ], outputs=[ onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [5]) ], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) x = np.array([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=np.float32) y = np.array([-0.5, 0.0, 0.0, 0.0, 0.5], dtype=np.float32) expect(model, inputs=[x], outputs=[y], name="test_shrink") onnx-onnx-bca0315/onnx/backend/test/case/model/sign.py000066400000000000000000000021771511334557700227530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect class SingleSign(Base): @staticmethod def export() -> None: node = onnx.helper.make_node("Sign", ["x"], ["y"], name="test") x = np.array([-1.0, 4.5, -4.5, 3.1, 0.0, 2.4, -5.5]).astype(np.float32) y = np.array([-1.0, 1.0, -1.0, 1.0, 0.0, 1.0, -1.0]).astype(np.float32) graph = onnx.helper.make_graph( nodes=[node], name="SingleSign", inputs=[ onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [7]) ], outputs=[ onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [7]) ], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) expect(model, inputs=[x], outputs=[y], name="test_sign_model") onnx-onnx-bca0315/onnx/backend/test/case/model/single-relu.py000066400000000000000000000020761511334557700242370ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect class SingleRelu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node("Relu", ["x"], ["y"], name="test") graph = onnx.helper.make_graph( nodes=[node], name="SingleRelu", inputs=[ onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [1, 2]) ], outputs=[ onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [1, 2]) ], ) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 9)], ) x = np.random.randn(1, 2).astype(np.float32) y = np.maximum(x, 0) expect(model, inputs=[x], outputs=[y], name="test_single_relu_model") onnx-onnx-bca0315/onnx/backend/test/case/model/stringnormalizer.py000066400000000000000000000141131511334557700254150ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import TYPE_CHECKING import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.model import expect if TYPE_CHECKING: from collections.abc import Sequence class NormalizeStrings(Base): @staticmethod def export() -> None: def make_graph( node: onnx.helper.NodeProto, input_shape: Sequence[int], output_shape: Sequence[int], ) -> onnx.helper.GraphProto: return onnx.helper.make_graph( nodes=[node], name="StringNormalizer", inputs=[ onnx.helper.make_tensor_value_info( "x", onnx.TensorProto.STRING, input_shape ) ], outputs=[ onnx.helper.make_tensor_value_info( "y", onnx.TensorProto.STRING, output_shape ) ], ) # 1st model_monday_casesensintive_nochangecase stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, stopwords=stopwords, ) x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) y = np.array(["tuesday", "wednesday", "thursday"]).astype(object) graph = make_graph(node, [4], [3]) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_nochangecase", ) # 2nd model_nostopwords_nochangecase node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1 ) x = np.array(["monday", "tuesday"]).astype(object) y = x graph = make_graph(node, [2], [2]) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_nostopwords_nochangecase", ) # 3rd model_monday_casesensintive_lower stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="LOWER", is_case_sensitive=1, stopwords=stopwords, ) x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) y = np.array(["tuesday", "wednesday", "thursday"]).astype(object) graph = make_graph(node, [4], [3]) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_lower", ) # 4 model_monday_casesensintive_upper stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) y = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object) graph = make_graph(node, [4], [3]) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_casesensintive_upper", ) # 5 monday_insensintive_upper_twodim stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", stopwords=stopwords, ) input_shape = [1, 6] output_shape = [1, 4] x = ( np.array( ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"] ) .astype(object) .reshape(input_shape) ) y = ( np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"]) .astype(object) .reshape(output_shape) ) graph = make_graph(node, input_shape, output_shape) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_insensintive_upper_twodim", ) # 6 monday_empty_output stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=0, stopwords=stopwords, ) x = np.array(["monday", "monday"]).astype(object) y = np.array([""]).astype(object) graph = make_graph(node, [2], [1]) model = onnx.helper.make_model_gen_version( graph, producer_name="backend-test", opset_imports=[onnx.helper.make_opsetid("", 10)], ) expect( model, inputs=[x], outputs=[y], name="test_strnorm_model_monday_empty_output", ) onnx-onnx-bca0315/onnx/backend/test/case/node/000077500000000000000000000000001511334557700212575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/case/node/__init__.py000066400000000000000000000421011511334557700233660ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import subprocess import sys from copy import deepcopy from pathlib import Path from typing import TYPE_CHECKING, Any import onnx from onnx.backend.test.case.test_case import TestCase from onnx.backend.test.case.utils import import_recursive from onnx.onnx_pb import ( AttributeProto, FunctionProto, GraphProto, ModelProto, NodeProto, OperatorSetIdProto, TensorProto, TypeProto, ) if TYPE_CHECKING: from collections.abc import Callable, Sequence import numpy as np _NodeTestCases = [] _TargetOpType = None _DiffOpTypes = None _existing_names: dict[str, onnx.NodeProto] = {} def _rename_edges_helper( internal_node: NodeProto, rename_helper: Callable[[str], str], attribute_map: dict[str, AttributeProto], prefix: str, ) -> NodeProto: new_node = NodeProto() new_node.CopyFrom(internal_node) new_node.ClearField("input") new_node.ClearField("output") new_node.ClearField("attribute") for internal_name in internal_node.input: new_node.input.append(rename_helper(internal_name)) for internal_name in internal_node.output: new_node.output.append(rename_helper(internal_name)) for attr in internal_node.attribute: if attr.HasField("ref_attr_name"): if attr.ref_attr_name in attribute_map: new_attr = AttributeProto() new_attr.CopyFrom(attribute_map[attr.ref_attr_name]) new_attr.name = attr.name new_node.attribute.extend([new_attr]) else: new_attr = AttributeProto() new_attr.CopyFrom(attr) if attr.type == AttributeProto.GRAPH: new_graph = new_attr.g sg_rename = {} for in_desc in new_graph.input: sg_rename[in_desc.name] = in_desc.name = prefix + in_desc.name for out_desc in new_graph.output: sg_rename[out_desc.name] = out_desc.name = prefix + out_desc.name for init_desc in new_graph.initializer: sg_rename[init_desc.name] = init_desc.name = prefix + init_desc.name for sparse_init_desc in new_graph.sparse_initializer: sg_rename[sparse_init_desc.values.name] = ( sparse_init_desc.values.name ) = prefix + sparse_init_desc.values.name for sparse_init_desc in new_graph.sparse_initializer: sg_rename[sparse_init_desc.indices.name] = ( sparse_init_desc.indices.name ) = prefix + sparse_init_desc.indices.name def subgraph_rename_helper(name: str) -> Any: if name in sg_rename: # noqa: B023 return sg_rename[name] # noqa: B023 return rename_helper(name) new_nodes = [ _rename_edges_helper( node_desc, subgraph_rename_helper, attribute_map, prefix ) for node_desc in new_graph.node ] new_graph.ClearField("node") new_graph.node.extend(new_nodes) new_node.attribute.extend([new_attr]) return new_node # FIXME(TMVector): Any reason we can't get rid of this and use the C++ helper directly? def function_expand_helper( node: NodeProto, function_proto: FunctionProto, op_prefix: str ) -> list[NodeProto]: io_names_map = {} attribute_map = {a.name: a for a in node.attribute} for idx, input in enumerate(function_proto.input): io_names_map[input] = node.input[idx] if idx in range(len(node.input)) else "" for idx, output in enumerate(function_proto.output): # Even if the node has been created with optional outputs missing, we # can't assume that the function body handles this correctly, such as in # the case that output is also an intermediate value. # So we only add a name mapping if the output is present. An internal # name will be generated if the missing output is used, the same as any # other internal tensor. if idx in range(len(node.output)) and node.output[idx] != "": io_names_map[output] = node.output[idx] def rename_helper(internal_name: str) -> Any: if internal_name in io_names_map: return io_names_map[internal_name] if internal_name == "": return "" return op_prefix + internal_name return [ _rename_edges_helper(internal_node, rename_helper, attribute_map, op_prefix) for internal_node in function_proto.node ] def function_testcase_helper( node: NodeProto, input_types: list[TypeProto], name: str, opset_imports: Sequence[OperatorSetIdProto] | None = None, ) -> tuple[list[tuple[list[NodeProto], Any]], int]: test_op = node.op_type op_prefix = test_op + "_" + name + "_expanded_function_" if opset_imports is None: # No opset in the model. We take the most recent definition. schema = onnx.defs.get_schema(test_op, domain=node.domain) else: # We take the function coming defined in the specific version mentioned # in the model. if len(opset_imports) != 1: raise ValueError( f"Only one domain is allowed but {len(opset_imports)} found." ) version = opset_imports[0].version schema = onnx.defs.get_schema(test_op, version, domain=node.domain) # an op schema may have several functions, each for one opset version # opset versions include the op's since_version and other opset versions # if it is needed to define the op for a opset version other than the op's since_version. function_protos = [] for opset_version in schema.function_opset_versions: # type: ignore function_proto_str = schema.get_function_with_opset_version(opset_version) # type: ignore function_proto = FunctionProto() function_proto.ParseFromString(function_proto_str) function_protos.append(function_proto) for opset_version in schema.context_dependent_function_opset_versions: # type: ignore function_proto_str = schema.get_context_dependent_function_with_opset_version( # type: ignore opset_version, node.SerializeToString(), [t.SerializeToString() for t in input_types], ) function_proto = FunctionProto() function_proto.ParseFromString(function_proto_str) function_protos.append(function_proto) expanded_tests = [] for function_proto in function_protos: for attr in schema.attributes: if attr in [a.name for a in node.attribute]: continue if schema.attributes[attr].default_value: node.attribute.extend([schema.attributes[attr].default_value]) # function_proto.attributes node_list = function_expand_helper(node, function_proto, op_prefix) expanded_tests.append((node_list, function_proto.opset_import)) return expanded_tests, schema.since_version def _extract_value_info( input: list[Any] | np.ndarray | None, name: str, type_proto: TypeProto | None = None, ) -> onnx.ValueInfoProto: if type_proto is None: if input is None: raise NotImplementedError( "_extract_value_info: both input and type_proto arguments cannot be None." ) if isinstance(input, list): elem_type = onnx.helper.np_dtype_to_tensor_dtype(input[0].dtype) shape = None tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape) type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto) elif isinstance(input, TensorProto): elem_type = input.data_type shape = tuple(input.dims) type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape) else: elem_type = onnx.helper.np_dtype_to_tensor_dtype(input.dtype) shape = input.shape type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape) return onnx.helper.make_value_info(name, type_proto) def _make_test_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto: ( latest_onnx_version, latest_ml_version, latest_training_version, ) = onnx.helper.VERSION_TABLE[-1][2:5] # type: ignore if "opset_imports" in kwargs: for opset in kwargs["opset_imports"]: # If the test model uses an unreleased opset version (latest_version+1), # directly use make_model to create a model with the latest ir version if ( ( (opset.domain in {"", "ai.onnx"}) and opset.version == latest_onnx_version + 1 ) or ( opset.domain == "ai.onnx.ml" and opset.version == latest_ml_version + 1 ) or ( ( opset.domain in {"ai.onnx.training version", "ai.onnx.preview.training"} ) and opset.version == latest_training_version + 1 ) ): return onnx.helper.make_model(graph, **kwargs) # Otherwise, find and use the corresponding ir version according to given opset version return onnx.helper.make_model_gen_version(graph, **kwargs) # In the case of ops with optional inputs and outputs, node_op.input and node_op.output indicate # which inputs/outputs are present and which are omitted. However, the parameter inputs # and outputs of this function include values only for inputs/outputs that are present. # E.g., for an op with 3 inputs, if the second parameter is optional and we wish to omit it, # node_op.inputs would look like ["Param1", "", "Param3"], while inputs would look like # [input-1-value, input-3-value] # Instead of creating model with latest version, it now generates models for since_version by default. # Thus it can make every model uses the same opset version after every opset change. # Besides, user can specify "use_max_opset_version" to generate models for # the latest opset version that supports before targeted opset version def expect( node_op: onnx.NodeProto, inputs: Sequence[np.ndarray | TensorProto], outputs: Sequence[np.ndarray | TensorProto], name: str, **kwargs: Any, ) -> None: # skip if the node_op's op_type is not same as the given one if _TargetOpType and node_op.op_type != _TargetOpType: return if _DiffOpTypes is not None and node_op.op_type.lower() not in _DiffOpTypes: return if name in _existing_names: raise ValueError( f"Name {name!r} is already using by one test case for node type {node_op.op_type!r}." ) _existing_names[name] = node_op # in case node_op is modified node = deepcopy(node_op) present_inputs = [x for x in node.input if (x != "")] present_outputs = [x for x in node.output if (x != "")] input_type_protos = [None] * len(inputs) if "input_type_protos" in kwargs: input_type_protos = kwargs["input_type_protos"] del kwargs["input_type_protos"] output_type_protos = [None] * len(outputs) if "output_type_protos" in kwargs: output_type_protos = kwargs["output_type_protos"] del kwargs["output_type_protos"] inputs_vi = [ _extract_value_info(arr, arr_name, input_type) for arr, arr_name, input_type in zip( inputs, present_inputs, input_type_protos, strict=False ) ] outputs_vi = [ _extract_value_info(arr, arr_name, output_type) for arr, arr_name, output_type in zip( outputs, present_outputs, output_type_protos, strict=False ) ] graph = onnx.helper.make_graph( nodes=[node], name=name, inputs=inputs_vi, outputs=outputs_vi ) kwargs["producer_name"] = "backend-test" if "opset_imports" not in kwargs: # To make sure the model will be produced with the same opset_version after opset changes # By default, it uses since_version as opset_version for produced models produce_opset_version = onnx.defs.get_schema( node.op_type, domain=node.domain ).since_version kwargs["opset_imports"] = [ onnx.helper.make_operatorsetid(node.domain, produce_opset_version) ] model = _make_test_model_gen_version(graph, **kwargs) _NodeTestCases.append( TestCase( name=name, model_name=name, url=None, model_dir=None, model=model, data_sets=[(inputs, outputs)], kind="node", rtol=1e-3, atol=1e-7, ) ) # Create list of types for node.input, filling a default TypeProto for missing inputs: # E.g. merge(["x", "", "y"], [x-value-info, y-value-info]) will return [x-type, default-type, y-type] def merge( node_inputs: list[str], present_value_info: list[onnx.ValueInfoProto] ) -> list[TypeProto]: if node_inputs: if node_inputs[0] != "": return [ present_value_info[0].type, *merge(node_inputs[1:], present_value_info[1:]), ] return [TypeProto(), *merge(node_inputs[1:], present_value_info)] return [] merged_types = merge(list(node.input), inputs_vi) ( expanded_tests, since_version, ) = function_testcase_helper( node, merged_types, name, opset_imports=kwargs.get("opset_imports") ) for expanded_function_nodes, func_opset_import in expanded_tests: kwargs["producer_name"] = "backend-test" # TODO: if kwargs["opset_imports"] already exists, only generate test case for the opset version. # replace opset versions with what are specified in function proto if "opset_imports" not in kwargs: kwargs["opset_imports"] = func_opset_import else: for opset_import in func_opset_import: matches = [ opset for opset in kwargs["opset_imports"] if opset.domain == opset_import.domain ] if matches: matches[0].version = opset_import.version else: kwargs["opset_imports"].append(opset_import) onnx_ai_opset_version = "" if "opset_imports" in kwargs: onnx_ai_opset_imports = [ oi for oi in kwargs["opset_imports"] if oi.domain in ("", "ai.onnx") ] if len(onnx_ai_opset_imports) == 1: onnx_ai_opset_version = onnx_ai_opset_imports[0].version function_test_name = name + "_expanded" if onnx_ai_opset_version and onnx_ai_opset_version != since_version: function_test_name += f"_ver{onnx_ai_opset_version}" graph = onnx.helper.make_graph( nodes=expanded_function_nodes, name=function_test_name, inputs=inputs_vi, outputs=outputs_vi, ) model = _make_test_model_gen_version(graph, **kwargs) _NodeTestCases.append( TestCase( name=function_test_name, model_name=function_test_name, url=None, model_dir=None, model=model, data_sets=[(inputs, outputs)], kind="node", rtol=1e-3, atol=1e-7, ) ) def collect_testcases(op_type: str) -> list[TestCase]: """Collect node test cases""" # only keep those tests related to this operator global _TargetOpType # noqa: PLW0603 _TargetOpType = op_type import_recursive(sys.modules[__name__]) return _NodeTestCases def collect_diff_testcases() -> list[TestCase]: """Collect node test cases which are different from the main branch""" global _DiffOpTypes # noqa: PLW0603 _DiffOpTypes = get_diff_op_types() import_recursive(sys.modules[__name__]) return _NodeTestCases def get_diff_op_types(): cwd_path = Path.cwd() # git fetch first for git diff on GitHub Action subprocess.run( ["git", "fetch", "origin", "main:main"], cwd=cwd_path, capture_output=True, check=True, ) # obtain list of added or modified files in this PR with subprocess.Popen( ["git", "diff", "--name-only", "--diff-filter=AM", "origin/main", "HEAD"], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) as obtain_diff: stdoutput, _ = obtain_diff.communicate() diff_list = stdoutput.split() changed_op_types = [] for file in diff_list: file_name = file.decode("utf-8") if file_name.startswith( "onnx/backend/test/case/node/" ) and file_name.endswith(".py"): changed_op_types.append(file_name.split("/")[-1].replace(".py", "")) return changed_op_types onnx-onnx-bca0315/onnx/backend/test/case/node/_image_decoder_data.py000066400000000000000000006330201511334557700255340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 # This file contains freeze NumPy array for ImageDecoder backend test's input/output # They were generated by image_decoder.py's generate_test_data with pillow # To make ONNX backend test generation simple, use freeze NumPy array directly # People can still use generate_test_data to get these data here with installed pillow from __future__ import annotations from typing import NamedTuple import numpy as np class ImageData(NamedTuple): data: np.ndarray output: np.ndarray # Remove black check for better readability with large NumPy array # fmt: off image_decoder_decode_jpeg_rgb = ImageData( np.array([255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 255, 219, 0, 67, 0, 8, 6, 6, 7, 6, 5, 8, 7, 7, 7, 9, 9, 8, 10, 12, 20, 13, 12, 11, 11, 12, 25, 18, 19, 15, 20, 29, 26, 31, 30, 29, 26, 28, 28, 32, 36, 46, 39, 32, 34, 44, 35, 28, 28, 40, 55, 41, 44, 48, 49, 52, 52, 52, 31, 39, 57, 61, 56, 50, 60, 46, 51, 52, 50, 255, 219, 0, 67, 1, 9, 9, 9, 12, 11, 12, 24, 13, 13, 24, 50, 33, 28, 33, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 255, 192, 0, 17, 8, 0, 32, 0, 32, 3, 1, 34, 0, 2, 17, 1, 3, 17, 1, 255, 196, 0, 31, 0, 0, 1, 5, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 255, 196, 0, 181, 16, 0, 2, 1, 3, 3, 2, 4, 3, 5, 5, 4, 4, 0, 0, 1, 125, 1, 2, 3, 0, 4, 17, 5, 18, 33, 49, 65, 6, 19, 81, 97, 7, 34, 113, 20, 50, 129, 145, 161, 8, 35, 66, 177, 193, 21, 82, 209, 240, 36, 51, 98, 114, 130, 9, 10, 22, 23, 24, 25, 26, 37, 38, 39, 40, 41, 42, 52, 53, 54, 55, 56, 57, 58, 67, 68, 69, 70, 71, 72, 73, 74, 83, 84, 85, 86, 87, 88, 89, 90, 99, 100, 101, 102, 103, 104, 105, 106, 115, 116, 117, 118, 119, 120, 121, 122, 131, 132, 133, 134, 135, 136, 137, 138, 146, 147, 148, 149, 150, 151, 152, 153, 154, 162, 163, 164, 165, 166, 167, 168, 169, 170, 178, 179, 180, 181, 182, 183, 184, 185, 186, 194, 195, 196, 197, 198, 199, 200, 201, 202, 210, 211, 212, 213, 214, 215, 216, 217, 218, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 255, 196, 0, 31, 1, 0, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 255, 196, 0, 181, 17, 0, 2, 1, 2, 4, 4, 3, 4, 7, 5, 4, 4, 0, 1, 2, 119, 0, 1, 2, 3, 17, 4, 5, 33, 49, 6, 18, 65, 81, 7, 97, 113, 19, 34, 50, 129, 8, 20, 66, 145, 161, 177, 193, 9, 35, 51, 82, 240, 21, 98, 114, 209, 10, 22, 36, 52, 225, 37, 241, 23, 24, 25, 26, 38, 39, 40, 41, 42, 53, 54, 55, 56, 57, 58, 67, 68, 69, 70, 71, 72, 73, 74, 83, 84, 85, 86, 87, 88, 89, 90, 99, 100, 101, 102, 103, 104, 105, 106, 115, 116, 117, 118, 119, 120, 121, 122, 130, 131, 132, 133, 134, 135, 136, 137, 138, 146, 147, 148, 149, 150, 151, 152, 153, 154, 162, 163, 164, 165, 166, 167, 168, 169, 170, 178, 179, 180, 181, 182, 183, 184, 185, 186, 194, 195, 196, 197, 198, 199, 200, 201, 202, 210, 211, 212, 213, 214, 215, 216, 217, 218, 226, 227, 228, 229, 230, 231, 232, 233, 234, 242, 243, 244, 245, 246, 247, 248, 249, 250, 255, 218, 0, 12, 3, 1, 0, 2, 17, 3, 17, 0, 63, 0, 93, 15, 199, 122, 93, 181, 235, 187, 193, 120, 65, 140, 143, 149, 23, 212, 127, 181, 93, 36, 95, 16, 180, 150, 145, 36, 22, 247, 187, 119, 3, 247, 19, 255, 0, 138, 175, 29, 180, 183, 153, 37, 36, 195, 32, 249, 123, 169, 174, 175, 68, 138, 194, 73, 108, 226, 187, 104, 193, 105, 66, 186, 180, 155, 78, 11, 125, 120, 226, 184, 243, 204, 13, 12, 101, 117, 141, 154, 110, 46, 209, 211, 123, 247, 125, 45, 243, 61, 156, 62, 34, 141, 92, 59, 173, 137, 77, 201, 221, 46, 94, 253, 14, 135, 198, 14, 62, 32, 253, 139, 251, 39, 48, 253, 135, 127, 153, 246, 191, 151, 59, 246, 227, 27, 119, 127, 112, 245, 199, 106, 224, 124, 90, 134, 211, 194, 183, 118, 114, 96, 201, 8, 142, 54, 43, 208, 149, 117, 7, 30, 220, 87, 166, 234, 22, 182, 250, 103, 151, 253, 132, 163, 247, 153, 243, 188, 166, 50, 244, 198, 220, 231, 56, 234, 107, 142, 241, 245, 157, 168, 240, 86, 161, 114, 80, 11, 179, 229, 179, 18, 199, 59, 140, 139, 158, 51, 238, 107, 179, 15, 130, 246, 20, 21, 55, 36, 224, 175, 202, 147, 214, 251, 235, 243, 60, 117, 78, 166, 38, 42, 53, 218, 92, 154, 174, 154, 147, 29, 35, 96, 207, 159, 159, 248, 7, 255, 0, 94, 179, 100, 180, 251, 62, 168, 178, 239, 221, 229, 186, 182, 49, 140, 227, 6, 181, 173, 124, 127, 224, 229, 148, 153, 181, 4, 43, 142, 51, 107, 33, 231, 254, 248, 170, 26, 151, 141, 124, 35, 52, 243, 52, 23, 145, 144, 203, 242, 145, 107, 32, 231, 31, 238, 215, 6, 89, 154, 85, 141, 79, 99, 94, 140, 185, 31, 149, 149, 219, 93, 108, 107, 71, 56, 132, 40, 243, 209, 161, 40, 174, 145, 123, 223, 190, 167, 75, 164, 106, 249, 243, 191, 113, 253, 223, 227, 250, 251, 86, 119, 196, 109, 63, 30, 0, 212, 175, 124, 223, 188, 34, 125, 155, 122, 110, 149, 56, 206, 125, 235, 147, 255, 0, 132, 199, 71, 143, 253, 86, 160, 201, 158, 187, 99, 144, 103, 244, 170, 158, 37, 241, 133, 166, 167, 225, 155, 155, 8, 181, 73, 166, 103, 8, 4, 76, 36, 193, 195, 41, 238, 49, 218, 175, 49, 194, 42, 88, 181, 245, 95, 134, 234, 246, 215, 77, 58, 234, 24, 90, 95, 95, 162, 241, 56, 185, 165, 36, 155, 73, 232, 238, 180, 90, 105, 216, 255, 217], dtype=np.uint8), np.array([[[230, 73, 255], [227, 76, 255], [222, 81, 255], [211, 95, 206], [204, 123, 142], [251, 202, 133], [245, 226, 72], [234, 230, 34], [235, 234, 32], [231, 229, 32], [227, 221, 37], [233, 224, 45], [237, 227, 42], [232, 216, 32], [243, 219, 35], [255, 247, 63], [246, 255, 71], [249, 255, 83], [243, 253, 68], [255, 251, 134], [ 97, 23, 46], [127, 19, 121], [123, 0, 124], [118, 19, 128], [ 63, 28, 82], [ 99, 133, 143], [ 39, 146, 128], [ 24, 162, 110], [ 26, 157, 64], [ 66, 154, 76], [ 0, 9, 0], [ 15, 0, 9]], [[232, 77, 255], [228, 79, 255], [221, 83, 254], [210, 93, 208], [202, 117, 146], [248, 193, 137], [242, 216, 77], [230, 222, 35], [239, 237, 40], [235, 233, 36], [230, 225, 37], [235, 227, 41], [238, 228, 42], [230, 220, 34], [237, 225, 41], [255, 253, 70], [242, 250, 68], [242, 251, 70], [255, 255, 79], [255, 232, 124], [112, 32, 57], [119, 9, 108], [132, 10, 129], [117, 21, 121], [ 83, 50, 95], [ 98, 132, 134], [ 34, 137, 108], [ 31, 165, 106], [ 40, 168, 75], [ 62, 151, 71], [ 0, 9, 0], [ 11, 0, 5]], [[234, 81, 255], [229, 82, 255], [221, 86, 250], [211, 95, 218], [207, 115, 164], [254, 189, 161], [249, 213, 101], [238, 224, 57], [235, 232, 41], [231, 229, 30], [229, 221, 26], [229, 221, 26], [231, 225, 29], [218, 219, 27], [215, 229, 45], [237, 252, 71], [252, 252, 76], [255, 248, 80], [255, 249, 94], [255, 225, 135], [120, 30, 58], [120, 6, 101], [126, 9, 116], [109, 20, 104], [ 75, 47, 72], [ 97, 130, 113], [ 48, 144, 98], [ 37, 163, 89], [ 48, 169, 72], [ 59, 151, 66], [ 0, 16, 0], [ 0, 13, 0]], [[216, 83, 236], [211, 83, 238], [205, 88, 246], [203, 98, 226], [205, 114, 181], [255, 184, 178], [255, 207, 120], [251, 220, 77], [246, 228, 56], [247, 228, 46], [248, 216, 43], [251, 214, 45], [250, 217, 50], [234, 217, 51], [217, 232, 69], [234, 255, 95], [245, 251, 93], [255, 254, 104], [252, 232, 101], [255, 228, 153], [111, 34, 52], [126, 33, 103], [109, 15, 93], [103, 34, 91], [ 52, 33, 39], [ 98, 127, 99], [ 75, 152, 106], [ 48, 148, 86], [ 53, 151, 78], [ 69, 148, 83], [ 0, 16, 0], [ 0, 15, 0]], [[202, 104, 227], [196, 103, 235], [191, 106, 253], [192, 111, 242], [200, 117, 199], [253, 176, 192], [255, 193, 127], [255, 204, 85], [255, 210, 67], [255, 209, 67], [255, 195, 76], [255, 189, 82], [255, 193, 88], [255, 197, 88], [229, 217, 97], [229, 246, 118], [228, 250, 123], [249, 255, 148], [245, 251, 145], [239, 224, 155], [ 97, 57, 49], [ 87, 35, 58], [ 72, 21, 52], [ 83, 48, 68], [ 80, 76, 65], [ 99, 122, 96], [ 88, 136, 110], [ 63, 123, 98], [ 70, 127, 108], [ 86, 130, 115], [ 0, 9, 0], [ 0, 7, 0]], [[ 83, 27, 128], [ 76, 23, 137], [ 70, 23, 161], [ 73, 22, 151], [ 84, 16, 103], [138, 60, 86], [146, 66, 13], [150, 72, 0], [146, 73, 0], [164, 71, 0], [186, 55, 0], [198, 48, 15], [202, 51, 24], [176, 56, 19], [136, 81, 16], [116, 114, 31], [ 94, 131, 38], [ 62, 112, 17], [ 75, 109, 23], [108, 125, 55], [173, 175, 128], [200, 194, 162], [203, 197, 175], [175, 174, 153], [150, 161, 131], [ 98, 117, 95], [ 83, 105, 102], [ 80, 103, 117], [ 92, 111, 143], [ 92, 107, 138], [ 0, 3, 12], [ 0, 5, 2]], [[ 52, 43, 132], [ 45, 40, 142], [ 41, 38, 167], [ 51, 35, 159], [ 70, 21, 103], [129, 55, 82], [144, 53, 6], [155, 53, 0], [163, 53, 0], [189, 52, 10], [224, 32, 53], [242, 23, 77], [251, 25, 88], [223, 33, 77], [172, 59, 61], [131, 98, 65], [ 62, 111, 55], [ 47, 130, 58], [ 80, 146, 72], [ 73, 128, 44], [153, 200, 104], [139, 183, 86], [160, 202, 120], [169, 206, 139], [174, 203, 155], [ 92, 109, 91], [ 92, 92, 116], [101, 90, 148], [103, 88, 171], [ 97, 87, 160], [ 0, 0, 23], [ 0, 4, 4]], [[ 26, 47, 140], [ 21, 44, 146], [ 22, 48, 169], [ 39, 47, 158], [ 67, 31, 103], [131, 62, 83], [153, 56, 14], [170, 52, 0], [164, 37, 0], [195, 37, 25], [233, 21, 71], [255, 13, 100], [255, 14, 111], [236, 20, 103], [180, 45, 88], [131, 84, 92], [ 70, 118, 92], [ 46, 136, 85], [ 43, 118, 59], [ 53, 122, 41], [153, 222, 106], [156, 225, 100], [143, 209, 101], [150, 205, 122], [155, 192, 141], [ 89, 101, 91], [119, 98, 137], [120, 83, 161], [104, 65, 174], [104, 78, 167], [ 4, 6, 27], [ 0, 7, 0]], [[ 18, 50, 161], [ 20, 54, 164], [ 13, 53, 166], [ 26, 47, 140], [ 69, 44, 99], [118, 52, 64], [155, 55, 23], [170, 48, 0], [169, 41, 2], [190, 36, 26], [233, 35, 71], [249, 26, 91], [239, 10, 92], [218, 23, 102], [177, 54, 117], [129, 83, 119], [ 75, 112, 105], [ 58, 131, 101], [ 56, 116, 78], [ 64, 120, 57], [151, 214, 110], [153, 217, 103], [138, 203, 109], [147, 202, 134], [169, 199, 163], [102, 102, 104], [124, 88, 132], [120, 67, 147], [131, 78, 184], [101, 68, 147], [ 3, 9, 7], [ 0, 18, 0]], [[ 15, 46, 136], [ 16, 52, 138], [ 11, 53, 135], [ 23, 46, 114], [ 66, 43, 89], [114, 48, 76], [151, 46, 60], [164, 39, 45], [167, 43, 51], [164, 28, 38], [200, 43, 54], [214, 43, 59], [210, 42, 59], [195, 56, 79], [144, 65, 94], [115, 93, 114], [ 72, 103, 106], [ 63, 116, 108], [ 70, 111, 103], [ 87, 127, 103], [139, 187, 129], [151, 201, 138], [147, 198, 155], [160, 198, 177], [150, 162, 162], [117, 97, 124], [143, 86, 141], [142, 68, 147], [148, 76, 176], [124, 74, 145], [ 6, 0, 0], [ 0, 10, 0]], [[ 36, 57, 88], [ 37, 62, 92], [ 29, 65, 91], [ 39, 55, 88], [ 76, 47, 95], [118, 47, 115], [150, 39, 134], [158, 32, 132], [154, 41, 123], [146, 43, 86], [196, 101, 79], [194, 111, 45], [180, 109, 17], [170, 126, 37], [140, 138, 77], [173, 197, 165], [185, 215, 215], [183, 214, 235], [186, 206, 239], [173, 192, 225], [120, 142, 165], [107, 128, 157], [101, 121, 171], [112, 118, 178], [158, 138, 199], [180, 129, 195], [215, 130, 198], [226, 125, 203], [222, 126, 216], [201, 125, 189], [ 44, 6, 3], [ 23, 5, 0]], [[ 43, 58, 65], [ 43, 62, 66], [ 36, 65, 63], [ 42, 55, 64], [ 72, 44, 84], [112, 41, 121], [140, 28, 156], [143, 21, 158], [130, 28, 140], [115, 39, 86], [164, 121, 70], [151, 132, 14], [131, 127, 0], [127, 142, 0], [115, 151, 55], [196, 235, 190], [189, 214, 218], [192, 206, 245], [201, 208, 255], [202, 207, 255], [110, 114, 188], [109, 113, 202], [117, 115, 225], [126, 109, 224], [137, 95, 195], [195, 127, 212], [225, 129, 201], [237, 127, 200], [229, 120, 203], [217, 125, 188], [ 41, 0, 0], [ 32, 0, 0]], [[ 44, 55, 73], [ 46, 63, 73], [ 41, 66, 63], [ 46, 57, 59], [ 74, 50, 76], [111, 47, 108], [141, 35, 146], [142, 27, 148], [146, 52, 148], [114, 55, 85], [139, 130, 55], [125, 147, 10], [119, 155, 0], [115, 158, 16], [104, 140, 68], [186, 213, 194], [196, 209, 226], [186, 190, 235], [187, 187, 247], [198, 197, 255], [109, 105, 202], [121, 113, 232], [123, 108, 249], [122, 91, 231], [149, 99, 214], [211, 141, 230], [225, 133, 200], [232, 126, 190], [224, 118, 192], [227, 132, 196], [ 51, 0, 14], [ 43, 0, 4]], [[ 33, 57, 59], [ 36, 62, 59], [ 34, 66, 53], [ 39, 58, 52], [ 69, 52, 70], [107, 50, 105], [137, 36, 140], [142, 27, 148], [128, 30, 131], [110, 49, 93], [126, 120, 68], [116, 146, 32], [115, 160, 18], [112, 150, 31], [121, 131, 81], [205, 200, 196], [215, 206, 227], [218, 206, 242], [215, 207, 248], [214, 206, 255], [120, 113, 193], [121, 110, 215], [125, 106, 234], [135, 105, 229], [141, 97, 192], [190, 133, 202], [206, 133, 178], [216, 135, 176], [210, 132, 184], [209, 139, 193], [ 36, 0, 21], [ 29, 0, 18]], [[ 22, 75, 33], [ 27, 79, 40], [ 26, 77, 44], [ 33, 65, 50], [ 61, 57, 72], [ 99, 50, 106], [134, 32, 141], [143, 19, 153], [146, 29, 162], [142, 57, 150], [136, 108, 120], [123, 131, 84], [123, 141, 57], [123, 124, 48], [162, 121, 91], [248, 192, 193], [227, 185, 197], [233, 201, 214], [221, 199, 201], [207, 192, 197], [134, 126, 150], [122, 114, 161], [116, 105, 174], [133, 116, 184], [150, 127, 171], [171, 143, 165], [181, 149, 154], [189, 157, 160], [188, 156, 177], [175, 152, 181], [ 11, 0, 30], [ 1, 0, 27]], [[ 58, 122, 61], [ 61, 125, 64], [ 60, 121, 64], [ 66, 106, 71], [ 92, 93, 97], [129, 81, 133], [165, 59, 170], [174, 41, 184], [172, 40, 185], [151, 48, 165], [102, 50, 112], [ 82, 60, 73], [ 91, 77, 50], [ 94, 59, 27], [134, 53, 50], [199, 106, 114], [193, 121, 124], [186, 127, 121], [173, 115, 101], [184, 143, 121], [179, 165, 139], [192, 189, 170], [181, 172, 175], [180, 174, 176], [168, 169, 155], [150, 158, 134], [134, 151, 119], [125, 148, 120], [123, 150, 133], [121, 143, 140], [ 0, 4, 17], [ 0, 3, 26]], [[ 61, 119, 61], [ 60, 121, 54], [ 60, 124, 38], [ 71, 116, 47], [ 96, 97, 79], [127, 76, 119], [164, 51, 167], [181, 39, 183], [181, 41, 172], [164, 45, 161], [118, 40, 139], [100, 45, 113], [106, 56, 85], [124, 56, 71], [140, 31, 60], [224, 107, 126], [212, 114, 105], [213, 116, 97], [223, 109, 99], [206, 116, 89], [205, 181, 109], [185, 190, 100], [192, 182, 110], [188, 185, 116], [170, 194, 118], [112, 162, 89], [104, 180, 118], [ 87, 176, 120], [ 76, 166, 105], [ 98, 165, 121], [ 0, 20, 7], [ 0, 0, 5]], [[ 61, 114, 72], [ 61, 116, 59], [ 63, 121, 37], [ 77, 114, 44], [100, 97, 78], [128, 77, 120], [161, 54, 168], [178, 43, 182], [183, 50, 169], [174, 52, 163], [135, 37, 158], [120, 35, 139], [120, 38, 104], [135, 39, 87], [151, 24, 77], [231, 100, 132], [223, 109, 98], [227, 111, 86], [240, 101, 94], [220, 111, 82], [210, 183, 92], [186, 193, 79], [195, 186, 85], [189, 191, 94], [161, 199, 98], [ 94, 165, 71], [ 80, 181, 105], [ 76, 190, 118], [ 72, 184, 100], [ 91, 174, 104], [ 0, 13, 0], [ 0, 1, 0]], [[ 59, 110, 91], [ 65, 111, 82], [ 73, 113, 63], [ 86, 107, 68], [106, 94, 98], [127, 79, 131], [149, 62, 169], [163, 54, 179], [163, 47, 154], [164, 48, 155], [138, 26, 154], [134, 23, 144], [136, 23, 111], [149, 30, 98], [162, 27, 94], [231, 98, 141], [223, 106, 99], [226, 110, 87], [235, 102, 95], [214, 110, 85], [208, 181, 102], [184, 190, 92], [189, 184, 103], [181, 189, 112], [167, 211, 126], [102, 174, 90], [ 81, 173, 98], [ 83, 181, 104], [ 86, 179, 88], [102, 173, 95], [ 0, 14, 0], [ 1, 12, 0]], [[ 88, 95, 88], [ 94, 95, 81], [104, 95, 64], [114, 90, 66], [124, 81, 88], [133, 73, 111], [138, 65, 138], [143, 60, 142], [155, 66, 132], [163, 62, 142], [144, 29, 148], [151, 26, 154], [152, 25, 130], [160, 33, 124], [162, 35, 124], [204, 91, 157], [200, 115, 138], [199, 120, 125], [207, 115, 130], [190, 121, 114], [196, 187, 128], [180, 194, 115], [186, 189, 122], [182, 193, 125], [145, 189, 102], [115, 176, 81], [104, 170, 80], [105, 171, 74], [110, 167, 60], [124, 167, 75], [ 0, 12, 0], [ 0, 7, 0]], [[194, 115, 111], [198, 114, 103], [207, 113, 88], [210, 112, 87], [204, 111, 96], [192, 111, 108], [178, 113, 121], [171, 114, 123], [171, 111, 111], [175, 99, 127], [159, 49, 144], [170, 44, 169], [167, 39, 158], [164, 43, 159], [146, 43, 160], [148, 77, 181], [108, 84, 162], [ 99, 93, 155], [107, 89, 147], [100, 94, 122], [124, 153, 122], [121, 157, 96], [135, 148, 92], [139, 152, 82], [142, 179, 76], [170, 211, 91], [191, 217, 92], [200, 212, 84], [202, 206, 70], [207, 207, 93], [ 14, 19, 0], [ 2, 8, 0]], [[230, 98, 93], [234, 97, 87], [239, 97, 77], [236, 97, 74], [220, 101, 79], [197, 105, 80], [170, 113, 84], [158, 117, 85], [170, 127, 92], [177, 112, 118], [159, 53, 143], [180, 50, 184], [175, 44, 182], [167, 48, 192], [143, 51, 198], [117, 69, 205], [ 81, 93, 205], [ 66, 105, 198], [ 73, 105, 182], [ 73, 108, 148], [113, 160, 142], [117, 161, 112], [134, 154, 103], [140, 158, 84], [122, 156, 44], [185, 213, 77], [214, 213, 73], [223, 200, 58], [225, 192, 51], [224, 201, 89], [ 4, 7, 0], [ 0, 8, 0]], [[245, 93, 82], [247, 93, 81], [250, 92, 80], [242, 95, 79], [222, 101, 82], [196, 109, 81], [168, 119, 79], [155, 122, 79], [158, 120, 81], [169, 106, 115], [156, 45, 148], [179, 44, 199], [173, 36, 201], [166, 44, 215], [148, 53, 233], [113, 66, 232], [ 73, 92, 220], [ 54, 108, 204], [ 56, 110, 184], [ 59, 113, 147], [111, 160, 139], [119, 159, 109], [133, 153, 100], [137, 158, 83], [133, 170, 54], [198, 222, 84], [220, 203, 63], [231, 189, 53], [235, 183, 61], [229, 197, 110], [ 0, 8, 0], [ 0, 18, 21]], [[222, 106, 57], [223, 107, 60], [225, 106, 66], [218, 109, 70], [202, 115, 72], [182, 120, 69], [159, 128, 64], [150, 129, 64], [158, 129, 71], [170, 118, 107], [150, 59, 138], [167, 58, 185], [152, 46, 178], [142, 54, 192], [128, 68, 216], [ 95, 76, 218], [ 60, 99, 218], [ 44, 113, 208], [ 42, 116, 187], [ 49, 117, 152], [111, 159, 147], [124, 154, 116], [135, 149, 113], [142, 153, 93], [137, 162, 61], [205, 209, 86], [230, 185, 58], [255, 183, 62], [255, 182, 73], [246, 190, 116], [ 0, 5, 0], [ 0, 12, 25]], [[167, 145, 26], [163, 140, 28], [162, 137, 37], [162, 137, 45], [153, 136, 46], [145, 134, 42], [144, 140, 40], [150, 146, 46], [156, 142, 45], [153, 122, 65], [134, 81, 99], [130, 76, 126], [123, 85, 126], [105, 89, 128], [ 92, 102, 153], [ 60, 97, 165], [ 50, 114, 202], [ 31, 115, 205], [ 28, 119, 189], [ 54, 131, 173], [105, 144, 151], [138, 151, 141], [147, 146, 141], [149, 143, 117], [160, 159, 92], [217, 188, 96], [255, 176, 77], [255, 179, 77], [255, 162, 64], [255, 159, 89], [ 31, 6, 0], [ 0, 3, 5]], [[119, 197, 23], [121, 197, 29], [124, 193, 42], [132, 194, 49], [145, 196, 55], [161, 203, 59], [178, 206, 59], [191, 207, 59], [196, 199, 58], [187, 183, 75], [153, 145, 99], [135, 138, 117], [118, 148, 110], [ 96, 152, 115], [ 78, 155, 139], [ 46, 135, 153], [ 33, 125, 192], [ 26, 121, 205], [ 21, 122, 192], [ 43, 117, 166], [ 87, 105, 127], [114, 97, 107], [121, 90, 105], [126, 86, 84], [132, 88, 49], [175, 104, 42], [205, 78, 9], [231, 73, 0], [230, 69, 0], [219, 95, 35], [ 34, 0, 0], [ 7, 3, 2]], [[ 41, 226, 10], [ 46, 226, 15], [ 51, 219, 20], [ 62, 212, 16], [ 93, 215, 16], [131, 224, 22], [164, 223, 21], [177, 214, 13], [191, 217, 22], [180, 207, 42], [131, 173, 63], [102, 168, 78], [ 82, 184, 82], [ 64, 193, 101], [ 45, 188, 134], [ 13, 152, 147], [ 28, 143, 198], [ 36, 139, 218], [ 37, 140, 209], [ 62, 125, 178], [111, 96, 127], [140, 78, 101], [151, 73, 97], [164, 72, 83], [167, 65, 50], [210, 83, 50], [234, 64, 28], [252, 67, 23], [238, 63, 8], [210, 82, 37], [ 34, 0, 0], [ 1, 0, 4]], [[ 14, 255, 23], [ 22, 255, 27], [ 27, 248, 23], [ 38, 234, 10], [ 77, 234, 5], [127, 245, 13], [163, 239, 7], [176, 225, 0], [178, 216, 0], [166, 209, 15], [113, 177, 29], [ 78, 171, 41], [ 62, 189, 50], [ 53, 200, 83], [ 38, 191, 127], [ 2, 149, 143], [ 4, 133, 190], [ 12, 131, 213], [ 20, 132, 206], [ 52, 116, 177], [113, 85, 125], [150, 67, 95], [164, 66, 91], [181, 66, 81], [194, 63, 55], [226, 71, 51], [239, 52, 33], [250, 56, 29], [234, 57, 15], [203, 75, 38], [ 42, 0, 0], [ 10, 2, 0]], [[ 0, 251, 19], [ 5, 252, 23], [ 15, 241, 17], [ 29, 228, 1], [ 70, 231, 0], [122, 242, 4], [159, 237, 3], [171, 222, 0], [186, 228, 6], [175, 222, 22], [126, 188, 29], [ 93, 174, 33], [ 80, 184, 37], [ 76, 189, 73], [ 69, 175, 127], [ 25, 138, 154], [ 8, 140, 212], [ 1, 140, 235], [ 8, 138, 224], [ 44, 121, 191], [113, 91, 137], [150, 74, 102], [160, 72, 88], [174, 68, 70], [195, 65, 51], [224, 69, 49], [235, 53, 40], [248, 63, 43], [230, 67, 26], [192, 70, 29], [ 46, 0, 0], [ 15, 0, 0]], [[ 37, 234, 58], [ 45, 233, 60], [ 55, 225, 58], [ 70, 216, 47], [101, 215, 39], [137, 220, 44], [163, 216, 48], [170, 207, 42], [170, 205, 43], [163, 203, 55], [125, 176, 55], [103, 168, 52], [ 96, 173, 43], [ 94, 170, 70], [ 99, 159, 133], [ 66, 133, 160], [ 33, 131, 192], [ 19, 133, 206], [ 17, 130, 200], [ 48, 117, 174], [114, 93, 126], [144, 80, 96], [141, 81, 83], [146, 78, 69], [166, 73, 58], [191, 77, 66], [204, 68, 70], [219, 81, 78], [205, 83, 59], [168, 79, 47], [ 50, 13, 0], [ 10, 1, 0]], [[ 0, 28, 0], [ 0, 25, 0], [ 0, 19, 0], [ 0, 15, 0], [ 0, 10, 0], [ 0, 7, 0], [ 5, 4, 0], [ 5, 6, 0], [ 1, 13, 0], [ 0, 21, 0], [ 0, 15, 0], [ 0, 19, 0], [ 0, 24, 0], [ 0, 20, 0], [ 0, 8, 6], [ 0, 1, 30], [ 0, 5, 28], [ 0, 10, 32], [ 0, 15, 41], [ 0, 9, 28], [ 18, 0, 0], [ 31, 0, 0], [ 10, 0, 0], [ 4, 1, 0], [ 18, 0, 0], [ 27, 0, 0], [ 32, 0, 7], [ 36, 0, 5], [ 35, 0, 0], [ 31, 1, 0], [ 0, 1, 0], [ 0, 5, 7]], [[ 0, 2, 0], [ 3, 0, 0], [ 10, 0, 4], [ 17, 0, 10], [ 22, 0, 11], [ 18, 0, 14], [ 15, 0, 25], [ 9, 0, 29], [ 0, 0, 21], [ 0, 5, 18], [ 0, 8, 11], [ 0, 16, 0], [ 0, 20, 0], [ 0, 13, 0], [ 7, 4, 15], [ 8, 0, 25], [ 6, 0, 2], [ 0, 4, 0], [ 0, 13, 16], [ 0, 8, 9], [ 22, 0, 0], [ 25, 0, 0], [ 0, 7, 0], [ 0, 12, 0], [ 0, 7, 0], [ 0, 5, 15], [ 1, 0, 32], [ 10, 0, 33], [ 9, 0, 10], [ 8, 6, 11], [ 0, 5, 14], [ 0, 16, 27]]], dtype=np.uint8), ) image_decoder_decode_jpeg_grayscale = ImageData( image_decoder_decode_jpeg_rgb.data, np.array([[[141], [142], [143], [142], [149], [209], [214], [209], [211], [207], [202], [206], [209], [200], [205], [228], [231], [234], [229], [239], [ 48], [ 63], [ 51], [ 61], [ 45], [124], [112], [115], [107], [119], [ 5], [ 6]],[[144], [144], [144], [141], [146], [203], [208], [203], [215], [211], [205], [208], [210], [202], [208], [233], [227], [228], [235], [227], [ 59], [ 53], [ 60], [ 61], [ 65], [122], [103], [118], [119], [115], [ 5], [ 4]],[[147], [146], [145], [144], [148], [205], [211], [209], [211], [207], [201], [201], [204], [197], [204], [227], [232], [231], [233], [224], [ 60], [ 51], [ 56], [ 56], [ 58], [118], [110], [117], [122], [114], [ 9], [ 8]],[[140], [139], [141], [144], [149], [205], [211], [213], [214], [213], [206], [206], [208], [203], [209], [230], [231], [237], [223], [228], [ 59], [ 69], [ 52], [ 61], [ 39], [115], [124], [111], [113], [117], [ 9], [ 9]],[[147], [146], [148], [150], [151], [201], [204], [206], [207], [207], [199], [197], [200], [202], [207], [226], [229], [241], [237], [221], [ 68], [ 53], [ 40], [ 61], [ 76], [112], [119], [102], [108], [115], [ 5], [ 4]],[[ 55], [ 52], [ 53], [ 52], [ 46], [ 86], [ 84], [ 87], [ 87], [ 91], [ 88], [ 89], [ 93], [ 88], [ 90], [105], [109], [ 86], [ 89], [112], [169], [192], [196], [172], [154], [109], [ 98], [ 98], [109], [106], [ 3], [ 3]],[[ 56], [ 53], [ 54], [ 54], [ 45], [ 80], [ 75], [ 77], [ 80], [ 88], [ 92], [ 95], [100], [ 95], [ 93], [104], [ 90], [ 97], [118], [102], [175], [159], [180], [187], [189], [102], [ 95], [100], [102], [ 98], [ 3], [ 3]],[[ 51], [ 49], [ 54], [ 57], [ 50], [ 85], [ 80], [ 81], [ 71], [ 83], [ 90], [ 95], [ 97], [ 94], [ 90], [ 99], [101], [103], [ 89], [ 92], [188], [190], [177], [179], [175], [ 96], [109], [103], [ 89], [ 96], [ 8], [ 4]],[[ 53], [ 56], [ 54], [ 51], [ 58], [ 73], [ 81], [ 79], [ 75], [ 81], [ 98], [100], [ 88], [ 90], [ 98], [101], [100], [106], [ 94], [ 96], [183], [185], [173], [178], [186], [102], [104], [ 92], [106], [ 87], [ 7], [ 11]],[[ 47], [ 51], [ 50], [ 47], [ 55], [ 71], [ 79], [ 77], [ 81], [ 70], [ 91], [ 96], [ 94], [100], [ 92], [102], [ 94], [ 99], [ 98], [112], [166], [179], [178], [184], [158], [106], [109], [ 99], [109], [ 97], [ 2], [ 6]],[[ 54], [ 58], [ 57], [ 54], [ 61], [ 76], [ 83], [ 81], [ 84], [ 79], [127], [128], [120], [129], [132], [186], [206], [207], [204], [190], [138], [125], [121], [123], [151], [152], [163], [164], [165], [155], [ 17], [ 10]],[[ 54], [ 57], [ 56], [ 52], [ 57], [ 71], [ 76], [ 73], [ 71], [ 67], [128], [124], [114], [121], [129], [218], [207], [206], [211], [211], [121], [122], [128], [127], [119], [157], [166], [168], [162], [160], [ 12], [ 10]],[[ 54], [ 59], [ 58], [ 54], [ 60], [ 73], [ 79], [ 75], [ 91], [ 76], [124], [125], [127], [129], [121], [203], [207], [194], [194], [204], [117], [129], [129], [116], [127], [172], [168], [165], [158], [168], [ 17], [ 13]],[[ 50], [ 54], [ 55], [ 52], [ 59], [ 73], [ 78], [ 75], [ 71], [ 72], [116], [124], [130], [125], [122], [201], [211], [214], [214], [214], [124], [125], [126], [128], [121], [158], [160], [164], [161], [166], [ 13], [ 11]],[[ 54], [ 59], [ 58], [ 54], [ 60], [ 71], [ 75], [ 71], [ 79], [ 93], [118], [123], [126], [115], [130], [209], [199], [212], [206], [197], [131], [122], [116], [129], [139], [154], [159], [167], [168], [162], [ 7], [ 3]],[[ 96], [ 99], [ 96], [ 90], [ 93], [101], [103], [ 97], [ 96], [ 92], [ 73], [ 68], [ 78], [ 66], [ 77], [135], [143], [144], [131], [153], [166], [188], [175], [176], [167], [153], [142], [138], [140], [136], [ 4], [ 5]],[[ 95], [ 95], [ 95], [ 95], [ 95], [ 96], [ 98], [ 98], [ 98], [ 94], [ 75], [ 69], [ 74], [ 78], [ 67], [144], [142], [143], [142], [140], [180], [178], [177], [178], [178], [139], [150], [143], [132], [140], [ 13], [ 1]],[[ 93], [ 93], [ 94], [ 95], [ 96], [ 97], [ 99], [ 99], [103], [101], [ 80], [ 72], [ 70], [ 73], [ 68], [143], [142], [143], [142], [140], [181], [178], [177], [179], [176], [133], [142], [148], [141], [141], [ 8], [ 1]],[[ 93], [ 94], [ 95], [ 96], [ 98], [ 99], [100], [101], [ 94], [ 95], [ 74], [ 70], [ 67], [ 73], [ 75], [143], [140], [142], [141], [138], [180], [177], [176], [178], [188], [143], [137], [143], [141], [143], [ 8], [ 7]],[[ 92], [ 93], [ 94], [ 94], [ 95], [ 95], [ 95], [ 94], [100], [101], [ 77], [ 78], [ 75], [ 81], [ 83], [132], [143], [144], [144], [141], [183], [181], [180], [182], [166], [147], [140], [140], [138], [144], [ 7], [ 4]],[[138], [138], [138], [138], [137], [135], [133], [132], [129], [125], [ 93], [ 96], [ 91], [ 92], [ 87], [110], [100], [102], [101], [ 99], [141], [139], [138], [140], [156], [185], [195], [194], [189], [194], [ 15], [ 5]],[[137], [137], [137], [136], [134], [130], [127], [126], [136], [132], [ 95], [104], [ 99], [100], [ 95], [ 99], [102], [104], [104], [102], [144], [142], [142], [144], [133], [189], [197], [191], [186], [195], [ 5], [ 5]],[[137], [138], [138], [137], [135], [132], [129], [127], [127], [126], [ 90], [102], [ 96], [100], [102], [ 99], [101], [103], [102], [101], [143], [141], [141], [143], [146], [199], [192], [186], [185], [197], [ 5], [ 13]],[[135], [136], [137], [137], [136], [133], [130], [128], [131], [132], [ 95], [105], [ 93], [ 96], [103], [ 98], [101], [103], [102], [101], [143], [141], [141], [143], [143], [194], [184], [191], [191], [198], [ 3], [ 10]],[[138], [134], [133], [134], [131], [127], [130], [136], [135], [125], [ 99], [ 98], [101], [ 98], [105], [ 94], [105], [100], [100], [113], [133], [146], [146], [142], [152], [186], [188], [190], [179], [180], [ 13], [ 2]],[[154], [155], [155], [159], [165], [174], [181], [185], [182], [172], [142], [135], [135], [131], [130], [110], [105], [102], [100], [100], [102], [103], [101], [ 98], [ 97], [118], [108], [112], [109], [125], [ 10], [ 4]],[[146], [148], [146], [145], [156], [173], [182], [180], [187], [180], [148], [138], [142], [144], [139], [110], [115], [117], [117], [112], [104], [ 99], [ 99], [101], [ 94], [117], [111], [117], [109], [115], [ 10], [ 1]],[[156], [159], [156], [150], [161], [183], [190], [185], [180], [174], [141], [128], [135], [143], [138], [104], [101], [105], [107], [104], [ 98], [ 95], [ 98], [102], [101], [115], [106], [111], [105], [109], [ 13], [ 4]],[[150], [152], [148], [143], [157], [179], [187], [181], [190], [185], [151], [134], [136], [142], [138], [106], [109], [109], [109], [106], [103], [100], [100], [100], [102], [113], [106], [116], [111], [102], [ 14], [ 4]],[[155], [157], [155], [153], [161], [175], [181], [177], [176], [174], [147], [135], [135], [136], [138], [116], [109], [107], [104], [103], [103], [101], [ 99], [ 97], [ 99], [110], [109], [122], [117], [102], [ 23], [ 4]],[[ 16], [ 15], [ 11], [ 9], [ 6], [ 4], [ 4], [ 5], [ 8], [ 12], [ 9], [ 11], [ 14], [ 12], [ 5], [ 4], [ 6], [ 10], [ 13], [ 8], [ 5], [ 9], [ 3], [ 2], [ 5], [ 8], [ 10], [ 11], [ 10], [ 10], [ 1], [ 4]],[[ 1], [ 1], [ 3], [ 6], [ 8], [ 7], [ 7], [ 6], [ 2], [ 5], [ 6], [ 9], [ 12], [ 8], [ 6], [ 5], [ 2], [ 2], [ 9], [ 6], [ 7], [ 7], [ 4], [ 7], [ 4], [ 5], [ 4], [ 7], [ 4], [ 7], [ 5], [ 12]]], dtype=np.uint8), ) image_decoder_decode_jpeg_bgr = ImageData( image_decoder_decode_jpeg_rgb.data, np.array([[[255, 73, 230], [255, 76, 227], [255, 81, 222], [206, 95, 211], [142, 123, 204], [133, 202, 251], [ 72, 226, 245], [ 34, 230, 234], [ 32, 234, 235], [ 32, 229, 231], [ 37, 221, 227], [ 45, 224, 233], [ 42, 227, 237], [ 32, 216, 232], [ 35, 219, 243], [ 63, 247, 255], [ 71, 255, 246], [ 83, 255, 249], [ 68, 253, 243], [134, 251, 255], [ 46, 23, 97], [121, 19, 127], [124, 0, 123], [128, 19, 118], [ 82, 28, 63], [143, 133, 99], [128, 146, 39], [110, 162, 24], [ 64, 157, 26], [ 76, 154, 66], [ 0, 9, 0], [ 9, 0, 15]], [[255, 77, 232], [255, 79, 228], [254, 83, 221], [208, 93, 210], [146, 117, 202], [137, 193, 248], [ 77, 216, 242], [ 35, 222, 230], [ 40, 237, 239], [ 36, 233, 235], [ 37, 225, 230], [ 41, 227, 235], [ 42, 228, 238], [ 34, 220, 230], [ 41, 225, 237], [ 70, 253, 255], [ 68, 250, 242], [ 70, 251, 242], [ 79, 255, 255], [124, 232, 255], [ 57, 32, 112], [108, 9, 119], [129, 10, 132], [121, 21, 117], [ 95, 50, 83], [134, 132, 98], [108, 137, 34], [106, 165, 31], [ 75, 168, 40], [ 71, 151, 62], [ 0, 9, 0], [ 5, 0, 11]], [[255, 81, 234], [255, 82, 229], [250, 86, 221], [218, 95, 211], [164, 115, 207], [161, 189, 254], [101, 213, 249], [ 57, 224, 238], [ 41, 232, 235], [ 30, 229, 231], [ 26, 221, 229], [ 26, 221, 229], [ 29, 225, 231], [ 27, 219, 218], [ 45, 229, 215], [ 71, 252, 237], [ 76, 252, 252], [ 80, 248, 255], [ 94, 249, 255], [135, 225, 255], [ 58, 30, 120], [101, 6, 120], [116, 9, 126], [104, 20, 109], [ 72, 47, 75], [113, 130, 97], [ 98, 144, 48], [ 89, 163, 37], [ 72, 169, 48], [ 66, 151, 59], [ 0, 16, 0], [ 0, 13, 0]], [[236, 83, 216], [238, 83, 211], [246, 88, 205], [226, 98, 203], [181, 114, 205], [178, 184, 255], [120, 207, 255], [ 77, 220, 251], [ 56, 228, 246], [ 46, 228, 247], [ 43, 216, 248], [ 45, 214, 251], [ 50, 217, 250], [ 51, 217, 234], [ 69, 232, 217], [ 95, 255, 234], [ 93, 251, 245], [104, 254, 255], [101, 232, 252], [153, 228, 255], [ 52, 34, 111], [103, 33, 126], [ 93, 15, 109], [ 91, 34, 103], [ 39, 33, 52], [ 99, 127, 98], [106, 152, 75], [ 86, 148, 48], [ 78, 151, 53], [ 83, 148, 69], [ 0, 16, 0], [ 0, 15, 0]], [[227, 104, 202], [235, 103, 196], [253, 106, 191], [242, 111, 192], [199, 117, 200], [192, 176, 253], [127, 193, 255], [ 85, 204, 255], [ 67, 210, 255], [ 67, 209, 255], [ 76, 195, 255], [ 82, 189, 255], [ 88, 193, 255], [ 88, 197, 255], [ 97, 217, 229], [118, 246, 229], [123, 250, 228], [148, 255, 249], [145, 251, 245], [155, 224, 239], [ 49, 57, 97], [ 58, 35, 87], [ 52, 21, 72], [ 68, 48, 83], [ 65, 76, 80], [ 96, 122, 99], [110, 136, 88], [ 98, 123, 63], [108, 127, 70], [115, 130, 86], [ 0, 9, 0], [ 0, 7, 0]], [[128, 27, 83], [137, 23, 76], [161, 23, 70], [151, 22, 73], [103, 16, 84], [ 86, 60, 138], [ 13, 66, 146], [ 0, 72, 150], [ 0, 73, 146], [ 0, 71, 164], [ 0, 55, 186], [ 15, 48, 198], [ 24, 51, 202], [ 19, 56, 176], [ 16, 81, 136], [ 31, 114, 116], [ 38, 131, 94], [ 17, 112, 62], [ 23, 109, 75], [ 55, 125, 108], [128, 175, 173], [162, 194, 200], [175, 197, 203], [153, 174, 175], [131, 161, 150], [ 95, 117, 98], [102, 105, 83], [117, 103, 80], [143, 111, 92], [138, 107, 92], [ 12, 3, 0], [ 2, 5, 0]], [[132, 43, 52], [142, 40, 45], [167, 38, 41], [159, 35, 51], [103, 21, 70], [ 82, 55, 129], [ 6, 53, 144], [ 0, 53, 155], [ 0, 53, 163], [ 10, 52, 189], [ 53, 32, 224], [ 77, 23, 242], [ 88, 25, 251], [ 77, 33, 223], [ 61, 59, 172], [ 65, 98, 131], [ 55, 111, 62], [ 58, 130, 47], [ 72, 146, 80], [ 44, 128, 73], [104, 200, 153], [ 86, 183, 139], [120, 202, 160], [139, 206, 169], [155, 203, 174], [ 91, 109, 92], [116, 92, 92], [148, 90, 101], [171, 88, 103], [160, 87, 97], [ 23, 0, 0], [ 4, 4, 0]], [[140, 47, 26], [146, 44, 21], [169, 48, 22], [158, 47, 39], [103, 31, 67], [ 83, 62, 131], [ 14, 56, 153], [ 0, 52, 170], [ 0, 37, 164], [ 25, 37, 195], [ 71, 21, 233], [100, 13, 255], [111, 14, 255], [103, 20, 236], [ 88, 45, 180], [ 92, 84, 131], [ 92, 118, 70], [ 85, 136, 46], [ 59, 118, 43], [ 41, 122, 53], [106, 222, 153], [100, 225, 156], [101, 209, 143], [122, 205, 150], [141, 192, 155], [ 91, 101, 89], [137, 98, 119], [161, 83, 120], [174, 65, 104], [167, 78, 104], [ 27, 6, 4], [ 0, 7, 0]], [[161, 50, 18], [164, 54, 20], [166, 53, 13], [140, 47, 26], [ 99, 44, 69], [ 64, 52, 118], [ 23, 55, 155], [ 0, 48, 170], [ 2, 41, 169], [ 26, 36, 190], [ 71, 35, 233], [ 91, 26, 249], [ 92, 10, 239], [102, 23, 218], [117, 54, 177], [119, 83, 129], [105, 112, 75], [101, 131, 58], [ 78, 116, 56], [ 57, 120, 64], [110, 214, 151], [103, 217, 153], [109, 203, 138], [134, 202, 147], [163, 199, 169], [104, 102, 102], [132, 88, 124], [147, 67, 120], [184, 78, 131], [147, 68, 101], [ 7, 9, 3], [ 0, 18, 0]], [[136, 46, 15], [138, 52, 16], [135, 53, 11], [114, 46, 23], [ 89, 43, 66], [ 76, 48, 114], [ 60, 46, 151], [ 45, 39, 164], [ 51, 43, 167], [ 38, 28, 164], [ 54, 43, 200], [ 59, 43, 214], [ 59, 42, 210], [ 79, 56, 195], [ 94, 65, 144], [114, 93, 115], [106, 103, 72], [108, 116, 63], [103, 111, 70], [103, 127, 87], [129, 187, 139], [138, 201, 151], [155, 198, 147], [177, 198, 160], [162, 162, 150], [124, 97, 117], [141, 86, 143], [147, 68, 142], [176, 76, 148], [145, 74, 124], [ 0, 0, 6], [ 0, 10, 0]], [[ 88, 57, 36], [ 92, 62, 37], [ 91, 65, 29], [ 88, 55, 39], [ 95, 47, 76], [115, 47, 118], [134, 39, 150], [132, 32, 158], [123, 41, 154], [ 86, 43, 146], [ 79, 101, 196], [ 45, 111, 194], [ 17, 109, 180], [ 37, 126, 170], [ 77, 138, 140], [165, 197, 173], [215, 215, 185], [235, 214, 183], [239, 206, 186], [225, 192, 173], [165, 142, 120], [157, 128, 107], [171, 121, 101], [178, 118, 112], [199, 138, 158], [195, 129, 180], [198, 130, 215], [203, 125, 226], [216, 126, 222], [189, 125, 201], [ 3, 6, 44], [ 0, 5, 23]], [[ 65, 58, 43], [ 66, 62, 43], [ 63, 65, 36], [ 64, 55, 42], [ 84, 44, 72], [121, 41, 112], [156, 28, 140], [158, 21, 143], [140, 28, 130], [ 86, 39, 115], [ 70, 121, 164], [ 14, 132, 151], [ 0, 127, 131], [ 0, 142, 127], [ 55, 151, 115], [190, 235, 196], [218, 214, 189], [245, 206, 192], [255, 208, 201], [255, 207, 202], [188, 114, 110], [202, 113, 109], [225, 115, 117], [224, 109, 126], [195, 95, 137], [212, 127, 195], [201, 129, 225], [200, 127, 237], [203, 120, 229], [188, 125, 217], [ 0, 0, 41], [ 0, 0, 32]], [[ 73, 55, 44], [ 73, 63, 46], [ 63, 66, 41], [ 59, 57, 46], [ 76, 50, 74], [108, 47, 111], [146, 35, 141], [148, 27, 142], [148, 52, 146], [ 85, 55, 114], [ 55, 130, 139], [ 10, 147, 125], [ 0, 155, 119], [ 16, 158, 115], [ 68, 140, 104], [194, 213, 186], [226, 209, 196], [235, 190, 186], [247, 187, 187], [255, 197, 198], [202, 105, 109], [232, 113, 121], [249, 108, 123], [231, 91, 122], [214, 99, 149], [230, 141, 211], [200, 133, 225], [190, 126, 232], [192, 118, 224], [196, 132, 227], [ 14, 0, 51], [ 4, 0, 43]], [[ 59, 57, 33], [ 59, 62, 36], [ 53, 66, 34], [ 52, 58, 39], [ 70, 52, 69], [105, 50, 107], [140, 36, 137], [148, 27, 142], [131, 30, 128], [ 93, 49, 110], [ 68, 120, 126], [ 32, 146, 116], [ 18, 160, 115], [ 31, 150, 112], [ 81, 131, 121], [196, 200, 205], [227, 206, 215], [242, 206, 218], [248, 207, 215], [255, 206, 214], [193, 113, 120], [215, 110, 121], [234, 106, 125], [229, 105, 135], [192, 97, 141], [202, 133, 190], [178, 133, 206], [176, 135, 216], [184, 132, 210], [193, 139, 209], [ 21, 0, 36], [ 18, 0, 29]], [[ 33, 75, 22], [ 40, 79, 27], [ 44, 77, 26], [ 50, 65, 33], [ 72, 57, 61], [106, 50, 99], [141, 32, 134], [153, 19, 143], [162, 29, 146], [150, 57, 142], [120, 108, 136], [ 84, 131, 123], [ 57, 141, 123], [ 48, 124, 123], [ 91, 121, 162], [193, 192, 248], [197, 185, 227], [214, 201, 233], [201, 199, 221], [197, 192, 207], [150, 126, 134], [161, 114, 122], [174, 105, 116], [184, 116, 133], [171, 127, 150], [165, 143, 171], [154, 149, 181], [160, 157, 189], [177, 156, 188], [181, 152, 175], [ 30, 0, 11], [ 27, 0, 1]], [[ 61, 122, 58], [ 64, 125, 61], [ 64, 121, 60], [ 71, 106, 66], [ 97, 93, 92], [133, 81, 129], [170, 59, 165], [184, 41, 174], [185, 40, 172], [165, 48, 151], [112, 50, 102], [ 73, 60, 82], [ 50, 77, 91], [ 27, 59, 94], [ 50, 53, 134], [114, 106, 199], [124, 121, 193], [121, 127, 186], [101, 115, 173], [121, 143, 184], [139, 165, 179], [170, 189, 192], [175, 172, 181], [176, 174, 180], [155, 169, 168], [134, 158, 150], [119, 151, 134], [120, 148, 125], [133, 150, 123], [140, 143, 121], [ 17, 4, 0], [ 26, 3, 0]], [[ 61, 119, 61], [ 54, 121, 60], [ 38, 124, 60], [ 47, 116, 71], [ 79, 97, 96], [119, 76, 127], [167, 51, 164], [183, 39, 181], [172, 41, 181], [161, 45, 164], [139, 40, 118], [113, 45, 100], [ 85, 56, 106], [ 71, 56, 124], [ 60, 31, 140], [126, 107, 224], [105, 114, 212], [ 97, 116, 213], [ 99, 109, 223], [ 89, 116, 206], [109, 181, 205], [100, 190, 185], [110, 182, 192], [116, 185, 188], [118, 194, 170], [ 89, 162, 112], [118, 180, 104], [120, 176, 87], [105, 166, 76], [121, 165, 98], [ 7, 20, 0], [ 5, 0, 0]], [[ 72, 114, 61], [ 59, 116, 61], [ 37, 121, 63], [ 44, 114, 77], [ 78, 97, 100], [120, 77, 128], [168, 54, 161], [182, 43, 178], [169, 50, 183], [163, 52, 174], [158, 37, 135], [139, 35, 120], [104, 38, 120], [ 87, 39, 135], [ 77, 24, 151], [132, 100, 231], [ 98, 109, 223], [ 86, 111, 227], [ 94, 101, 240], [ 82, 111, 220], [ 92, 183, 210], [ 79, 193, 186], [ 85, 186, 195], [ 94, 191, 189], [ 98, 199, 161], [ 71, 165, 94], [105, 181, 80], [118, 190, 76], [100, 184, 72], [104, 174, 91], [ 0, 13, 0], [ 0, 1, 0]], [[ 91, 110, 59], [ 82, 111, 65], [ 63, 113, 73], [ 68, 107, 86], [ 98, 94, 106], [131, 79, 127], [169, 62, 149], [179, 54, 163], [154, 47, 163], [155, 48, 164], [154, 26, 138], [144, 23, 134], [111, 23, 136], [ 98, 30, 149], [ 94, 27, 162], [141, 98, 231], [ 99, 106, 223], [ 87, 110, 226], [ 95, 102, 235], [ 85, 110, 214], [102, 181, 208], [ 92, 190, 184], [103, 184, 189], [112, 189, 181], [126, 211, 167], [ 90, 174, 102], [ 98, 173, 81], [104, 181, 83], [ 88, 179, 86], [ 95, 173, 102], [ 0, 14, 0], [ 0, 12, 1]], [[ 88, 95, 88], [ 81, 95, 94], [ 64, 95, 104], [ 66, 90, 114], [ 88, 81, 124], [111, 73, 133], [138, 65, 138], [142, 60, 143], [132, 66, 155], [142, 62, 163], [148, 29, 144], [154, 26, 151], [130, 25, 152], [124, 33, 160], [124, 35, 162], [157, 91, 204], [138, 115, 200], [125, 120, 199], [130, 115, 207], [114, 121, 190], [128, 187, 196], [115, 194, 180], [122, 189, 186], [125, 193, 182], [102, 189, 145], [ 81, 176, 115], [ 80, 170, 104], [ 74, 171, 105], [ 60, 167, 110], [ 75, 167, 124], [ 0, 12, 0], [ 0, 7, 0]], [[111, 115, 194], [103, 114, 198], [ 88, 113, 207], [ 87, 112, 210], [ 96, 111, 204], [108, 111, 192], [121, 113, 178], [123, 114, 171], [111, 111, 171], [127, 99, 175], [144, 49, 159], [169, 44, 170], [158, 39, 167], [159, 43, 164], [160, 43, 146], [181, 77, 148], [162, 84, 108], [155, 93, 99], [147, 89, 107], [122, 94, 100], [122, 153, 124], [ 96, 157, 121], [ 92, 148, 135], [ 82, 152, 139], [ 76, 179, 142], [ 91, 211, 170], [ 92, 217, 191], [ 84, 212, 200], [ 70, 206, 202], [ 93, 207, 207], [ 0, 19, 14], [ 0, 8, 2]], [[ 93, 98, 230], [ 87, 97, 234], [ 77, 97, 239], [ 74, 97, 236], [ 79, 101, 220], [ 80, 105, 197], [ 84, 113, 170], [ 85, 117, 158], [ 92, 127, 170], [118, 112, 177], [143, 53, 159], [184, 50, 180], [182, 44, 175], [192, 48, 167], [198, 51, 143], [205, 69, 117], [205, 93, 81], [198, 105, 66], [182, 105, 73], [148, 108, 73], [142, 160, 113], [112, 161, 117], [103, 154, 134], [ 84, 158, 140], [ 44, 156, 122], [ 77, 213, 185], [ 73, 213, 214], [ 58, 200, 223], [ 51, 192, 225], [ 89, 201, 224], [ 0, 7, 4], [ 0, 8, 0]], [[ 82, 93, 245], [ 81, 93, 247], [ 80, 92, 250], [ 79, 95, 242], [ 82, 101, 222], [ 81, 109, 196], [ 79, 119, 168], [ 79, 122, 155], [ 81, 120, 158], [115, 106, 169], [148, 45, 156], [199, 44, 179], [201, 36, 173], [215, 44, 166], [233, 53, 148], [232, 66, 113], [220, 92, 73], [204, 108, 54], [184, 110, 56], [147, 113, 59], [139, 160, 111], [109, 159, 119], [100, 153, 133], [ 83, 158, 137], [ 54, 170, 133], [ 84, 222, 198], [ 63, 203, 220], [ 53, 189, 231], [ 61, 183, 235], [110, 197, 229], [ 0, 8, 0], [ 21, 18, 0]], [[ 57, 106, 222], [ 60, 107, 223], [ 66, 106, 225], [ 70, 109, 218], [ 72, 115, 202], [ 69, 120, 182], [ 64, 128, 159], [ 64, 129, 150], [ 71, 129, 158], [107, 118, 170], [138, 59, 150], [185, 58, 167], [178, 46, 152], [192, 54, 142], [216, 68, 128], [218, 76, 95], [218, 99, 60], [208, 113, 44], [187, 116, 42], [152, 117, 49], [147, 159, 111], [116, 154, 124], [113, 149, 135], [ 93, 153, 142], [ 61, 162, 137], [ 86, 209, 205], [ 58, 185, 230], [ 62, 183, 255], [ 73, 182, 255], [116, 190, 246], [ 0, 5, 0], [ 25, 12, 0]], [[ 26, 145, 167], [ 28, 140, 163], [ 37, 137, 162], [ 45, 137, 162], [ 46, 136, 153], [ 42, 134, 145], [ 40, 140, 144], [ 46, 146, 150], [ 45, 142, 156], [ 65, 122, 153], [ 99, 81, 134], [126, 76, 130], [126, 85, 123], [128, 89, 105], [153, 102, 92], [165, 97, 60], [202, 114, 50], [205, 115, 31], [189, 119, 28], [173, 131, 54], [151, 144, 105], [141, 151, 138], [141, 146, 147], [117, 143, 149], [ 92, 159, 160], [ 96, 188, 217], [ 77, 176, 255], [ 77, 179, 255], [ 64, 162, 255], [ 89, 159, 255], [ 0, 6, 31], [ 5, 3, 0]], [[ 23, 197, 119], [ 29, 197, 121], [ 42, 193, 124], [ 49, 194, 132], [ 55, 196, 145], [ 59, 203, 161], [ 59, 206, 178], [ 59, 207, 191], [ 58, 199, 196], [ 75, 183, 187], [ 99, 145, 153], [117, 138, 135], [110, 148, 118], [115, 152, 96], [139, 155, 78], [153, 135, 46], [192, 125, 33], [205, 121, 26], [192, 122, 21], [166, 117, 43], [127, 105, 87], [107, 97, 114], [105, 90, 121], [ 84, 86, 126], [ 49, 88, 132], [ 42, 104, 175], [ 9, 78, 205], [ 0, 73, 231], [ 0, 69, 230], [ 35, 95, 219], [ 0, 0, 34], [ 2, 3, 7]], [[ 10, 226, 41], [ 15, 226, 46], [ 20, 219, 51], [ 16, 212, 62], [ 16, 215, 93], [ 22, 224, 131], [ 21, 223, 164], [ 13, 214, 177], [ 22, 217, 191], [ 42, 207, 180], [ 63, 173, 131], [ 78, 168, 102], [ 82, 184, 82], [101, 193, 64], [134, 188, 45], [147, 152, 13], [198, 143, 28], [218, 139, 36], [209, 140, 37], [178, 125, 62], [127, 96, 111], [101, 78, 140], [ 97, 73, 151], [ 83, 72, 164], [ 50, 65, 167], [ 50, 83, 210], [ 28, 64, 234], [ 23, 67, 252], [ 8, 63, 238], [ 37, 82, 210], [ 0, 0, 34], [ 4, 0, 1]], [[ 23, 255, 14], [ 27, 255, 22], [ 23, 248, 27], [ 10, 234, 38], [ 5, 234, 77], [ 13, 245, 127], [ 7, 239, 163], [ 0, 225, 176], [ 0, 216, 178], [ 15, 209, 166], [ 29, 177, 113], [ 41, 171, 78], [ 50, 189, 62], [ 83, 200, 53], [127, 191, 38], [143, 149, 2], [190, 133, 4], [213, 131, 12], [206, 132, 20], [177, 116, 52], [125, 85, 113], [ 95, 67, 150], [ 91, 66, 164], [ 81, 66, 181], [ 55, 63, 194], [ 51, 71, 226], [ 33, 52, 239], [ 29, 56, 250], [ 15, 57, 234], [ 38, 75, 203], [ 0, 0, 42], [ 0, 2, 10]], [[ 19, 251, 0], [ 23, 252, 5], [ 17, 241, 15], [ 1, 228, 29], [ 0, 231, 70], [ 4, 242, 122], [ 3, 237, 159], [ 0, 222, 171], [ 6, 228, 186], [ 22, 222, 175], [ 29, 188, 126], [ 33, 174, 93], [ 37, 184, 80], [ 73, 189, 76], [127, 175, 69], [154, 138, 25], [212, 140, 8], [235, 140, 1], [224, 138, 8], [191, 121, 44], [137, 91, 113], [102, 74, 150], [ 88, 72, 160], [ 70, 68, 174], [ 51, 65, 195], [ 49, 69, 224], [ 40, 53, 235], [ 43, 63, 248], [ 26, 67, 230], [ 29, 70, 192], [ 0, 0, 46], [ 0, 0, 15]], [[ 58, 234, 37], [ 60, 233, 45], [ 58, 225, 55], [ 47, 216, 70], [ 39, 215, 101], [ 44, 220, 137], [ 48, 216, 163], [ 42, 207, 170], [ 43, 205, 170], [ 55, 203, 163], [ 55, 176, 125], [ 52, 168, 103], [ 43, 173, 96], [ 70, 170, 94], [133, 159, 99], [160, 133, 66], [192, 131, 33], [206, 133, 19], [200, 130, 17], [174, 117, 48], [126, 93, 114], [ 96, 80, 144], [ 83, 81, 141], [ 69, 78, 146], [ 58, 73, 166], [ 66, 77, 191], [ 70, 68, 204], [ 78, 81, 219], [ 59, 83, 205], [ 47, 79, 168], [ 0, 13, 50], [ 0, 1, 10]], [[ 0, 28, 0], [ 0, 25, 0], [ 0, 19, 0], [ 0, 15, 0], [ 0, 10, 0], [ 0, 7, 0], [ 0, 4, 5], [ 0, 6, 5], [ 0, 13, 1], [ 0, 21, 0], [ 0, 15, 0], [ 0, 19, 0], [ 0, 24, 0], [ 0, 20, 0], [ 6, 8, 0], [ 30, 1, 0], [ 28, 5, 0], [ 32, 10, 0], [ 41, 15, 0], [ 28, 9, 0], [ 0, 0, 18], [ 0, 0, 31], [ 0, 0, 10], [ 0, 1, 4], [ 0, 0, 18], [ 0, 0, 27], [ 7, 0, 32], [ 5, 0, 36], [ 0, 0, 35], [ 0, 1, 31], [ 0, 1, 0], [ 7, 5, 0]], [[ 0, 2, 0], [ 0, 0, 3], [ 4, 0, 10], [ 10, 0, 17], [ 11, 0, 22], [ 14, 0, 18], [ 25, 0, 15], [ 29, 0, 9], [ 21, 0, 0], [ 18, 5, 0], [ 11, 8, 0], [ 0, 16, 0], [ 0, 20, 0], [ 0, 13, 0], [ 15, 4, 7], [ 25, 0, 8], [ 2, 0, 6], [ 0, 4, 0], [ 16, 13, 0], [ 9, 8, 0], [ 0, 0, 22], [ 0, 0, 25], [ 0, 7, 0], [ 0, 12, 0], [ 0, 7, 0], [ 15, 5, 0], [ 32, 0, 1], [ 33, 0, 10], [ 10, 0, 9], [ 11, 6, 8], [ 14, 5, 0], [ 27, 16, 0]]], dtype=np.uint8), ) image_decoder_decode_jpeg2k_rgb = ImageData( np.array([ 0, 0, 0, 12, 106, 80, 32, 32, 13, 10, 135, 10, 0, 0, 0, 20, 102, 116, 121, 112, 106, 112, 50, 32, 0, 0, 0, 0, 106, 112, 50, 32, 0, 0, 0, 45, 106, 112, 50, 104, 0, 0, 0, 22, 105, 104, 100, 114, 0, 0, 0, 32, 0, 0, 0, 32, 0, 3, 7, 7, 0, 0, 0, 0, 0, 15, 99, 111, 108, 114, 1, 0, 0, 0, 0, 0, 16, 0, 0, 7, 18, 106, 112, 50, 99, 255, 79, 255, 81, 0, 47, 0, 0, 0, 0, 0, 32, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 1, 1, 7, 1, 1, 7, 1, 1, 255, 82, 0, 12, 0, 0, 0, 1, 0, 5, 4, 4, 0, 1, 255, 92, 0, 19, 64, 64, 72, 72, 80, 72, 72, 80, 72, 72, 80, 72, 72, 80, 72, 72, 80, 255, 100, 0, 37, 0, 1, 67, 114, 101, 97, 116, 101, 100, 32, 98, 121, 32, 79, 112, 101, 110, 74, 80, 69, 71, 32, 118, 101, 114, 115, 105, 111, 110, 32, 50, 46, 53, 46, 48, 255, 144, 0, 10, 0, 0, 0, 0, 6, 139, 0, 1, 255, 147, 195, 231, 4, 0, 31, 193, 242, 2, 9, 127, 195, 231, 2, 7, 195, 234, 2, 143, 180, 10, 31, 104, 16, 4, 115, 5, 159, 0, 79, 199, 218, 5, 15, 168, 10, 31, 104, 8, 3, 25, 8, 159, 9, 193, 243, 130, 131, 231, 5, 63, 48, 32, 9, 111, 8, 127, 4, 167, 199, 218, 11, 63, 0, 104, 252, 1, 64, 14, 25, 236, 129, 63, 10, 242, 160, 112, 52, 191, 3, 34, 223, 152, 23, 207, 192, 22, 62, 208, 88, 125, 160, 192, 14, 120, 105, 193, 230, 9, 210, 147, 177, 25, 9, 144, 148, 195, 126, 191, 207, 192, 26, 62, 208, 88, 252, 1, 64, 7, 247, 239, 48, 181, 159, 12, 90, 58, 122, 9, 12, 13, 199, 195, 143, 207, 192, 78, 126, 2, 113, 248, 10, 0, 22, 170, 96, 67, 117, 221, 185, 238, 123, 216, 135, 177, 65, 60, 79, 209, 148, 7, 159, 1, 17, 79, 179, 172, 17, 21, 60, 101, 99, 223, 82, 254, 246, 110, 40, 44, 0, 127, 21, 238, 219, 43, 124, 121, 243, 213, 250, 63, 49, 33, 98, 47, 113, 45, 170, 153, 160, 243, 207, 192, 78, 126, 2, 144, 251, 68, 64, 34, 100, 150, 169, 72, 29, 190, 139, 77, 175, 243, 121, 86, 141, 46, 115, 17, 255, 127, 27, 206, 245, 36, 137, 3, 88, 150, 229, 17, 22, 80, 214, 179, 155, 191, 9, 119, 234, 159, 22, 133, 163, 43, 90, 41, 169, 46, 72, 36, 172, 70, 56, 17, 202, 131, 183, 199, 218, 37, 63, 1, 41, 249, 137, 128, 20, 147, 110, 123, 141, 178, 193, 87, 126, 222, 22, 15, 24, 73, 29, 111, 114, 149, 32, 218, 201, 39, 120, 134, 155, 184, 134, 66, 58, 217, 10, 0, 156, 49, 44, 239, 22, 2, 193, 92, 254, 178, 255, 95, 37, 196, 94, 93, 83, 106, 3, 55, 215, 87, 63, 223, 154, 12, 252, 17, 35, 240, 60, 57, 83, 92, 9, 186, 184, 253, 97, 108, 204, 42, 121, 198, 54, 152, 214, 158, 133, 109, 255, 22, 77, 129, 217, 162, 182, 150, 79, 119, 10, 227, 28, 236, 98, 161, 32, 251, 158, 183, 120, 79, 138, 187, 174, 187, 111, 254, 30, 115, 99, 23, 127, 60, 252, 16, 118, 155, 70, 55, 232, 247, 73, 175, 224, 111, 60, 142, 99, 107, 203, 71, 232, 40, 144, 21, 187, 70, 32, 169, 182, 179, 169, 171, 82, 155, 15, 190, 227, 163, 167, 10, 254, 246, 118, 49, 244, 220, 255, 116, 207, 201, 87, 180, 16, 121, 97, 154, 12, 240, 180, 206, 164, 0, 13, 219, 227, 153, 33, 36, 212, 33, 89, 109, 6, 212, 168, 226, 221, 243, 0, 50, 17, 127, 97, 234, 151, 12, 94, 181, 84, 194, 7, 118, 33, 72, 104, 4, 221, 22, 70, 229, 50, 52, 80, 241, 134, 38, 236, 26, 66, 70, 23, 53, 244, 221, 116, 56, 165, 197, 163, 99, 7, 113, 30, 233, 241, 30, 91, 217, 146, 56, 101, 216, 39, 56, 250, 172, 211, 15, 16, 47, 65, 127, 207, 192, 254, 126, 7, 208, 251, 77, 192, 39, 93, 199, 157, 201, 217, 6, 20, 49, 113, 89, 51, 238, 155, 138, 137, 202, 96, 134, 221, 198, 47, 240, 209, 203, 72, 12, 202, 155, 22, 157, 81, 250, 5, 207, 71, 13, 208, 91, 14, 180, 66, 25, 170, 123, 251, 123, 200, 96, 140, 228, 107, 89, 93, 20, 28, 69, 249, 146, 179, 144, 250, 127, 62, 97, 226, 175, 173, 54, 142, 247, 5, 69, 105, 71, 176, 25, 38, 200, 187, 193, 148, 222, 254, 21, 255, 122, 230, 25, 106, 31, 176, 84, 10, 231, 179, 117, 255, 50, 202, 29, 225, 188, 22, 227, 11, 177, 103, 37, 205, 237, 14, 73, 88, 35, 83, 169, 242, 85, 211, 102, 4, 142, 68, 95, 37, 82, 80, 214, 20, 11, 88, 115, 195, 98, 106, 218, 114, 145, 123, 75, 75, 137, 231, 55, 77, 234, 255, 15, 84, 210, 255, 64, 64, 20, 121, 57, 78, 253, 249, 212, 65, 128, 151, 92, 94, 202, 110, 182, 34, 187, 7, 53, 92, 36, 83, 100, 165, 121, 71, 207, 192, 250, 126, 7, 176, 251, 77, 128, 36, 198, 178, 18, 76, 218, 211, 240, 218, 60, 13, 248, 75, 214, 67, 64, 18, 66, 236, 198, 84, 245, 51, 12, 3, 1, 92, 45, 35, 139, 118, 66, 222, 137, 88, 222, 75, 85, 44, 193, 125, 206, 91, 163, 92, 147, 203, 52, 24, 223, 120, 99, 100, 87, 72, 29, 61, 233, 70, 212, 108, 239, 82, 243, 93, 156, 226, 55, 118, 254, 179, 72, 189, 40, 125, 216, 140, 179, 219, 226, 142, 202, 239, 14, 133, 104, 196, 156, 84, 111, 170, 186, 228, 127, 214, 32, 165, 68, 129, 204, 171, 3, 120, 212, 6, 164, 158, 250, 178, 132, 138, 76, 206, 80, 43, 120, 0, 235, 221, 142, 194, 133, 111, 76, 233, 61, 247, 7, 103, 43, 113, 35, 140, 59, 224, 80, 183, 221, 52, 198, 22, 127, 253, 147, 102, 60, 126, 241, 91, 200, 146, 170, 69, 217, 123, 36, 141, 88, 160, 132, 121, 239, 104, 123, 227, 227, 123, 190, 157, 71, 12, 179, 28, 187, 245, 45, 111, 207, 193, 146, 126, 12, 112, 251, 77, 192, 172, 193, 118, 152, 92, 250, 245, 178, 145, 220, 2, 246, 200, 173, 130, 233, 43, 23, 207, 176, 154, 220, 79, 208, 152, 232, 49, 148, 197, 12, 28, 187, 215, 231, 74, 253, 114, 157, 45, 56, 62, 45, 55, 37, 63, 220, 131, 255, 32, 189, 251, 116, 36, 204, 16, 72, 124, 138, 94, 44, 96, 110, 62, 104, 57, 246, 44, 174, 179, 147, 146, 32, 91, 92, 114, 196, 87, 209, 170, 88, 95, 21, 139, 254, 78, 207, 156, 124, 151, 116, 227, 49, 30, 171, 162, 67, 120, 31, 199, 127, 172, 192, 52, 46, 151, 230, 141, 248, 110, 35, 243, 135, 232, 26, 43, 142, 196, 185, 12, 31, 123, 137, 118, 145, 48, 91, 56, 46, 85, 72, 18, 97, 34, 253, 207, 73, 6, 204, 217, 124, 228, 59, 146, 216, 33, 21, 121, 105, 196, 65, 232, 214, 181, 33, 112, 35, 55, 32, 33, 125, 46, 251, 203, 242, 226, 175, 225, 187, 121, 111, 111, 255, 29, 75, 9, 3, 194, 219, 238, 88, 133, 72, 196, 238, 51, 233, 109, 12, 25, 10, 233, 187, 9, 192, 207, 13, 156, 39, 179, 130, 200, 62, 211, 143, 151, 41, 170, 39, 11, 131, 60, 130, 78, 123, 42, 202, 95, 61, 144, 135, 25, 74, 217, 191, 126, 75, 2, 248, 229, 162, 158, 203, 118, 243, 164, 243, 12, 46, 4, 178, 182, 205, 93, 28, 245, 197, 208, 12, 129, 253, 202, 125, 52, 175, 199, 218, 191, 31, 106, 252, 62, 211, 16, 28, 232, 48, 162, 23, 193, 178, 85, 202, 167, 50, 213, 135, 203, 213, 27, 46, 88, 209, 38, 100, 195, 157, 78, 8, 92, 80, 131, 1, 63, 224, 212, 8, 26, 89, 41, 113, 24, 140, 81, 51, 226, 213, 93, 247, 195, 32, 162, 62, 37, 90, 178, 221, 91, 29, 208, 212, 99, 228, 11, 68, 39, 232, 182, 168, 128, 68, 47, 120, 91, 215, 14, 39, 113, 211, 156, 72, 108, 30, 128, 171, 43, 176, 182, 171, 113, 246, 165, 179, 121, 20, 155, 19, 123, 199, 34, 225, 202, 31, 38, 55, 160, 175, 114, 38, 236, 62, 89, 241, 175, 49, 106, 198, 41, 44, 173, 11, 243, 12, 6, 173, 111, 165, 11, 112, 162, 192, 47, 188, 234, 18, 87, 128, 156, 125, 24, 251, 76, 185, 246, 22, 208, 211, 181, 43, 9, 5, 168, 66, 222, 117, 48, 39, 165, 199, 250, 206, 38, 56, 205, 16, 78, 244, 43, 227, 148, 36, 44, 93, 250, 6, 159, 16, 86, 5, 48, 155, 224, 126, 49, 225, 114, 202, 48, 143, 249, 97, 218, 162, 47, 65, 170, 89, 251, 164, 134, 209, 85, 26, 37, 116, 11, 118, 109, 5, 54, 2, 191, 141, 206, 195, 31, 248, 32, 125, 3, 11, 136, 183, 82, 5, 75, 21, 29, 7, 133, 33, 216, 139, 51, 76, 2, 21, 246, 232, 226, 180, 170, 62, 199, 218, 181, 31, 106, 236, 62, 211, 80, 28, 76, 84, 159, 4, 65, 45, 39, 210, 159, 35, 192, 202, 118, 120, 113, 103, 253, 75, 75, 29, 51, 207, 47, 118, 61, 205, 24, 61, 63, 1, 17, 52, 104, 120, 192, 211, 103, 43, 123, 71, 170, 107, 8, 243, 28, 56, 166, 39, 181, 109, 213, 6, 39, 56, 190, 181, 90, 56, 15, 152, 255, 45, 19, 192, 235, 243, 23, 245, 230, 151, 239, 68, 55, 179, 23, 15, 235, 29, 68, 125, 242, 9, 113, 86, 86, 21, 82, 207, 67, 115, 187, 10, 73, 82, 210, 112, 95, 199, 163, 129, 28, 179, 43, 72, 187, 122, 30, 152, 227, 80, 163, 50, 129, 121, 138, 9, 19, 10, 19, 44, 101, 108, 69, 246, 105, 58, 85, 146, 155, 114, 216, 168, 49, 23, 223, 94, 202, 124, 62, 127, 16, 20, 86, 26, 183, 95, 189, 192, 165, 141, 55, 91, 79, 49, 229, 108, 77, 250, 123, 247, 184, 138, 47, 103, 176, 157, 8, 81, 201, 192, 156, 59, 240, 251, 47, 34, 71, 39, 3, 179, 38, 223, 110, 159, 133, 196, 85, 72, 241, 227, 104, 224, 110, 127, 216, 242, 88, 42, 220, 81, 93, 138, 63, 86, 78, 76, 149, 234, 116, 126, 32, 5, 40, 46, 103, 109, 144, 200, 232, 34, 217, 153, 38, 76, 185, 122, 147, 225, 110, 237, 245, 41, 121, 206, 151, 255, 217], dtype=np.uint8), np.array([[[226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 0, 0, 0], [ 0, 0, 0]], [[226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 0, 0, 0], [ 0, 0, 0]], [[226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 0, 0, 0], [ 0, 0, 0]], [[226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 0, 0, 0], [ 0, 0, 0]], [[226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [226, 81, 251], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [237, 229, 45], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [225, 227, 29], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [253, 253, 80], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [129, 8, 118], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 33, 164, 77], [ 0, 0, 0], [ 0, 0, 0]], [[ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [ 0, 0, 0], [ 0, 0, 0]], [[ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [ 0, 0, 0], [ 0, 0, 0]], [[ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [ 0, 0, 0], [ 0, 0, 0]], [[ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [ 0, 0, 0], [ 0, 0, 0]], [[ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [ 21, 47, 169], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [173, 45, 10], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [254, 15, 94], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [ 52, 126, 81], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [145, 211, 101], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [109, 86, 145], [ 0, 0, 0], [ 0, 0, 0]], [[ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [ 0, 0, 0], [ 0, 0, 0]], [[ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [ 0, 0, 0], [ 0, 0, 0]], [[ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [ 0, 0, 0], [ 0, 0, 0]], [[ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [ 0, 0, 0], [ 0, 0, 0]], [[ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [ 34, 66, 54], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [136, 29, 158], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [117, 152, 1], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [187, 208, 244], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [118, 108, 234], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [246, 113, 205], [ 0, 0, 0], [ 0, 0, 0]], [[ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 0, 0, 0], [ 0, 0, 0]], [[ 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0, 0]], [[ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 0, 0, 0], [ 0, 0, 0]], [[ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [ 64, 118, 59], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [157, 54, 167], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [142, 26, 122], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [236, 105, 88], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [184, 191, 91], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 76, 185, 95], [ 0, 0, 0], [ 0, 0, 0]], [[247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [ 0, 0, 0], [ 0, 0, 0]], [[247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [ 0, 0, 0], [ 0, 0, 0]], [[247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [ 0, 0, 0], [ 0, 0, 0]], [[247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [ 0, 0, 0], [ 0, 0, 0]], [[247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [247, 90, 80], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [167, 118, 85], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [172, 39, 208], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [ 60, 106, 191], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [114, 163, 112], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [228, 201, 50], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0]]], dtype=np.uint8), ) image_decoder_decode_bmp_rgb = ImageData( np.array([ 66, 77, 54, 12, 0, 0, 0, 0, 0, 0, 54, 0, 0, 0, 40, 0, 0, 0, 32, 0, 0, 0, 32, 0, 0, 0, 1, 0, 24, 0, 0, 0, 0, 0, 0, 12, 0, 0, 196, 14, 0, 0, 196, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 0, 0, 0, 0, 0, 0, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 0, 0, 0, 0, 0, 0, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 37, 188, 77, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 230, 138, 2, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 81, 74, 159, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 27, 63, 240, 0, 0, 0, 0, 0, 0, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 11, 246, 24, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 6, 219, 177, 37, 188, 77, 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158, 29, 136, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 0, 0, 0, 0, 0, 0, 54, 66, 34, 54, 66, 34, 54, 66, 34, 54, 66, 34, 54, 66, 34, 158, 29, 136, 158, 29, 136, 158, 29, 136, 158, 29, 136, 158, 29, 136, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 0, 0, 0, 0, 0, 0, 54, 66, 34, 54, 66, 34, 54, 66, 34, 54, 66, 34, 54, 66, 34, 158, 29, 136, 158, 29, 136, 158, 29, 136, 158, 29, 136, 158, 29, 136, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 1, 152, 117, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 244, 208, 187, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 234, 108, 118, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 205, 113, 246, 0, 0, 0, 0, 0, 0, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 0, 0, 0, 0, 0, 0, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 0, 0, 0, 0, 0, 0, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 0, 0, 0, 0, 0, 0, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 0, 0, 0, 0, 0, 0, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 169, 47, 21, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 10, 45, 173, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 94, 15, 254, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 81, 126, 52, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 101, 211, 145, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 145, 86, 109, 0, 0, 0, 0, 0, 0, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 0, 0, 0, 0, 0, 0, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 0, 0, 0, 0, 0, 0, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 0, 0, 0, 0, 0, 0, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 0, 0, 0, 0, 0, 0, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 251, 81, 226, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 45, 229, 237, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 29, 227, 225, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 118, 8, 129, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 77, 164, 33, 0, 0, 0, 0, 0, 0], dtype=np.uint8), image_decoder_decode_jpeg2k_rgb.output, ) image_decoder_decode_png_rgb = ImageData( np.array([137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 0, 32, 0, 0, 0, 32, 8, 2, 0, 0, 0, 252, 24, 237, 163, 0, 0, 0, 255, 73, 68, 65, 84, 120, 156, 99, 124, 20, 248, 155, 1, 9, 112, 79, 49, 66, 230, 126, 249, 247, 1, 153, 43, 35, 101, 140, 204, 109, 225, 86, 67, 230, 46, 152, 115, 29, 153, 123, 63, 102, 51, 3, 3, 3, 19, 3, 141, 193, 168, 5, 4, 1, 139, 241, 189, 117, 200, 252, 25, 30, 119, 145, 185, 129, 143, 66, 144, 185, 102, 249, 159, 145, 185, 177, 167, 223, 35, 115, 125, 154, 93, 144, 185, 147, 25, 70, 35, 121, 112, 88, 192, 168, 228, 100, 134, 204, 79, 187, 157, 129, 204, 125, 91, 157, 140, 204, 117, 179, 64, 137, 228, 221, 115, 190, 33, 115, 27, 88, 31, 35, 115, 185, 250, 141, 25, 134, 67, 16, 13, 125, 11, 88, 228, 76, 88, 145, 249, 162, 95, 56, 145, 185, 108, 77, 149, 200, 92, 67, 255, 20, 100, 238, 223, 48, 102, 100, 110, 216, 47, 22, 20, 179, 251, 25, 24, 134, 67, 16, 13, 125, 11, 88, 182, 63, 17, 69, 230, 111, 112, 64, 41, 189, 249, 55, 86, 35, 115, 3, 24, 81, 202, 103, 179, 39, 27, 145, 185, 51, 212, 14, 33, 115, 101, 24, 24, 24, 134, 67, 16, 13, 125, 11, 88, 20, 231, 236, 70, 230, 207, 124, 138, 210, 14, 155, 227, 118, 1, 153, 123, 236, 220, 65, 100, 110, 242, 242, 135, 200, 92, 158, 175, 47, 49, 45, 24, 250, 65, 52, 244, 45, 24, 250, 0, 0, 70, 253, 59, 210, 74, 38, 46, 197, 0, 0, 0, 0, 73, 69, 78, 68, 174, 66, 96, 130], dtype=np.uint8), image_decoder_decode_bmp_rgb.output, ) image_decoder_decode_tiff_rgb = ImageData( np.array([ 73, 73, 42, 0, 8, 0, 0, 0, 10, 0, 0, 1, 4, 0, 1, 0, 0, 0, 32, 0, 0, 0, 1, 1, 4, 0, 1, 0, 0, 0, 32, 0, 0, 0, 2, 1, 3, 0, 3, 0, 0, 0, 134, 0, 0, 0, 3, 1, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 6, 1, 3, 0, 1, 0, 0, 0, 2, 0, 0, 0, 17, 1, 4, 0, 1, 0, 0, 0, 140, 0, 0, 0, 21, 1, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 22, 1, 4, 0, 1, 0, 0, 0, 32, 0, 0, 0, 23, 1, 4, 0, 1, 0, 0, 0, 0, 12, 0, 0, 28, 1, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 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95, 76, 185, 95, 76, 185, 95, 76, 185, 95, 76, 185, 95, 0, 0, 0, 0, 0, 0, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 0, 0, 0, 0, 0, 0, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 0, 0, 0, 0, 0, 0, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 0, 0, 0, 0, 0, 0, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 0, 0, 0, 0, 0, 0, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 247, 90, 80, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 167, 118, 85, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 172, 39, 208, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 60, 106, 191, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 114, 163, 112, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 228, 201, 50, 0, 0, 0, 0, 0, 0, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 0, 0, 0, 0, 0, 0, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 0, 0, 0, 0, 0, 0, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 0, 0, 0, 0, 0, 0, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 0, 0, 0, 0, 0, 0, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 24, 246, 11, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 177, 219, 6, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 77, 188, 37, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 2, 138, 230, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 159, 74, 81, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 240, 63, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.uint8), image_decoder_decode_bmp_rgb.output, ) image_decoder_decode_webp_rgb = ImageData( np.array([ 82, 73, 70, 70, 32, 2, 0, 0, 87, 69, 66, 80, 86, 80, 56, 32, 20, 2, 0, 0, 80, 13, 0, 157, 1, 42, 32, 0, 32, 0, 62, 109, 46, 146, 70, 164, 34, 161, 161, 40, 13, 80, 128, 13, 137, 108, 0, 157, 50, 132, 112, 55, 151, 254, 57, 126, 51, 115, 134, 108, 25, 128, 93, 115, 126, 83, 204, 7, 168, 15, 178, 191, 82, 95, 48, 15, 232, 29, 32, 63, 93, 191, 183, 117, 128, 253, 57, 246, 0, 255, 147, 230, 171, 236, 1, 232, 1, 230, 153, 254, 107, 245, 239, 225, 11, 246, 51, 246, 239, 218, 118, 236, 247, 43, 147, 15, 163, 180, 0, 165, 21, 251, 68, 81, 89, 121, 30, 65, 221, 213, 115, 111, 216, 229, 96, 0, 253, 210, 122, 138, 75, 92, 219, 102, 22, 86, 171, 131, 154, 9, 97, 230, 179, 178, 43, 43, 206, 57, 143, 19, 232, 129, 21, 139, 23, 214, 170, 75, 14, 52, 78, 111, 88, 54, 0, 216, 240, 73, 98, 154, 194, 36, 47, 34, 68, 148, 194, 197, 79, 81, 220, 205, 223, 88, 250, 42, 71, 218, 15, 124, 129, 12, 38, 200, 116, 251, 93, 147, 57, 36, 183, 245, 31, 100, 160, 116, 212, 180, 65, 118, 26, 4, 133, 242, 198, 50, 89, 34, 109, 244, 199, 253, 50, 134, 185, 19, 231, 104, 63, 202, 255, 252, 169, 61, 255, 112, 75, 33, 165, 235, 249, 208, 254, 59, 253, 66, 115, 104, 1, 198, 21, 242, 126, 158, 181, 152, 55, 50, 77, 29, 83, 7, 74, 139, 7, 157, 145, 131, 163, 5, 105, 181, 174, 38, 245, 83, 73, 183, 195, 243, 162, 207, 173, 142, 14, 38, 114, 149, 107, 162, 8, 74, 172, 85, 135, 196, 54, 62, 66, 159, 28, 239, 150, 13, 1, 197, 236, 121, 183, 69, 189, 23, 138, 22, 15, 245, 216, 244, 67, 220, 105, 77, 80, 101, 145, 47, 254, 82, 86, 190, 233, 60, 253, 192, 109, 30, 199, 255, 255, 11, 191, 132, 172, 157, 189, 172, 98, 91, 223, 41, 168, 79, 177, 124, 248, 219, 66, 28, 89, 184, 193, 218, 73, 123, 199, 170, 197, 85, 60, 244, 144, 65, 180, 53, 51, 76, 101, 246, 98, 255, 202, 147, 239, 114, 41, 19, 231, 202, 205, 1, 24, 55, 73, 164, 212, 62, 253, 138, 147, 181, 58, 249, 167, 19, 186, 248, 198, 120, 78, 227, 215, 3, 206, 125, 176, 241, 255, 249, 143, 31, 16, 189, 165, 240, 216, 14, 60, 28, 102, 246, 10, 178, 9, 29, 179, 207, 170, 34, 51, 136, 167, 194, 160, 17, 118, 203, 188, 35, 229, 124, 128, 245, 253, 232, 250, 94, 77, 150, 161, 46, 3, 185, 215, 69, 122, 167, 159, 211, 209, 119, 111, 211, 153, 178, 160, 153, 174, 180, 229, 61, 15, 196, 225, 48, 93, 243, 101, 64, 200, 23, 47, 211, 242, 50, 31, 133, 231, 63, 151, 123, 87, 178, 77, 165, 172, 205, 35, 249, 186, 231, 150, 205, 218, 230, 245, 10, 39, 42, 157, 116, 223, 241, 120, 201, 34, 28, 59, 113, 38, 151, 73, 39, 119, 255, 227, 105, 45, 148, 84, 0, 0, 0], dtype=np.uint8), np.array([[[230, 84, 242], [230, 84, 240], [232, 84, 238], [225, 94, 202], [207, 108, 126], [255, 199, 131], [243, 221, 74], [229, 229, 47], [232, 234, 65], [228, 231, 61], [228, 226, 43], [228, 223, 31], [232, 225, 31], [227, 221, 34], [224, 219, 43], [255, 250, 82], [255, 247, 84], [255, 248, 82], [254, 253, 76], [255, 236, 130], [121, 22, 71], [132, 5, 128], [126, 10, 122], [105, 23, 107], [ 70, 43, 86], [ 90, 125, 122], [ 49, 154, 98], [ 27, 166, 83], [ 29, 168, 80], [ 48, 155, 87], [ 0, 13, 0], [ 0, 0, 0]], [[229, 84, 244], [229, 84, 242], [229, 85, 240], [222, 95, 204], [205, 108, 130], [255, 198, 137], [244, 219, 80], [232, 227, 49], [235, 233, 61], [232, 230, 55], [228, 227, 39], [227, 224, 29], [230, 226, 31], [225, 222, 32], [223, 220, 41], [255, 251, 80], [255, 249, 82], [255, 250, 80], [252, 254, 76], [255, 237, 130], [118, 23, 71], [130, 6, 126], [126, 10, 122], [107, 22, 109], [ 70, 43, 84], [ 90, 125, 120], [ 51, 153, 98], [ 31, 165, 83], [ 32, 167, 80], [ 50, 154, 87], [ 0, 13, 0], [ 0, 0, 0]], [[224, 86, 246], [224, 86, 246], [222, 87, 244], [217, 96, 210], [202, 108, 138], [255, 196, 147], [249, 215, 90], [239, 223, 55], [240, 233, 53], [235, 231, 43], [230, 228, 33], [227, 224, 27], [227, 227, 31], [224, 223, 32], [221, 221, 37], [252, 254, 76], [252, 254, 76], [250, 254, 76], [247, 255, 76], [255, 238, 132], [115, 25, 69], [129, 7, 126], [126, 9, 126], [107, 21, 112], [ 70, 43, 82], [ 92, 125, 116], [ 56, 152, 94], [ 35, 163, 81], [ 37, 164, 80], [ 55, 151, 89], [ 0, 11, 0], [ 0, 0, 2]], [[208, 93, 250], [208, 93, 250], [208, 93, 250], [205, 101, 218], [194, 111, 148], [255, 195, 157], [255, 209, 104], [250, 215, 67], [255, 222, 63], [252, 220, 53], [249, 215, 49], [246, 211, 47], [245, 214, 54], [237, 212, 56], [231, 212, 59], [255, 249, 94], [244, 254, 94], [239, 255, 94], [239, 255, 96], [252, 241, 142], [103, 33, 59], [116, 17, 107], [116, 17, 107], [100, 27, 97], [ 69, 46, 74], [ 95, 124, 112], [ 64, 147, 98], [ 47, 156, 87], [ 48, 157, 88], [ 63, 146, 95], [ 0, 9, 0], [ 0, 0, 2]], [[183, 109, 255], [183, 109, 255], [185, 108, 255], [186, 113, 229], [189, 120, 166], [255, 195, 174], [255, 217, 133], [255, 208, 93], [255, 202, 86], [255, 195, 84], [255, 183, 86], [255, 176, 88], [255, 180, 98], [255, 183, 101], [255, 202, 115], [255, 230, 132], [235, 247, 134], [223, 254, 132], [224, 253, 130], [235, 242, 158], [ 84, 46, 41], [ 95, 35, 69], [ 97, 34, 69], [ 86, 41, 65], [ 70, 57, 64], [102, 125, 112], [ 77, 135, 104], [ 69, 143, 104], [ 72, 147, 108], [ 77, 134, 108], [ 0, 11, 3], [ 0, 0, 2]], [[ 55, 31, 165], [ 57, 30, 167], [ 62, 27, 171], [ 70, 28, 142], [ 78, 26, 78], [123, 60, 52], [140, 64, 1], [155, 66, 0], [155, 55, 0], [174, 52, 0], [199, 48, 10], [208, 48, 25], [204, 54, 38], [183, 56, 38], [164, 79, 44], [146, 109, 58], [ 93, 112, 37], [ 75, 121, 35], [ 77, 121, 33], [ 85, 115, 43], [184, 183, 147], [194, 177, 155], [195, 176, 153], [190, 178, 157], [176, 176, 163], [ 92, 107, 97], [ 83, 114, 106], [ 77, 116, 110], [ 75, 115, 111], [ 81, 110, 109], [ 0, 1, 4], [ 0, 0, 1]], [[ 20, 51, 168], [ 23, 48, 172], [ 31, 43, 176], [ 47, 40, 150], [ 78, 46, 100], [121, 61, 63], [142, 53, 8], [165, 60, 0], [163, 46, 0], [189, 45, 18], [229, 39, 48], [248, 40, 72], [233, 35, 75], [201, 39, 71], [163, 64, 72], [131, 95, 79], [ 84, 113, 74], [ 63, 124, 72], [ 63, 124, 72], [ 70, 123, 64], [158, 197, 127], [164, 195, 118], [164, 195, 116], [167, 191, 129], [170, 182, 150], [103, 108, 104], [ 98, 99, 119], [ 93, 93, 125], [ 95, 93, 130], [ 96, 96, 125], [ 0, 0, 11], [ 0, 2, 7]], [[ 0, 55, 168], [ 3, 53, 168], [ 13, 47, 172], [ 32, 43, 146], [ 58, 36, 91], [121, 63, 71], [155, 58, 22], [173, 56, 4], [170, 47, 9], [191, 38, 30], [232, 22, 63], [249, 17, 84], [255, 30, 102], [220, 32, 93], [171, 56, 90], [128, 89, 95], [ 77, 112, 89], [ 53, 125, 85], [ 51, 126, 85], [ 56, 127, 69], [147, 207, 119], [152, 208, 103], [151, 209, 101], [157, 203, 117], [166, 187, 146], [105, 105, 105], [102, 87, 122], [106, 84, 139], [113, 92, 149], [108, 95, 138], [ 1, 0, 13], [ 0, 2, 3]], [[ 7, 56, 174], [ 10, 55, 172], [ 16, 52, 168], [ 34, 48, 141], [ 61, 38, 94], [122, 60, 74], [152, 50, 20], [167, 45, 0], [171, 48, 8], [184, 32, 26], [232, 28, 78], [248, 16, 96], [254, 20, 99], [228, 28, 95], [180, 53, 93], [120, 73, 82], [ 78, 114, 88], [ 49, 129, 84], [ 47, 131, 80], [ 54, 130, 66], [145, 210, 118], [152, 210, 102], [150, 212, 100], [157, 205, 116], [169, 189, 148], [104, 102, 102], [112, 92, 133], [111, 86, 145], [108, 89, 148], [104, 93, 134], [ 2, 0, 4], [ 1, 3, 0]], [[ 15, 58, 141], [ 16, 58, 139], [ 21, 56, 137], [ 36, 51, 123], [ 59, 39, 94], [119, 58, 92], [149, 44, 57], [162, 39, 39], [165, 44, 48], [168, 36, 49], [197, 45, 71], [199, 35, 65], [210, 39, 65], [201, 54, 72], [157, 65, 73], [116, 83, 78], [ 78, 111, 91], [ 56, 122, 93], [ 54, 123, 91], [ 59, 122, 85], [152, 200, 154], [157, 199, 146], [155, 200, 146], [163, 194, 156], [175, 178, 177], [114, 93, 123], [123, 80, 135], [125, 73, 143], [125, 80, 148], [117, 83, 133], [ 6, 0, 6], [ 0, 0, 0]], [[ 28, 65, 73], [ 28, 64, 77], [ 28, 63, 83], [ 39, 56, 91], [ 56, 41, 94], [111, 56, 127], [137, 37, 125], [149, 29, 124], [141, 26, 117], [144, 50, 98], [168, 112, 76], [164, 128, 45], [163, 130, 36], [151, 124, 40], [149, 134, 75], [206, 206, 174], [191, 212, 205], [181, 214, 221], [179, 214, 225], [183, 210, 235], [113, 125, 173], [116, 122, 183], [114, 122, 187], [124, 116, 189], [142, 109, 189], [195, 134, 218], [215, 126, 210], [228, 123, 209], [223, 120, 207], [209, 129, 199], [ 20, 0, 18], [ 6, 0, 8]], [[ 37, 66, 43], [ 36, 66, 47], [ 34, 65, 55], [ 40, 59, 69], [ 54, 43, 88], [106, 56, 137], [130, 35, 156], [140, 26, 164], [133, 25, 157], [128, 56, 119], [133, 126, 55], [117, 147, 5], [118, 151, 4], [119, 152, 20], [123, 147, 55], [197, 218, 170], [191, 211, 211], [189, 208, 233], [189, 206, 239], [192, 201, 255], [117, 115, 212], [121, 110, 230], [121, 109, 234], [130, 105, 230], [148, 97, 215], [207, 125, 234], [228, 114, 212], [242, 111, 203], [244, 109, 207], [222, 118, 197], [ 26, 0, 20], [ 3, 0, 10]], [[ 42, 62, 49], [ 40, 63, 49], [ 37, 64, 51], [ 42, 60, 61], [ 55, 48, 78], [ 96, 53, 120], [118, 31, 142], [137, 31, 163], [133, 32, 160], [120, 54, 113], [129, 125, 49], [113, 143, 0], [114, 149, 7], [115, 149, 24], [117, 146, 54], [191, 217, 167], [186, 213, 213], [186, 208, 237], [189, 206, 239], [191, 202, 255], [113, 115, 215], [115, 111, 233], [118, 109, 233], [129, 105, 227], [150, 100, 213], [204, 123, 228], [230, 117, 212], [242, 111, 203], [246, 112, 207], [223, 120, 194], [ 27, 0, 18], [ 3, 0, 4]], [[ 42, 63, 45], [ 40, 64, 45], [ 37, 65, 47], [ 42, 61, 57], [ 61, 55, 80], [102, 60, 122], [124, 38, 144], [143, 38, 165], [139, 37, 162], [123, 49, 112], [149, 134, 73], [129, 142, 22], [131, 143, 27], [131, 143, 41], [132, 142, 66], [206, 215, 173], [201, 209, 213], [201, 206, 231], [204, 204, 231], [202, 202, 246], [119, 119, 199], [119, 117, 213], [120, 116, 211], [130, 112, 205], [142, 105, 190], [198, 137, 215], [212, 126, 196], [225, 122, 191], [231, 131, 199], [212, 135, 188], [ 21, 0, 14], [ 2, 0, 3]], [[ 41, 72, 34], [ 39, 72, 38], [ 37, 71, 46], [ 44, 65, 60], [ 61, 52, 82], [104, 56, 122], [127, 35, 139], [148, 34, 159], [139, 23, 151], [134, 41, 122], [157, 110, 92], [152, 124, 56], [151, 119, 50], [155, 120, 59], [156, 120, 77], [231, 193, 174], [230, 195, 202], [228, 194, 212], [228, 194, 208], [222, 196, 216], [129, 120, 157], [122, 122, 165], [122, 123, 161], [124, 123, 157], [131, 121, 154], [174, 153, 183], [187, 149, 173], [185, 140, 162], [188, 151, 170], [178, 151, 167], [ 10, 0, 10], [ 2, 0, 6]], [[ 72, 113, 67], [ 72, 112, 71], [ 70, 112, 79], [ 80, 104, 93], [ 86, 77, 103], [128, 82, 143], [152, 60, 162], [174, 58, 182], [165, 47, 173], [162, 58, 153], [125, 50, 76], [122, 58, 47], [120, 49, 36], [125, 50, 39], [128, 51, 45], [204, 127, 128], [195, 118, 126], [193, 119, 126], [193, 120, 122], [179, 127, 124], [190, 176, 169], [178, 182, 171], [178, 182, 167], [175, 184, 167], [167, 184, 165], [136, 155, 135], [137, 150, 127], [138, 151, 128], [134, 154, 129], [137, 154, 137], [ 0, 0, 0], [ 2, 0, 4]], [[ 64, 116, 73], [ 65, 115, 73], [ 67, 114, 73], [ 76, 108, 83], [ 97, 95, 107], [121, 78, 129], [146, 60, 155], [159, 51, 169], [159, 49, 168], [163, 55, 159], [135, 37, 111], [137, 38, 97], [139, 35, 90], [141, 33, 81], [143, 35, 67], [221, 110, 129], [220, 108, 112], [222, 109, 104], [222, 109, 102], [201, 121, 98], [200, 179, 129], [181, 189, 125], [181, 189, 125], [173, 193, 125], [153, 199, 125], [112, 174, 98], [103, 172, 101], [101, 174, 106], [ 96, 171, 105], [112, 168, 117], [ 0, 8, 0], [ 2, 3, 0]], [[ 60, 118, 71], [ 62, 117, 71], [ 64, 117, 69], [ 75, 109, 79], [ 95, 97, 101], [117, 81, 125], [143, 62, 153], [157, 52, 167], [159, 50, 164], [164, 53, 164], [143, 29, 129], [145, 30, 121], [146, 26, 117], [149, 25, 103], [152, 28, 81], [232, 104, 131], [233, 104, 102], [236, 104, 90], [238, 103, 90], [214, 117, 84], [203, 181, 107], [181, 194, 101], [181, 194, 101], [171, 198, 103], [147, 207, 104], [102, 182, 80], [ 87, 183, 87], [ 84, 185, 94], [ 80, 181, 92], [ 99, 177, 107], [ 0, 12, 0], [ 0, 7, 0]], [[ 62, 119, 63], [ 62, 119, 63], [ 64, 118, 65], [ 73, 110, 77], [ 93, 98, 99], [117, 81, 123], [143, 63, 149], [159, 52, 163], [163, 48, 164], [169, 51, 162], [145, 29, 125], [145, 30, 119], [146, 27, 115], [148, 26, 103], [151, 28, 81], [232, 105, 129], [236, 102, 100], [239, 103, 88], [239, 103, 90], [215, 116, 82], [203, 182, 101], [179, 196, 95], [179, 195, 97], [170, 199, 101], [148, 206, 104], [104, 182, 80], [ 91, 181, 85], [ 87, 184, 90], [ 82, 181, 88], [ 99, 177, 105], [ 0, 14, 0], [ 0, 9, 0]], [[ 97, 102, 55], [ 97, 102, 57], [ 99, 101, 59], [105, 96, 67], [117, 88, 85], [130, 78, 105], [145, 67, 125], [153, 61, 135], [155, 58, 134], [163, 58, 143], [143, 29, 129], [146, 26, 135], [144, 24, 131], [141, 25, 121], [138, 30, 105], [211, 109, 162], [206, 111, 134], [204, 114, 124], [204, 114, 124], [187, 125, 112], [189, 186, 121], [171, 197, 107], [171, 197, 109], [167, 200, 109], [155, 202, 106], [118, 175, 74], [113, 173, 69], [111, 176, 68], [106, 173, 66], [117, 171, 89], [ 0, 11, 0], [ 0, 7, 0]], [[205, 108, 89], [207, 107, 89], [210, 106, 89], [210, 105, 91], [204, 107, 99], [192, 106, 103], [180, 107, 111], [172, 107, 113], [167, 110, 111], [174, 103, 138], [164, 55, 167], [170, 43, 193], [170, 43, 191], [161, 50, 188], [142, 60, 186], [124, 73, 179], [103, 86, 165], [ 92, 96, 156], [ 89, 98, 152], [ 88, 104, 131], [123, 153, 125], [122, 158, 101], [119, 161, 97], [122, 160, 90], [129, 161, 77], [188, 209, 108], [202, 208, 85], [205, 206, 74], [204, 212, 79], [197, 205, 104], [ 6, 5, 0], [ 1, 0, 0]], [[240, 92, 81], [242, 91, 81], [245, 90, 81], [240, 92, 83], [228, 98, 85], [207, 102, 85], [182, 111, 87], [165, 116, 85], [159, 119, 83], [170, 108, 122], [164, 53, 173], [171, 39, 207], [168, 41, 205], [156, 49, 208], [131, 61, 212], [103, 77, 209], [ 73, 95, 199], [ 58, 106, 192], [ 53, 109, 188], [ 59, 113, 159], [111, 156, 143], [115, 159, 113], [112, 162, 109], [117, 161, 99], [134, 158, 82], [201, 203, 102], [222, 201, 69], [229, 198, 52], [226, 205, 57], [215, 199, 86], [ 14, 2, 0], [ 4, 0, 0]], [[242, 93, 73], [242, 92, 75], [245, 90, 79], [239, 93, 81], [226, 99, 85], [205, 104, 83], [182, 111, 83], [167, 116, 81], [164, 117, 81], [173, 107, 120], [166, 53, 169], [171, 40, 205], [168, 42, 203], [155, 50, 206], [129, 62, 212], [103, 77, 209], [ 75, 94, 197], [ 60, 105, 190], [ 57, 107, 188], [ 61, 113, 157], [111, 157, 137], [114, 161, 107], [111, 163, 105], [117, 162, 94], [134, 158, 80], [202, 202, 102], [226, 199, 67], [231, 198, 48], [228, 205, 53], [215, 200, 84], [ 12, 3, 0], [ 1, 0, 0]], [[212, 111, 58], [212, 111, 60], [213, 109, 64], [210, 110, 66], [201, 115, 68], [187, 116, 67], [172, 120, 65], [162, 121, 64], [159, 123, 64], [166, 114, 102], [154, 64, 142], [157, 52, 176], [152, 55, 176], [140, 61, 184], [118, 71, 192], [ 95, 84, 197], [ 67, 98, 197], [ 53, 107, 196], [ 50, 110, 192], [ 59, 113, 161], [115, 155, 137], [123, 157, 105], [120, 159, 103], [127, 157, 94], [145, 153, 80], [213, 196, 104], [238, 192, 73], [245, 190, 56], [241, 197, 61], [224, 193, 92], [ 14, 2, 0], [ 0, 0, 0]], [[149, 147, 36], [149, 147, 38], [151, 146, 38], [151, 145, 40], [154, 144, 40], [158, 145, 43], [153, 135, 34], [160, 142, 43], [152, 139, 39], [160, 139, 73], [127, 84, 92], [129, 81, 124], [122, 83, 128], [104, 80, 131], [ 99, 94, 155], [ 76, 98, 173], [ 58, 114, 204], [ 43, 119, 214], [ 40, 121, 210], [ 58, 119, 176], [125, 146, 144], [141, 145, 109], [140, 146, 107], [146, 145, 97], [167, 144, 86], [232, 182, 108], [255, 181, 91], [255, 168, 68], [255, 179, 80], [238, 174, 102], [ 26, 3, 0], [ 1, 0, 0]], [[106, 198, 38], [108, 198, 36], [108, 198, 36], [114, 195, 34], [130, 193, 35], [173, 212, 60], [190, 205, 59], [195, 197, 55], [190, 198, 52], [191, 199, 80], [142, 150, 88], [136, 148, 115], [120, 142, 109], [117, 148, 128], [104, 149, 152], [ 60, 122, 161], [ 39, 124, 202], [ 27, 125, 222], [ 24, 127, 220], [ 51, 121, 184], [ 97, 98, 107], [123, 92, 73], [121, 93, 71], [128, 92, 61], [142, 86, 43], [183, 98, 45], [205, 88, 22], [214, 84, 14], [206, 83, 15], [181, 89, 40], [ 23, 0, 0], [ 3, 0, 2]], [[ 46, 230, 22], [ 48, 230, 20], [ 51, 229, 16], [ 64, 223, 10], [ 80, 207, 0], [143, 231, 30], [180, 220, 31], [186, 207, 20], [186, 213, 21], [175, 214, 44], [111, 171, 42], [ 95, 170, 60], [ 92, 172, 60], [ 91, 181, 87], [ 73, 177, 124], [ 23, 137, 135], [ 18, 135, 196], [ 11, 133, 224], [ 8, 134, 226], [ 43, 124, 190], [115, 94, 111], [152, 83, 74], [147, 84, 78], [155, 82, 70], [182, 80, 60], [209, 84, 53], [219, 71, 30], [232, 73, 29], [220, 66, 20], [194, 79, 45], [ 29, 0, 0], [ 8, 3, 7]], [[ 22, 251, 21], [ 24, 250, 19], [ 27, 250, 13], [ 43, 243, 5], [ 73, 231, 0], [130, 241, 19], [167, 224, 13], [182, 213, 7], [185, 221, 13], [169, 225, 31], [ 95, 184, 20], [ 76, 182, 35], [ 75, 183, 35], [ 69, 187, 61], [ 53, 186, 109], [ 16, 155, 138], [ 7, 141, 194], [ 2, 138, 224], [ 0, 139, 226], [ 40, 125, 192], [119, 88, 110], [161, 74, 76], [158, 75, 80], [166, 72, 74], [181, 62, 54], [217, 76, 60], [231, 69, 41], [237, 67, 35], [235, 65, 31], [205, 74, 50], [ 30, 0, 0], [ 3, 0, 2]], [[ 24, 246, 19], [ 24, 246, 17], [ 28, 246, 10], [ 42, 240, 4], [ 71, 232, 0], [130, 243, 21], [164, 225, 13], [175, 213, 9], [176, 219, 15], [163, 224, 33], [ 85, 179, 11], [ 69, 176, 24], [ 79, 184, 39], [ 73, 185, 70], [ 53, 179, 114], [ 11, 145, 136], [ 6, 145, 198], [ 1, 142, 226], [ 1, 142, 224], [ 42, 128, 192], [117, 90, 117], [159, 76, 83], [159, 76, 83], [167, 72, 79], [188, 74, 74], [209, 74, 63], [229, 70, 49], [237, 66, 37], [239, 70, 36], [202, 72, 46], [ 37, 0, 0], [ 4, 0, 0]], [[ 60, 221, 53], [ 58, 222, 51], [ 58, 223, 47], [ 68, 219, 43], [ 87, 211, 37], [135, 224, 55], [166, 214, 55], [181, 210, 57], [174, 208, 57], [163, 211, 70], [106, 175, 47], [ 94, 171, 56], [ 96, 172, 67], [ 90, 169, 88], [ 76, 167, 125], [ 43, 141, 140], [ 28, 132, 175], [ 25, 131, 193], [ 25, 131, 193], [ 56, 119, 166], [113, 91, 112], [143, 81, 88], [145, 80, 88], [151, 78, 82], [164, 80, 77], [180, 81, 71], [196, 78, 63], [203, 74, 54], [206, 76, 51], [178, 79, 58], [ 31, 0, 0], [ 8, 0, 0]], [[ 0, 24, 0], [ 0, 25, 0], [ 0, 27, 0], [ 0, 26, 0], [ 0, 24, 0], [ 0, 28, 0], [ 0, 13, 0], [ 1, 11, 0], [ 0, 14, 0], [ 0, 13, 0], [ 0, 10, 0], [ 0, 7, 0], [ 0, 15, 0], [ 0, 10, 0], [ 0, 13, 7], [ 0, 4, 9], [ 0, 5, 24], [ 0, 5, 28], [ 0, 6, 28], [ 0, 1, 17], [ 4, 0, 0], [ 16, 0, 0], [ 15, 0, 0], [ 16, 0, 0], [ 20, 0, 0], [ 28, 0, 0], [ 33, 0, 0], [ 34, 0, 0], [ 36, 0, 0], [ 29, 0, 0], [ 10, 0, 0], [ 2, 0, 0]], [[ 4, 0, 1], [ 1, 1, 1], [ 0, 4, 1], [ 0, 5, 1], [ 0, 3, 0], [ 0, 8, 5], [ 0, 0, 0], [ 4, 7, 6], [ 0, 1, 0], [ 0, 0, 0], [ 6, 0, 0], [ 7, 0, 0], [ 12, 0, 6], [ 2, 0, 2], [ 2, 0, 9], [ 0, 0, 3], [ 1, 0, 2], [ 0, 0, 0], [ 0, 0, 2], [ 0, 0, 0], [ 4, 0, 0], [ 7, 0, 0], [ 6, 0, 0], [ 4, 0, 0], [ 3, 0, 0], [ 6, 2, 0], [ 5, 2, 6], [ 4, 0, 8], [ 6, 1, 2], [ 8, 0, 0], [ 8, 0, 0], [ 10, 0, 0]]], dtype=np.uint8), ) image_decoder_decode_pnm_rgb = ImageData( np.array([ 80, 54, 10, 51, 50, 32, 51, 50, 10, 50, 53, 53, 10, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 226, 81, 251, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 237, 229, 45, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 225, 227, 29, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 253, 253, 80, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 129, 8, 118, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 33, 164, 77, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 21, 47, 169, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 173, 45, 10, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 254, 15, 94, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 52, 126, 81, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 145, 211, 101, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 109, 86, 145, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 244, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 118, 108, 234, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 246, 113, 205, 0, 0, 0, 0, 0, 0, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 34, 66, 54, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 136, 29, 158, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 117, 152, 1, 187, 208, 244, 187, 208, 244, 187, 208, 244, 187, 208, 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77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [ 24, 246, 11], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [177, 219, 6], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 77, 188, 37], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [ 2, 138, 230], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [159, 74, 81], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [240, 63, 27], [ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0], [ 0, 0, 0]]], dtype=np.uint8), ) # fmt: on onnx-onnx-bca0315/onnx/backend/test/case/node/abs.py000066400000000000000000000010671511334557700224020ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Abs(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Abs", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.abs(x) expect(node, inputs=[x], outputs=[y], name="test_abs") onnx-onnx-bca0315/onnx/backend/test/case/node/acos.py000066400000000000000000000013261511334557700225600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Acos(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Acos", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arccos(x) expect(node, inputs=[x], outputs=[y], name="test_acos") onnx-onnx-bca0315/onnx/backend/test/case/node/acosh.py000066400000000000000000000014351511334557700227310ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Acosh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Acosh", inputs=["x"], outputs=["y"], ) x = np.array([10, np.e, 1]).astype(np.float32) y = np.arccosh(x) # expected output [2.99322295, 1.65745449, 0.] expect(node, inputs=[x], outputs=[y], name="test_acosh_example") x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32) y = np.arccosh(x) expect(node, inputs=[x], outputs=[y], name="test_acosh") onnx-onnx-bca0315/onnx/backend/test/case/node/adagrad.py000066400000000000000000000072431511334557700232220ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN def apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor): # Compute adjusted learning-rate. r_ = r / (1 + t * decay_factor) # Add gradient of regularization term. g_regularized = norm_coefficient * x + g # Update squared accumulated gradient. h_new = h + g_regularized * g_regularized # Compute ADAGRAD's gradient scaling factors h_sqrt = np.sqrt(h_new) + epsilon # Apply ADAGRAD update rule. x_new = x - r_ * g_regularized / h_sqrt return (x_new.astype(x.dtype), h_new.astype(h.dtype)) class Adagrad(Base): @staticmethod def export_adagrad() -> None: # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 # Create operator. node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X", "G", "H"], outputs=["X_new", "H_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.0], dtype=np.float32) g = np.array([-1.0], dtype=np.float32) h = np.array([2.0], dtype=np.float32) # Compute expected outputs of Adagrad. x_new, h_new = apply_adagrad( r, t, x, g, h, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x, g, h], outputs=[x_new, h_new], name="test_adagrad", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) @staticmethod def export_adagrad_multiple() -> None: # Define operator attributes. norm_coefficient = 0.001 epsilon = 1e-5 decay_factor = 0.1 node = onnx.helper.make_node( "Adagrad", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, epsilon=epsilon, decay_factor=decay_factor, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) h1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) h2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Adagrad. x1_new, h1_new = apply_adagrad( r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor ) x2_new, h2_new = apply_adagrad( r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, h1, h2], outputs=[x1_new, x2_new, h1_new, h2_new], name="test_adagrad_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) onnx-onnx-bca0315/onnx/backend/test/case/node/adam.py000066400000000000000000000104441511334557700225360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN def apply_adam( r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon ): # Add gradient of regularization term. g_regularized = norm_coefficient * x + g # Update momentum. v_new = alpha * v + (1 - alpha) * g_regularized # Update second-order momentum. h_new = beta * h + (1 - beta) * (g_regularized * g_regularized) # Compute element-wise square root. h_sqrt = np.sqrt(h_new) + epsilon # Adjust learning rate. r_adjusted = None if t > 0: # Consider bias correction on momentums. r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t) else: # No bias correction on momentums. r_adjusted = r # Apply Adam update rule. x_new = x - r_adjusted * (v_new / h_sqrt) # It's possible to apply regularization in the end. x_final = (1 - norm_coefficient_post) * x_new return x_final, v_new, h_new class Adam(Base): @staticmethod def export_adam() -> None: # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 epsilon = 1e-7 # Create operator. node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X", "G", "V", "H"], outputs=["X_new", "V_new", "H_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, epsilon=epsilon, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) h = np.array([0.1, 0.1], dtype=np.float32) # Compute expected outputs of Adam. x_new, v_new, h_new = apply_adam( r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x, g, v, h], outputs=[x_new, v_new, h_new], name="test_adam", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) @staticmethod def export_adam_multiple() -> None: # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 epsilon = 1e-2 node = onnx.helper.make_node( "Adam", inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) h1 = np.array([0.5], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) h2 = np.array([1.0, 10.0], dtype=np.float32) # Compute expected outputs of Adam. x1_new, v1_new, h1_new = apply_adam( r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon ) x2_new, v2_new, h2_new = apply_adam( r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon ) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2], outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new], name="test_adam_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) onnx-onnx-bca0315/onnx/backend/test/case/node/add.py000066400000000000000000000043041511334557700223620ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Add(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint64") @staticmethod def export_add_broadcast() -> None: node = onnx.helper.make_node( "Add", inputs=["x", "y"], outputs=["sum"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/affinegrid.py000066400000000000000000000137041511334557700237340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_affine_grid import ( apply_affine_transform, construct_original_grid, ) def create_affine_matrix_3d( angle1, angle2, offset_x, offset_y, offset_z, shear_x, shear_y, shear_z, scale_x, scale_y, scale_z, ): rot_x = np.stack( [ np.ones_like(angle1), np.zeros_like(angle1), np.zeros_like(angle1), np.zeros_like(angle1), np.cos(angle1), -np.sin(angle1), np.zeros_like(angle1), np.sin(angle1), np.cos(angle1), ], axis=-1, ).reshape(-1, 3, 3) rot_y = np.stack( [ np.cos(angle2), np.zeros_like(angle2), np.sin(angle2), np.zeros_like(angle2), np.ones_like(angle2), np.zeros_like(angle2), -np.sin(angle2), np.zeros_like(angle2), np.cos(angle2), ], axis=-1, ).reshape(-1, 3, 3) shear = np.stack( [ np.ones_like(shear_x), shear_x, shear_y, shear_z, np.ones_like(shear_x), shear_x, shear_y, shear_x, np.ones_like(shear_x), ], axis=-1, ).reshape(-1, 3, 3) scale = np.stack( [ scale_x, np.zeros_like(scale_x), np.zeros_like(scale_x), np.zeros_like(scale_x), scale_y, np.zeros_like(scale_x), np.zeros_like(scale_x), np.zeros_like(scale_x), scale_z, ], axis=-1, ).reshape(-1, 3, 3) translation = np.transpose(np.array([offset_x, offset_y, offset_z])).reshape( -1, 1, 3 ) rotation_matrix = rot_y @ rot_x @ shear @ scale # (N, 3, 3) rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1)) affine_matrix = np.hstack((rotation_matrix, translation)) affine_matrix = np.transpose(affine_matrix, (0, 2, 1)) return affine_matrix.astype(np.float32) def create_affine_matrix_2d( angle1, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y ): rot = np.stack( [np.cos(angle1), -np.sin(angle1), np.sin(angle1), np.cos(angle1)], axis=-1 ).reshape(-1, 2, 2) shear = np.stack( [np.ones_like(shear_x), shear_x, shear_y, np.ones_like(shear_x)], axis=-1 ).reshape(-1, 2, 2) scale = np.stack( [scale_x, np.zeros_like(scale_x), np.zeros_like(scale_x), scale_y], axis=-1 ).reshape(-1, 2, 2) translation = np.transpose(np.array([offset_x, offset_y])).reshape(-1, 1, 2) rotation_matrix = rot @ shear @ scale # (N, 3, 3) rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1)) affine_matrix = np.hstack((rotation_matrix, translation)) affine_matrix = np.transpose(affine_matrix, (0, 2, 1)) return affine_matrix.astype(np.float32) def create_theta_2d(): angle = np.array([np.pi / 4, np.pi / 3]) offset_x = np.array([5.0, 2.5]) offset_y = np.array([-3.3, 1.1]) shear_x = np.array([-0.5, 0.5]) shear_y = np.array([0.3, -0.3]) scale_x = np.array([2.2, 1.1]) scale_y = np.array([3.1, 0.9]) return create_affine_matrix_2d( angle, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y ) def create_theta_3d(): angle1 = np.array([np.pi / 4, np.pi / 3]) angle2 = np.array([np.pi / 6, np.pi / 2]) offset_x = np.array([5.0, 2.5]) offset_y = np.array([-3.3, 1.1]) offset_z = np.array([-1.1, 2.2]) shear_x = np.array([-0.5, 0.5]) shear_y = np.array([0.3, -0.3]) shear_z = np.array([0.7, -0.2]) scale_x = np.array([2.2, 1.1]) scale_y = np.array([3.1, 0.9]) scale_z = np.array([0.5, 1.5]) return create_affine_matrix_3d( angle1, angle2, offset_x, offset_y, offset_z, shear_x, shear_y, shear_z, scale_x, scale_y, scale_z, ) class AffineGrid(Base): @staticmethod def export_2d_no_reference_evaluator() -> None: theta_2d = create_theta_2d() N, C, H, W = len(theta_2d), 3, 5, 6 data_size = (H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_2d, original_grid) test_name = "test_affine_grid_2d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_2d, np.array([N, C, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) @staticmethod def export_3d_no_reference_evaluator() -> None: theta_3d = create_theta_3d() N, C, D, H, W = len(theta_3d), 3, 4, 5, 6 data_size = (D, H, W) for align_corners in (0, 1): node = onnx.helper.make_node( "AffineGrid", inputs=["theta", "size"], outputs=["grid"], align_corners=align_corners, ) original_grid = construct_original_grid(data_size, align_corners) grid = apply_affine_transform(theta_3d, original_grid) test_name = "test_affine_grid_3d" if align_corners == 1: test_name += "_align_corners" expect( node, inputs=[theta_3d, np.array([N, C, D, H, W], dtype=np.int64)], outputs=[grid], name=test_name, ) onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/000077500000000000000000000000001511334557700234025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/__init__.py000066400000000000000000000000001511334557700255010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/array_feature_extractor.py000066400000000000000000000014721511334557700307040ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ArrayFeatureExtractor(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "ArrayFeatureExtractor", inputs=["x", "y"], outputs=["z"], domain="ai.onnx.ml", ) x = np.arange(12).reshape((3, 4)).astype(np.float32) y = np.array([0, 1], dtype=np.int64) z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T expect( node, inputs=[x, y], outputs=[z], name="test_ai_onnx_ml_array_feature_extractor", ) onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/binarizer.py000066400000000000000000000014151511334557700257420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.aionnxml.op_binarizer import compute_binarizer class Binarizer(Base): @staticmethod def export() -> None: threshold = 1.0 node = onnx.helper.make_node( "Binarizer", inputs=["X"], outputs=["Y"], threshold=threshold, domain="ai.onnx.ml", ) x = np.random.randn(3, 4, 5).astype(np.float32) y = compute_binarizer(x, threshold)[0] expect(node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_binarizer") onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/label_encoder.py000066400000000000000000000057451511334557700265450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import make_tensor class LabelEncoder(Base): @staticmethod def export_string_int_label_encoder() -> None: node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], default_int64=42, ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=["a", "b", "c"], values_int64s=[0, 1, 2], ) x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, -1, 2, -1]).astype(np.int64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_string_int_no_default", ) @staticmethod def export_tensor_based_label_encoder() -> None: tensor_keys = make_tensor( "keys_tensor", onnx.TensorProto.STRING, (3,), ["a", "b", "c"] ) repeated_string_keys = ["a", "b", "c"] x = np.array(["a", "b", "d", "c", "g"]).astype(object) y = np.array([0, 1, 42, 2, 42]).astype(np.int16) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_tensor=tensor_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_mapping", ) node = onnx.helper.make_node( "LabelEncoder", inputs=["X"], outputs=["Y"], domain="ai.onnx.ml", keys_strings=repeated_string_keys, values_tensor=make_tensor( "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2] ), default_tensor=make_tensor( "default_tensor", onnx.TensorProto.INT16, (1,), [42] ), ) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_label_encoder_tensor_value_only_mapping", ) onnx-onnx-bca0315/onnx/backend/test/case/node/ai_onnx_ml/tree_ensemble.py000066400000000000000000000075031511334557700265720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import make_tensor class TreeEnsemble(Base): @staticmethod def export_tree_ensemble_single_tree() -> None: node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=2, membership_values=None, nodes_missing_value_tracks_true=None, nodes_hitrates=None, aggregate_function=1, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 0, 0], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.DOUBLE, (3,), np.array([3.14, 1.2, 4.2], dtype=np.float64), ), nodes_truenodeids=[1, 0, 1], nodes_trueleafs=[0, 1, 1], nodes_falsenodeids=[2, 2, 3], nodes_falseleafs=[0, 1, 1], leaf_targetids=[0, 1, 0, 1], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.DOUBLE, (4,), np.array([5.23, 12.12, -12.23, 7.21], dtype=np.float64), ), ) x = np.array([1.2, 3.4, -0.12, 1.66, 4.14, 1.77], np.float64).reshape(3, 2) y = np.array([[5.23, 0], [5.23, 0], [0, 12.12]], dtype=np.float64) expect( node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_tree_ensemble_single_tree", ) @staticmethod def export_tree_ensemble_set_membership() -> None: node = onnx.helper.make_node( "TreeEnsemble", ["X"], ["Y"], domain="ai.onnx.ml", n_targets=4, aggregate_function=1, membership_values=make_tensor( "membership_values", onnx.TensorProto.FLOAT, (8,), [1.2, 3.7, 8, 9, np.nan, 12, 7, np.nan], ), nodes_missing_value_tracks_true=None, nodes_hitrates=None, post_transform=0, tree_roots=[0], nodes_modes=make_tensor( "nodes_modes", onnx.TensorProto.UINT8, (3,), np.array([0, 6, 6], dtype=np.uint8), ), nodes_featureids=[0, 0, 0], nodes_splits=make_tensor( "nodes_splits", onnx.TensorProto.FLOAT, (3,), np.array([11, 232344.0, np.nan], dtype=np.float32), ), nodes_trueleafs=[0, 1, 1], nodes_truenodeids=[1, 0, 1], nodes_falseleafs=[1, 0, 1], nodes_falsenodeids=[2, 2, 3], leaf_targetids=[0, 1, 2, 3], leaf_weights=make_tensor( "leaf_weights", onnx.TensorProto.FLOAT, (4,), [1, 10, 1000, 100] ), ) x = np.array([1.2, 3.4, -0.12, np.nan, 12, 7], np.float32).reshape(-1, 1) expected = np.array( [ [1, 0, 0, 0], [0, 0, 0, 100], [0, 0, 0, 100], [0, 0, 1000, 0], [0, 0, 1000, 0], [0, 10, 0, 0], ], dtype=np.float32, ) expect( node, inputs=[x], outputs=[expected], name="test_ai_onnx_ml_tree_ensemble_set_membership", ) onnx-onnx-bca0315/onnx/backend/test/case/node/and.py000066400000000000000000000047101511334557700223750ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class And(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and4d") @staticmethod def export_and_broadcast() -> None: node = onnx.helper.make_node( "And", inputs=["x", "y"], outputs=["and"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d") onnx-onnx-bca0315/onnx/backend/test/case/node/argmax.py000066400000000000000000000202501511334557700231070ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def argmax_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray: result = np.argmax(data, axis=axis) if keepdims == 1: result = np.expand_dims(result, axis) return result.astype(np.int64) def argmax_use_numpy_select_last_index( data: np.ndarray, axis: int = 0, keepdims: int = True ) -> np.ndarray: data = np.flip(data, axis) result = np.argmax(data, axis=axis) result = data.shape[axis] - result - 1 if keepdims: result = np.expand_dims(result, axis) return result.astype(np.int64) class ArgMax(Base): @staticmethod def export_no_keepdims() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [0, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random" ) @staticmethod def export_keepdims() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random" ) @staticmethod def export_default_axes_keepdims() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims ) # result: [[1, 1]] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random", ) @staticmethod def export_negative_axis_keepdims() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # result: [[0], [1]] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random", ) @staticmethod def export_no_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [1, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random_select_last_index", ) @staticmethod def export_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random_select_last_index", ) @staticmethod def export_default_axes_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[1, 1]] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmax_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_default_axis_random_select_last_index", ) @staticmethod def export_negative_axis_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [1]] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmax_negative_axis_keepdims_random_select_last_index", ) onnx-onnx-bca0315/onnx/backend/test/case/node/argmin.py000066400000000000000000000203721511334557700231120ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def argmin_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray: result = np.argmin(data, axis=axis) if keepdims == 1: result = np.expand_dims(result, axis) return result.astype(np.int64) def argmin_use_numpy_select_last_index( data: np.ndarray, axis: int = 0, keepdims: int = True ) -> np.ndarray: data = np.flip(data, axis) result = np.argmin(data, axis=axis) result = data.shape[axis] - result - 1 if keepdims: result = np.expand_dims(result, axis) return result.astype(np.int64) class ArgMin(Base): @staticmethod def export_no_keepdims() -> None: data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1, 0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random" ) @staticmethod def export_keepdims() -> None: data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example" ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random" ) @staticmethod def export_default_axes_keepdims() -> None: data = np.array([[2, 1], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims ) # The content of result is : [[0], [0]] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random", ) @staticmethod def export_negative_axis_keepdims() -> None: data = np.array([[2, 1], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims ) # The content of result is : [[1], [0]] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random", ) @staticmethod def export_no_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 0 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1, 0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random_select_last_index", ) @staticmethod def export_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = 1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 1, 4] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random_select_last_index", ) @staticmethod def export_default_axes_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims, select_last_index=True, ) # result: [[0, 0]] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [1, 3, 4] result = argmin_use_numpy_select_last_index(data, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_default_axis_random_select_last_index", ) @staticmethod def export_negative_axis_keepdims_select_last_index() -> None: data = np.array([[2, 2], [3, 10]], dtype=np.float32) axis = -1 keepdims = 1 node = onnx.helper.make_node( "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims, select_last_index=True, ) # result: [[1], [0]] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_example_select_last_index", ) data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32) # result's shape: [2, 3, 1] result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims) expect( node, inputs=[data], outputs=[result], name="test_argmin_negative_axis_keepdims_random_select_last_index", ) onnx-onnx-bca0315/onnx/backend/test/case/node/asin.py000066400000000000000000000013261511334557700225650ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Asin(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Asin", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin_example") x = np.random.rand(3, 4, 5).astype(np.float32) y = np.arcsin(x) expect(node, inputs=[x], outputs=[y], name="test_asin") onnx-onnx-bca0315/onnx/backend/test/case/node/asinh.py000066400000000000000000000014141511334557700227330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Asinh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Asinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arcsinh(x) # expected output [-0.88137358, 0., 0.88137358] expect(node, inputs=[x], outputs=[y], name="test_asinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arcsinh(x) expect(node, inputs=[x], outputs=[y], name="test_asinh") onnx-onnx-bca0315/onnx/backend/test/case/node/atan.py000066400000000000000000000013231511334557700225530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Atan(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Atan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.arctan(x) expect(node, inputs=[x], outputs=[y], name="test_atan") onnx-onnx-bca0315/onnx/backend/test/case/node/atanh.py000066400000000000000000000014361511334557700227300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Atanh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Atanh", inputs=["x"], outputs=["y"], ) x = np.array([-0.5, 0, 0.5]).astype(np.float32) y = np.arctanh(x) # expected output [-0.54930615, 0., 0.54930615] expect(node, inputs=[x], outputs=[y], name="test_atanh_example") x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32) y = np.arctanh(x) expect(node, inputs=[x], outputs=[y], name="test_atanh") onnx-onnx-bca0315/onnx/backend/test/case/node/attention.py000066400000000000000000001751211511334557700236450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_attention import _compute_attention class Attention(Base): @staticmethod def export_attention() -> None: node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_fp16() -> None: node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa() -> None: node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes() -> None: node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"]) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_scaled() -> None: scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_scaled() -> None: scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_scaled() -> None: scale = 1e-2 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, scale=scale) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_3d_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_3d_mask_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_4d_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_4d_mask_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], is_causal=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_4d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_mask_bool() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_attn_mask_bool_4d() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6).astype(bool) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_attn_mask_bool_4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_attn_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_attn_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_4d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_with_past_and_present() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_with_past_and_present_fp16() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 9, 4, 8).astype(np.float16) K = np.random.rand(2, 3, 6, 8).astype(np.float16) V = np.random.rand(2, 3, 6, 8).astype(np.float16) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float16) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_gqa_with_past_and_present_fp16", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_with_past_and_present() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_with_past_and_present_mask3D() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_with_past_and_present_mask4D() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_4d_diff_heads_with_past_and_present_mask4d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_softcap() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_gqa_softcap() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 9, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_diff_head_sizes_softcap() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=2.0, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=2.0, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_4d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_qk_matmul() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y", "", "", "qk_matmul_output"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention(Q, K, V) expect( node, inputs=[Q, K, V], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_qk_matmul_bias() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=1, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_qk_matmul_softcap() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], softcap=2.0, qk_matmul_output_mode=2, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_qk_matmul_softmax() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y", "", "", "qk_matmul_output"], qk_matmul_output_mode=3, ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y, qk_matmul_output], name="test_attention_4d_with_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul_bias() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul_bias_3d_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul_bias_4d_mask() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul_bias_3d_mask_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul_bias_4d_mask_causal() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], qk_matmul_output_mode=1, is_causal=1, ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, qk_matmul_output_mode=1, is_causal=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_with_past_and_present_qk_matmul() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], ) past_sequence_length = 12 Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 8).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_4d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa() -> None: q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_scaled() -> None: scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa_scaled() -> None: scale = 1e-2 q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes_scaled() -> None: scale = 1e-2 q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, scale=scale, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_scaled", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_causal() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa_causal() -> None: q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes_causal() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, is_causal=1, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_causal", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_attn_mask() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa_attn_mask() -> None: q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_gqa_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes_attn_mask() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask], outputs=[Y], name="test_attention_3d_diff_heads_sizes_attn_mask", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_softcap() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa_softcap() -> None: q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_gqa_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes_softcap() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) Y, _, _, _ = _compute_attention( Q, K, V, softcap=3.0, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_diff_heads_sizes_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_with_past_and_present() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_gqa_with_past_and_present() -> None: q_num_heads, kv_num_heads = 9, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 72).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_gqa_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_diff_head_sizes_with_past_and_present() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 30).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) Y, present_key, present_value, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value], name="test_attention_3d_diff_heads_with_past_and_present", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_with_past_and_present_qk_matmul() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_with_past_and_present_qk_matmul_bias() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=1, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_bias", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_with_past_and_present_qk_matmul_softcap() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, softcap=2.0, qk_matmul_output_mode=2, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softcap", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_with_past_and_present_qk_matmul_softmax() -> None: q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"], outputs=["Y", "present_key", "present_value", "qk_matmul_output"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) past_sequence_length = 12 Q = np.random.rand(2, 4, 24).astype(np.float32) K = np.random.rand(2, 6, 24).astype(np.float32) V = np.random.rand(2, 6, 24).astype(np.float32) attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) Y, present_key, present_value, qk_matmul_output = _compute_attention( Q, K, V, attn_mask=attn_mask, past_key=past_key, past_value=past_value, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, qk_matmul_output_mode=3, ) expect( node, inputs=[Q, K, V, attn_mask, past_key, past_value], outputs=[Y, present_key, present_value, qk_matmul_output], name="test_attention_3d_with_past_and_present_qk_matmul_softmax", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_3d_transpose_verification() -> None: """Test case to verify correct 3D to 4D transpose behavior. This test verifies that 3D inputs are correctly reshaped and transposed according to the ONNX specification: [batch_size, seq_length, hidden_size] -> [batch_size, seq_length, num_heads, head_size] -> [batch_size, num_heads, seq_length, head_size] """ q_num_heads, kv_num_heads = 3, 3 node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V"], outputs=["Y"], q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) # Test inputs that will clearly demonstrate the transpose behavior batch_size = 1 q_seq_length = 2 kv_seq_length = 2 head_size = 4 q_hidden_size = q_num_heads * head_size # 3 * 4 = 12 kv_hidden_size = kv_num_heads * head_size # 3 * 4 = 12 # Create structured inputs to verify correct transpose behavior # Q has a pattern where each position in hidden dimension has a specific value Q = np.zeros((batch_size, q_seq_length, q_hidden_size), dtype=np.float32) # Fill Q with pattern: head0=[1,1,1,1], head1=[2,2,2,2], head2=[3,3,3,3] for head in range(q_num_heads): start_idx = head * head_size end_idx = start_idx + head_size Q[0, :, start_idx:end_idx] = float(head + 1) K = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 V = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1 Y, _, _, _ = _compute_attention( Q, K, V, q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, ) expect( node, inputs=[Q, K, V], outputs=[Y], name="test_attention_3d_transpose_verification", opset_imports=[onnx.helper.make_opsetid("", 23)], ) @staticmethod def export_attention_4d_diff_heads_mask4d_padded_kv() -> None: node = onnx.helper.make_node( "Attention", inputs=["Q", "K", "V", "attn_mask", "", "", "nonpad_kv_seqlen"], outputs=["Y"], ) Q = np.random.rand(2, 3, 4, 8).astype(np.float32) K = np.random.rand(2, 3, 6, 8).astype(np.float32) V = np.random.rand(2, 3, 6, 10).astype(np.float32) attn_mask = np.random.rand(2, 3, 4, 4).astype(np.float32) nonpad_kv_seqlen = np.array([3, 4], dtype=np.int64) Y, _, _, _ = _compute_attention( Q, K, V, attn_mask=attn_mask, nonpad_kv_seqlen=nonpad_kv_seqlen, ) expect( node, inputs=[Q, K, V, attn_mask, nonpad_kv_seqlen], outputs=[Y], name="test_attention_4d_diff_heads_mask4d_padded_kv", opset_imports=[onnx.helper.make_opsetid("", 24)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/averagepool.py000066400000000000000000000512651511334557700241460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_pool_common import ( get_output_shape_auto_pad, get_output_shape_explicit_padding, get_pad_shape, pool, ) class AveragePool(Base): @staticmethod def export_averagepool_2d_precomputed_pads() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [7, 7.5, 8, 8.5, 9], [9.5, 10, 10.5, 11, 11.5], [12, 12.5, 13, 13.5, 14], [14.5, 15, 15.5, 16, 16.5], [17, 17.5, 18, 18.5, 19], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads" ) @staticmethod def export_averagepool_2d_precomputed_pads_count_include_pad() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [2.5200, 3.6000, 4.8000, 4.0800, 3.2400], [4.5600, 6.4000, 8.4000, 7.0400, 5.5200], [7.2000, 10.0000, 13.0000, 10.8000, 8.4000], [6.9600, 9.6000, 12.4000, 10.2400, 7.9200], [6.1200, 8.4000, 10.8000, 8.8800, 6.8400], ] ] ] ).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads_count_include_pad", ) @staticmethod def export_averagepool_2d_precomputed_strides() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_strides", ) @staticmethod def export_averagepool_2d_precomputed_same_upper() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype( np.float32 ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_same_upper", ) @staticmethod def export_averagepool_1d_default() -> None: """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default") @staticmethod def export_averagepool_2d_default() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default") @staticmethod def export_averagepool_3d_default() -> None: """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG") expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default") @staticmethod def export_averagepool_2d_same_upper() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper") @staticmethod def export_averagepool_2d_same_lower() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = (pad_top, pad_left, pad_bottom, pad_right) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower") @staticmethod def export_averagepool_2d_pads() -> None: """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads") @staticmethod def export_averagepool_2d_pads_count_include_pad() -> None: """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], count_include_pad=1, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) dilations = (1, 1) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = 2 pad_top = 2 pad_right = 2 pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=pads, count_include_pad=1, ) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads_count_include_pad", ) @staticmethod def export_averagepool_2d_strides() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( None, x_shape[2:], kernel_shape, strides, ceil_mode=False ) padded = x y = pool( padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads_required=pads, pads=None, ) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides") @staticmethod def export_averagepool_2d_ceil() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil") @staticmethod def export_averagepool_2d_ceil_last_window_starts_on_pad() -> None: """input_shape: [1, 3, 2, 2] output_shape: [1, 3, 1, 1] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[3, 3], pads=[1, 1, 1, 1], ceil_mode=True, count_include_pad=1, ) x = np.array( [ [ [[0.8580, 0.0786], [0.2692, 0.1537]], [[0.8816, 0.4353], [0.5772, 0.6623]], [[0.9067, 0.9483], [0.5970, 0.7630]], ] ] ).astype(np.float32) y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil_last_window_starts_on_pad", ) @staticmethod def export_averagepool_2d_dilations() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4] x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations") @staticmethod def export_averagepool_3d_dilations() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ceil_mode=True, ) # input shape: [1, 1, 4, 4, 4] x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small" ) @staticmethod def export_averagepool_3d_dilations_large() -> None: x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) count_include_pad = 0 for count_include_pad in (0, 1): for ceil_mode in (True, False): node = onnx.helper.make_node( "AveragePool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, count_include_pad=count_include_pad, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, extra_pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations=dilations, ceil_mode=ceil_mode, ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[3]), (extra_pads[1], extra_pads[4]), (extra_pads[2], extra_pads[5]), ), mode="constant", constant_values=0 if count_include_pad == 1 else np.nan, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "AVG", pads_required=extra_pads, pads=None, dilations=dilations, count_include_pad=count_include_pad, ) test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}" expect(node, inputs=[x], outputs=[y], name=test_name) onnx-onnx-bca0315/onnx/backend/test/case/node/batchnorm.py000066400000000000000000000111741511334557700236120ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def _batchnorm_test_mode(x, s, bias, mean, var, epsilon=1e-5): dims_x = len(x.shape) dim_ones = (1,) * (dims_x - 2) s = s.reshape(-1, *dim_ones) bias = bias.reshape(-1, *dim_ones) mean = mean.reshape(-1, *dim_ones) var = var.reshape(-1, *dim_ones) return s * (x - mean) / np.sqrt(var + epsilon) + bias def _batchnorm_training_mode(x, s, bias, mean, var, momentum=0.9, epsilon=1e-5): axis = tuple(np.delete(np.arange(len(x.shape)), 1)) saved_mean = x.mean(axis=axis) saved_var = x.var(axis=axis) output_mean = mean * momentum + saved_mean * (1 - momentum) output_var = var * momentum + saved_var * (1 - momentum) y = _batchnorm_test_mode(x, s, bias, saved_mean, saved_var, epsilon=epsilon) return y.astype(np.float32), output_mean, output_var class BatchNormalization(Base): @staticmethod def export() -> None: # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_example", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) epsilon = 1e-2 y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y], name="test_batchnorm_epsilon", ) @staticmethod def export_train() -> None: # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) # using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'" # working around by using np.byte(1).astype(bool) training_mode = 1 y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_example_training_mode", ) # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) mean = np.random.randn(3).astype(np.float32) var = np.random.rand(3).astype(np.float32) training_mode = 1 momentum = 0.9 epsilon = 1e-2 y, output_mean, output_var = _batchnorm_training_mode( x, s, bias, mean, var, momentum, epsilon ) node = onnx.helper.make_node( "BatchNormalization", inputs=["x", "s", "bias", "mean", "var"], outputs=["y", "output_mean", "output_var"], epsilon=epsilon, training_mode=training_mode, ) # output size: (2, 3, 4, 5) expect( node, inputs=[x, s, bias, mean, var], outputs=[y, output_mean, output_var], name="test_batchnorm_epsilon_training_mode", ) onnx-onnx-bca0315/onnx/backend/test/case/node/bernoulli.py000077500000000000000000000035161511334557700236340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def bernoulli_reference_implementation(x, dtype): # binomial n = 1 equal bernoulli # This example and test-case is for informational purpose. The generator operator is # non-deterministic and may not produce the same values in different implementations # even if a seed is specified. return np.random.binomial(1, p=x).astype(dtype) class Bernoulli(Base): @staticmethod def export_bernoulli_without_dtype() -> None: node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], ) x = np.random.uniform(0.0, 1.0, 10).astype(float) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli") @staticmethod def export_bernoulli_with_dtype() -> None: node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, float) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double") @staticmethod def export_bernoulli_with_seed() -> None: seed = float(0) node = onnx.helper.make_node( "Bernoulli", inputs=["x"], outputs=["y"], seed=seed, ) x = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y = bernoulli_reference_implementation(x, np.float32) expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed") onnx-onnx-bca0315/onnx/backend/test/case/node/bitshift.py000066400000000000000000000067751511334557700234640ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class BitShift(Base): @staticmethod def export_right_unit8() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8") @staticmethod def export_right_unit16() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16") @staticmethod def export_right_unit32() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32") @staticmethod def export_right_unit64() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x >> y # expected output [8, 1, 0] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64") @staticmethod def export_left_unit8() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint8) y = np.array([1, 2, 3]).astype(np.uint8) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8") @staticmethod def export_left_unit16() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint16) y = np.array([1, 2, 3]).astype(np.uint16) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16") @staticmethod def export_left_unit32() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint32) y = np.array([1, 2, 3]).astype(np.uint32) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32") @staticmethod def export_left_unit64() -> None: node = onnx.helper.make_node( "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT" ) x = np.array([16, 4, 1]).astype(np.uint64) y = np.array([1, 2, 3]).astype(np.uint64) z = x << y # expected output [32, 16, 8] expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64") onnx-onnx-bca0315/onnx/backend/test/case/node/bitwiseand.py000066400000000000000000000032111511334557700237570ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.numpy_helper import create_random_int class BitwiseAnd(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d") @staticmethod def export_bitwiseand_broadcast() -> None: node = onnx.helper.make_node( "BitwiseAnd", inputs=["x", "y"], outputs=["bitwiseand"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_and(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_and(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d") onnx-onnx-bca0315/onnx/backend/test/case/node/bitwisenot.py000066400000000000000000000017551511334557700240300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.numpy_helper import create_random_int class BitwiseNot(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "BitwiseNot", inputs=["x"], outputs=["bitwise_not"], ) # 2d x = create_random_int((3, 4), np.int32) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d") # 3d x = create_random_int((3, 4, 5), np.uint16) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d") # 4d x = create_random_int((3, 4, 5, 6), np.uint8) y = np.bitwise_not(x) expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d") onnx-onnx-bca0315/onnx/backend/test/case/node/bitwiseor.py000066400000000000000000000031501511334557700236370ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.numpy_helper import create_random_int class BitwiseOr(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d") # 4d x = create_random_int((3, 4, 5, 6), np.int8) y = create_random_int((3, 4, 5, 6), np.int8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d") @staticmethod def export_bitwiseor_broadcast() -> None: node = onnx.helper.make_node( "BitwiseOr", inputs=["x", "y"], outputs=["bitwiseor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d") # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d") onnx-onnx-bca0315/onnx/backend/test/case/node/bitwisexor.py000066400000000000000000000032101511334557700240240ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.numpy_helper import create_random_int class BitwiseXor(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 2d x = create_random_int((3, 4), np.int32) y = create_random_int((3, 4), np.int32) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d") # 3d x = create_random_int((3, 4, 5), np.int16) y = create_random_int((3, 4, 5), np.int16) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d") @staticmethod def export_bitwiseor_broadcast() -> None: node = onnx.helper.make_node( "BitwiseXor", inputs=["x", "y"], outputs=["bitwisexor"], ) # 3d vs 1d x = create_random_int((3, 4, 5), np.uint64) y = create_random_int((5,), np.uint64) z = np.bitwise_xor(x, y) expect( node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d" ) # 4d vs 3d x = create_random_int((3, 4, 5, 6), np.uint8) y = create_random_int((4, 5, 6), np.uint8) z = np.bitwise_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d") onnx-onnx-bca0315/onnx/backend/test/case/node/blackmanwindow.py000066400000000000000000000030031511334557700246250ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class BlackmanWindow(Base): @staticmethod def export() -> None: # Test periodic window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) y += a2 * np.cos(4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow", ) # Test symmetric window node = onnx.helper.make_node( "BlackmanWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.42 a1 = -0.5 a2 = 0.08 y = a0 y += a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) y += a2 * np.cos( 4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_blackmanwindow_symmetric", ) onnx-onnx-bca0315/onnx/backend/test/case/node/cast.py000066400000000000000000000324051511334557700225670ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import itertools import numpy as np import onnx from onnx import TensorProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import ( make_tensor, tensor_dtype_to_np_dtype, ) from onnx.numpy_helper import to_float8e8m0 F8_TYPES = frozenset({"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"}) FOUR_BIT_TYPES = frozenset({"UINT4", "INT4", "FLOAT4E2M1"}) TWO_BIT_TYPES = frozenset({"UINT2", "INT2"}) class Cast(Base): @staticmethod def export() -> None: test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in F8_TYPES or to_type in F8_TYPES: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, ) expect( node, inputs=[input], outputs=[output], name="test_cast_" + from_type + "_to_" + to_type, ) @staticmethod def export_saturate_false() -> None: test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=to_dtype, saturate=0, ) expect( node, inputs=[input], outputs=[output], name="test_cast_no_saturate_" + from_type + "_to_" + to_type, ) @staticmethod def export_e8m0() -> None: np_fp32 = np.array( [ "0.0", "0.124", "0.25", "0.5", "1.1", "2.0", "4.0", "8.0", ], dtype=np.float32, ) test_cases = [ ("FLOAT", "FLOAT8E8M0"), ("FLOAT16", "FLOAT8E8M0"), ("FLOAT8E8M0", "FLOAT"), ("FLOAT8E8M0", "FLOAT16"), ] for from_type, to_type in test_cases: if from_type == "FLOAT": input_np = np_fp32 output_np = to_float8e8m0(np_fp32) elif from_type == "FLOAT16": input_np = np_fp32.astype(np.float16) output_np = to_float8e8m0(input_np) elif from_type == "FLOAT8E8M0": input_np = to_float8e8m0(np_fp32) if to_type == "FLOAT": output_np = input_np.astype(np.float32) elif to_type == "FLOAT16": output_np = input_np.astype(np.float16) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) else: raise ValueError( f"Conversion from {from_type} to {to_type} is not tested." ) input = make_tensor( "input", getattr(TensorProto, from_type), [2, 4], input_np, raw=True, ) output = make_tensor( "output", getattr(TensorProto, to_type), [2, 4], output_np, raw=True, ) if to_type == "FLOAT8E8M0": node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), saturate=1, round_mode="up", ) else: node = onnx.helper.make_node( "Cast", inputs=["input"], outputs=["output"], to=getattr(TensorProto, to_type), ) expect( node, inputs=[input], outputs=[output], name="test_cast_e8m0_" + from_type + "_to_" + to_type, ) onnx-onnx-bca0315/onnx/backend/test/case/node/castlike.py000066400000000000000000000262021511334557700234320ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import itertools import numpy as np import onnx from onnx import TensorProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import make_tensor, tensor_dtype_to_np_dtype F8_TYPES = frozenset({"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"}) FOUR_BIT_TYPES = frozenset({"UINT4", "INT4", "FLOAT4E2M1"}) TWO_BIT_TYPES = frozenset({"UINT2", "INT2"}) class CastLike(Base): @staticmethod def export() -> None: test_cases = [ ("FLOAT", "FLOAT16"), ("FLOAT", "DOUBLE"), ("FLOAT16", "FLOAT"), ("FLOAT16", "DOUBLE"), ("DOUBLE", "FLOAT"), ("DOUBLE", "FLOAT16"), ("FLOAT", "BFLOAT16"), ("BFLOAT16", "FLOAT"), ("FLOAT", "FLOAT8E4M3FN"), ("FLOAT16", "FLOAT8E4M3FN"), ("FLOAT", "FLOAT8E4M3FNUZ"), ("FLOAT16", "FLOAT8E4M3FNUZ"), ("FLOAT8E4M3FN", "FLOAT"), ("FLOAT8E4M3FN", "FLOAT16"), ("FLOAT8E4M3FNUZ", "FLOAT"), ("FLOAT8E4M3FNUZ", "FLOAT16"), ("FLOAT", "FLOAT8E5M2"), ("FLOAT16", "FLOAT8E5M2"), ("FLOAT", "FLOAT8E5M2FNUZ"), ("FLOAT16", "FLOAT8E5M2FNUZ"), ("FLOAT8E5M2", "FLOAT"), ("FLOAT8E5M2", "FLOAT16"), ("FLOAT8E5M2FNUZ", "FLOAT"), ("FLOAT8E5M2FNUZ", "FLOAT16"), ("FLOAT", "UINT4"), ("FLOAT16", "UINT4"), ("FLOAT", "INT4"), ("FLOAT16", "INT4"), ("UINT4", "FLOAT"), ("UINT4", "FLOAT16"), ("UINT4", "UINT8"), ("INT4", "FLOAT"), ("INT4", "FLOAT16"), ("INT4", "INT8"), ("FLOAT4E2M1", "FLOAT"), ("FLOAT4E2M1", "FLOAT16"), ("FLOAT", "FLOAT4E2M1"), ("FLOAT16", "FLOAT4E2M1"), ("FLOAT", "UINT2"), ("FLOAT16", "UINT2"), ("FLOAT", "INT2"), ("FLOAT16", "INT2"), ("UINT2", "FLOAT"), ("UINT2", "FLOAT16"), ("UINT2", "UINT8"), ("INT2", "FLOAT"), ("INT2", "FLOAT16"), ("INT2", "INT8"), ] f8_types = {"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"} for from_type, to_type in test_cases: if from_type == to_type: # Skip cases where from_type and to_type are the same continue from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) if from_type == "BFLOAT16" or to_type == "BFLOAT16": np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ) input_shape = (3, 4) elif from_type in f8_types or to_type in f8_types: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input_shape = (3, 5) elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"): np_fp32 = np.arange(-9, 16).astype(np.float32) input_shape = (5, 5) elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"): np_fp32 = np.arange(-3, 4).astype(np.float32) input_shape = (7, 1) elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1": np_fp32 = np.array( [ "0.48", "0.25", "1.05", "-3.5", "-8", "9", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-4", "0.01", "-0.0", ], dtype=np.float32, ) input_shape = (3, 5) else: np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.816468", "0.21087195", "0.7229038", "NaN", "INF", "+INF", "-INF", ], dtype=np.float32, ).reshape([3, 4]) input_shape = (3, 4) if from_type in F8_TYPES: np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True, ) elif from_type in FOUR_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_4bitx2(np_from) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif from_type in TWO_BIT_TYPES: np_from = np_fp32.astype(from_np_dtype) packed = onnx.numpy_helper._pack_2bitx4(np_from) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. input = make_tensor( "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: np_from = np_fp32.astype(from_np_dtype) input = make_tensor( "input", from_dtype, input_shape, vals=np_from, raw=True ) if to_type in F8_TYPES: output = make_tensor( "output", to_dtype, input_shape, vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype), raw=True, ) elif to_type in FOUR_BIT_TYPES: packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_4bitx2() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) elif to_type in TWO_BIT_TYPES: packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype)) # No byteswap needed on big-endian machines as _pack_2bitx4() # returns a numpy array with uint8 datatype. output = make_tensor( "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True ) else: output = make_tensor( "output", to_dtype, input_shape, vals=np_from.astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_" + from_type + "_to_" + to_type, ) @staticmethod def export_saturate_false() -> None: test_cases = itertools.product( [ "FLOAT", "FLOAT16", ], [ "FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ", ], ) input_shape = (3, 5) for from_type, to_type in test_cases: from_dtype = getattr(TensorProto, from_type) to_dtype = getattr(TensorProto, to_type) from_np_dtype = tensor_dtype_to_np_dtype(from_dtype) to_np_dtype = tensor_dtype_to_np_dtype(to_dtype) np_fp32 = np.array( [ "0.47892547", "0.48033667", "0.49968487", "0.81910545", "0.47031248", "0.7229038", "1000000", "1e-7", "NaN", "INF", "+INF", "-INF", "-0.0000001", "0.0000001", "-1000000", ], dtype=np.float32, ) input = make_tensor( "input", from_dtype, input_shape, vals=np_fp32.astype(from_np_dtype), raw=True, ) output = make_tensor( "output", to_dtype, input_shape, vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype), raw=True, ) like = make_tensor("like", to_dtype, (0,), vals=[]) node = onnx.helper.make_node( "CastLike", inputs=["input", "like"], outputs=["output"], saturate=0, ) expect( node, inputs=[input, like], outputs=[output], name="test_castlike_no_saturate_" + from_type + "_to_" + to_type, ) onnx-onnx-bca0315/onnx/backend/test/case/node/ceil.py000066400000000000000000000013551511334557700225510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Ceil(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Ceil", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2]).astype(np.float32) y = np.ceil(x) # expected output [-1., 2.] expect(node, inputs=[x], outputs=[y], name="test_ceil_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.ceil(x) expect(node, inputs=[x], outputs=[y], name="test_ceil") onnx-onnx-bca0315/onnx/backend/test/case/node/celu.py000066400000000000000000000030421511334557700225600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Celu(Base): @staticmethod def export() -> None: alpha = 2.0 node = onnx.helper.make_node( "Celu", inputs=["X"], outputs=["Y"], alpha=alpha, ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data positive_input = np.maximum(0, input_data) negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1)) expected_output = positive_input + negative_input expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu") onnx-onnx-bca0315/onnx/backend/test/case/node/center_crop_pad.py000066400000000000000000000075431511334557700247710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class CenterCropPad(Base): @staticmethod def export_center_crop_pad_crop() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(20, 10, 3).astype(np.float32) shape = np.array([10, 7, 3], dtype=np.int64) y = x[5:15, 1:8, :] expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop") @staticmethod def export_center_crop_pad_pad() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # First dim is even diff, second is uneven x = np.random.randn(10, 7, 3).astype(np.float32) shape = np.array([20, 10, 3], dtype=np.int64) y = np.zeros([20, 10, 3], dtype=np.float32) y[5:15, 1:8, :] = x expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad") @staticmethod def export_center_crop_pad_crop_and_pad() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 10, 3], dtype=np.int64) y = np.zeros([10, 10, 3], dtype=np.float32) y[:, 1:9, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_and_pad", ) @staticmethod def export_center_crop_pad_crop_axes_hwc() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[0, 1], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_hwc", ) @staticmethod def export_center_crop_pad_crop_negative_axes_hwc() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[-3, -2], ) # Cropping on first dim, padding on second, third stays the same x = np.random.randn(20, 8, 3).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([10, 9, 3], dtype=np.float32) y[:, :8, :] = x[5:15, :, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_negative_axes_hwc", ) @staticmethod def export_center_crop_pad_crop_axes_chw() -> None: node = onnx.helper.make_node( "CenterCropPad", inputs=["x", "shape"], outputs=["y"], axes=[1, 2], ) # Cropping on second dim, padding on third, first stays the same x = np.random.randn(3, 20, 8).astype(np.float32) shape = np.array([10, 9], dtype=np.int64) y = np.zeros([3, 10, 9], dtype=np.float32) y[:, :, :8] = x[:, 5:15, :] expect( node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop_axes_chw", ) onnx-onnx-bca0315/onnx/backend/test/case/node/clip.py000066400000000000000000000107461511334557700225700ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Clip(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) x = np.array([-2, 0, 2]).astype(np.float32) min_val = np.float32(-1) max_val = np.float32(1) y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.] expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example" ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, max_val) expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip") node = onnx.helper.make_node( "Clip", inputs=["x", "min", "max"], outputs=["y"], ) min_val = np.float32(-5) max_val = np.float32(5) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds" ) x = np.array([-6, 0, 6]).astype(np.float32) y = np.array([-5, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds" ) x = np.array([-1, 0, 6]).astype(np.float32) y = np.array([-1, 0, 5]).astype(np.float32) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_splitbounds", ) x = np.array([-2, 0, 6]).astype(np.float32) y = np.array([1, 1, 1]).astype(np.float32) min_val = np.float32(2) max_val = np.float32(1) expect( node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_min_greater_than_max", ) @staticmethod def export_clip_default() -> None: node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, min_val, np.inf) expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min") no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.float32(0) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, -np.inf, max_val) expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max") no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-1, 0, 1]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds") @staticmethod def export_clip_default_int8() -> None: node = onnx.helper.make_node( "Clip", inputs=["x", "min"], outputs=["y"], ) min_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, min_val, np.iinfo(np.int8).max) expect( node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min" ) no_min = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, "max"], outputs=["y"], ) max_val = np.int8(0) x = np.random.randn(3, 4, 5).astype(np.int8) y = np.clip(x, np.iinfo(np.int8).min, max_val) expect( node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max" ) no_max = "" # optional input, not supplied node = onnx.helper.make_node( "Clip", inputs=["x", no_min, no_max], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.int8) y = np.array([-1, 0, 1]).astype(np.int8) expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds") onnx-onnx-bca0315/onnx/backend/test/case/node/col2im.py000066400000000000000000000254751511334557700230330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Col2Im(Base): """Col2Im operator with N-dimension support The tests below can be reproduced in Python using https://github.com/f-dangel/unfoldNd/ """ @staticmethod def export() -> None: input = np.array( [ [ [1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5) [2.0, 7.0, 12.0, 17.0, 22.0], [3.0, 8.0, 13.0, 18.0, 23.0], [4.0, 9.0, 14.0, 19.0, 24.0], [5.0, 0.0, 15.0, 20.0, 25.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) output = np.array( [ [ [ [1.0, 2.0, 3.0, 4.0, 5.0], # (1, 1, 5, 5) [6.0, 7.0, 8.0, 9.0, 0.0], [11.0, 12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0, 20.0], [21.0, 22.0, 23.0, 24.0, 25.0], ] ] ] ).astype(np.float32) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im", ) @staticmethod def export_col2im_strides() -> None: input = np.array( [ [ [0.0, 0.0, 0.0, 0.0], # (1, 9, 4) [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([3, 3]).astype(np.int64) output = np.array( [ [ [ [0.0, 1.0, 1.0, 1.0, 1.0], # (1, 1, 5, 5) [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 2.0, 1.0, 2.0, 1.0], [1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], strides=[2, 2], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_strides", ) @staticmethod def export_col2im_pads() -> None: input = np.array( [ [ [ 1.0, 6.0, 11.0, 16.0, 21.0, 26, 31, 36, 41, 46, 51, 56, 61, 66, 71, ], # (1, 5, 15) [ 2.0, 7.0, 12.0, 17.0, 22.0, 27, 32, 37, 42, 47, 52, 57, 62, 67, 72, ], [ 3.0, 8.0, 13.0, 18.0, 23.0, 28, 33, 38, 43, 48, 53, 58, 63, 68, 73, ], [ 4.0, 9.0, 14.0, 19.0, 24.0, 29, 34, 39, 44, 49, 54, 59, 64, 69, 74, ], [ 5.0, 10.0, 15.0, 20.0, 25.0, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, ], ] ] ).astype(np.float32) image_shape = np.array([5, 5]).astype(np.int64) block_shape = np.array([1, 5]).astype(np.int64) output = np.array( [ [ [ [8.0, 21.0, 24.0, 27.0, 24.0], # (1, 1, 5, 5) [38.0, 66.0, 69.0, 72.0, 54.0], [68.0, 111.0, 114.0, 117.0, 84.0], [98.0, 156.0, 159.0, 162.0, 114.0], [128.0, 201.0, 204.0, 207.0, 144.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], pads=[0, 1, 0, 1], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_pads", ) @staticmethod def export_col2im_dilations() -> None: input = np.array( [ [ [1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5) [2.0, 6.0, 10.0, 14.0, 18], [3.0, 7.0, 11.0, 15.0, 19], [4.0, 8.0, 12.0, 16.0, 20], ] ] ).astype(np.float32) image_shape = np.array([6, 6]).astype(np.int64) block_shape = np.array([2, 2]).astype(np.int64) output = np.array( [ [ [ [1.0, 0.0, 0.0, 0.0, 0.0, 2.0], # (1, 1, 6, 6) [8.0, 0.0, 0.0, 0.0, 0.0, 10.0], [16.0, 0.0, 0.0, 0.0, 0.0, 18.0], [24.0, 0.0, 0.0, 0.0, 0.0, 26.0], [32.0, 0.0, 0.0, 0.0, 0.0, 34.0], [19.0, 0.0, 0.0, 0.0, 0.0, 20.0], ] ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"], dilations=[1, 5], ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_dilations", ) @staticmethod def export_col2im_5d() -> None: input = np.array( [ [ [1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12) [2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57], [3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58], [4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59], [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60], [61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116], [62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117], [63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118], [64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119], [65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120], ] ] ).astype(np.float32) image_shape = np.array([3, 4, 5]).astype(np.int64) block_shape = np.array([1, 1, 5]).astype(np.int64) output = np.array( [ [ [ [ [1, 2, 3, 4, 5], # (1, 2, 3, 4, 5) [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], ], [ [21, 22, 23, 24, 25], [26, 27, 28, 29, 30], [31, 32, 33, 34, 35], [36, 37, 38, 39, 40], ], [ [41, 42, 43, 44, 45], [46, 47, 48, 49, 50], [51, 52, 53, 54, 55], [56, 57, 58, 59, 60], ], ], [ [ [61, 62, 63, 64, 65], [66, 67, 68, 69, 70], [71, 72, 73, 74, 75], [76, 77, 78, 79, 80], ], [ [81, 82, 83, 84, 85], [86, 87, 88, 89, 90], [91, 92, 93, 94, 95], [96, 97, 98, 99, 100], ], [ [101, 102, 103, 104, 105], [106, 107, 108, 109, 110], [111, 112, 113, 114, 115], [116, 117, 118, 119, 120], ], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "Col2Im", ["input", "image_shape", "block_shape"], ["output"] ) expect( node, inputs=[input, image_shape, block_shape], outputs=[output], name="test_col2im_5d", ) onnx-onnx-bca0315/onnx/backend/test/case/node/compress.py000066400000000000000000000052721511334557700234720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Compress(Base): @staticmethod def export_compress_0() -> None: node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=0, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 1]) output = np.compress(condition, input, axis=0) # print(output) # [[ 3. 4.] # [ 5. 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_0", ) @staticmethod def export_compress_1() -> None: node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_1", ) @staticmethod def export_compress_default_axis() -> None: node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1, 0, 0, 1]) output = np.compress(condition, input) # print(output) # [ 2., 5.] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_default_axis", ) @staticmethod def export_compress_negative_axis() -> None: node = onnx.helper.make_node( "Compress", inputs=["input", "condition"], outputs=["output"], axis=-1, ) input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32) condition = np.array([0, 1]) output = np.compress(condition, input, axis=-1) # print(output) # [[ 2.] # [ 4.] # [ 6.]] expect( node, inputs=[input, condition.astype(bool)], outputs=[output], name="test_compress_negative_axis", ) onnx-onnx-bca0315/onnx/backend/test/case/node/concat.py000066400000000000000000000036101511334557700231000ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import TYPE_CHECKING, Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect if TYPE_CHECKING: from collections.abc import Sequence class Concat(Base): @staticmethod def export() -> None: test_cases: dict[str, Sequence[Any]] = { "1d": ([1, 2], [3, 4]), "2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]), "3d": ( [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]], ), } for test_case, values_ in test_cases.items(): values = [np.asarray(v, dtype=np.float32) for v in values_] for i in range(len(values[0].shape)): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_" + str(i), ) for i in range(-len(values[0].shape), 0): in_args = ["value" + str(k) for k in range(len(values))] node = onnx.helper.make_node( "Concat", inputs=list(in_args), outputs=["output"], axis=i ) output = np.concatenate(values, i) expect( node, inputs=list(values), outputs=[output], name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)), ) onnx-onnx-bca0315/onnx/backend/test/case/node/constant.py000066400000000000000000000014411511334557700234620ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Constant(Base): @staticmethod def export() -> None: values = np.random.randn(5, 5).astype(np.float32) node = onnx.helper.make_node( "Constant", inputs=[], outputs=["values"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=values.shape, vals=values.flatten().astype(float), ), ) expect(node, inputs=[], outputs=[values], name="test_constant") onnx-onnx-bca0315/onnx/backend/test/case/node/constantofshape.py000066400000000000000000000035201511334557700250300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ConstantOfShape(Base): @staticmethod def export_float_ones() -> None: x = np.array([4, 3, 2]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.FLOAT, [1], [1] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.ones(x, dtype=np.float32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_float_ones") @staticmethod def export_int32_zeros() -> None: x = np.array([10, 6]).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_constantofshape_int_zeros") @staticmethod def export_int32_shape_zero() -> None: x = np.array( [ 0, ] ).astype(np.int64) tensor_value = onnx.helper.make_tensor( "value", onnx.TensorProto.INT32, [1], [0] ) node = onnx.helper.make_node( "ConstantOfShape", inputs=["x"], outputs=["y"], value=tensor_value, ) y = np.zeros(x, dtype=np.int32) expect( node, inputs=[x], outputs=[y], name="test_constantofshape_int_shape_zero" ) onnx-onnx-bca0315/onnx/backend/test/case/node/conv.py000066400000000000000000000177551511334557700226150ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Conv(Base): @staticmethod def export() -> None: x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[1, 1, 1, 1], ) y_with_padding = np.array( [ [ [ [12.0, 21.0, 27.0, 33.0, 24.0], # (1, 1, 5, 5) output tensor [33.0, 54.0, 63.0, 72.0, 51.0], [63.0, 99.0, 108.0, 117.0, 81.0], [93.0, 144.0, 153.0, 162.0, 111.0], [72.0, 111.0, 117.0, 123.0, 84.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_basic_conv_with_padding", ) # Convolution without padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1 pads=[0, 0, 0, 0], ) y_without_padding = np.array( [ [ [ [54.0, 63.0, 72.0], # (1, 1, 3, 3) output tensor [99.0, 108.0, 117.0], [144.0, 153.0, 162.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_basic_conv_without_padding", ) @staticmethod def export_conv_with_strides() -> None: x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 7, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with strides=2 and padding node_with_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_padding = np.array( [ [ [ [12.0, 27.0, 24.0], # (1, 1, 4, 3) output tensor [63.0, 108.0, 81.0], [123.0, 198.0, 141.0], [112.0, 177.0, 124.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[x, W], outputs=[y_with_padding], name="test_conv_with_strides_padding", ) # Convolution with strides=2 and no padding node_without_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_without_padding = np.array( [ [ [ [54.0, 72.0], # (1, 1, 3, 2) output tensor [144.0, 162.0], [234.0, 252.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[x, W], outputs=[y_without_padding], name="test_conv_with_strides_no_padding", ) # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor) node_with_asymmetric_padding = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], kernel_shape=[3, 3], pads=[1, 0, 1, 0], strides=[ 2, 2, ], # Default values for other attributes: dilations=[1, 1], groups=1 ) y_with_asymmetric_padding = np.array( [ [ [ [21.0, 33.0], # (1, 1, 4, 2) output tensor [99.0, 117.0], [189.0, 207.0], [171.0, 183.0], ] ] ] ).astype(np.float32) expect( node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding], name="test_conv_with_strides_and_asymmetric_padding", ) @staticmethod def export_conv_with_autopad_same() -> None: x = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], [20.0, 21.0, 22.0, 23.0, 24.0], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ] ] ] ).astype(np.float32) # Convolution with auto_pad='SAME_LOWER' and strides=2 node = onnx.helper.make_node( "Conv", inputs=["x", "W"], outputs=["y"], auto_pad="SAME_LOWER", kernel_shape=[3, 3], strides=[2, 2], ) y = np.array( [[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same") onnx-onnx-bca0315/onnx/backend/test/case/node/convinteger.py000066400000000000000000000052731511334557700241630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ConvInteger(Base): @staticmethod def export_without_padding() -> None: x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2)) y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2) # ConvInteger without padding convinteger_node = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"] ) expect( convinteger_node, inputs=[x, w, x_zero_point], outputs=[y], name="test_convinteger_without_padding", ) @staticmethod def export_with_padding() -> None: x = ( np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) .astype(np.uint8) .reshape((1, 1, 3, 3)) ) x_zero_point = np.uint8(1) w_zero_points = np.array([0, 1], dtype=np.uint8) w = np.array([1, 1, 1, 1, 1, 1, 1, 1]).astype(np.uint8).reshape((2, 1, 2, 2)) y = ( np.array( [ 1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ] ) .astype(np.int32) .reshape((1, 2, 4, 4)) ) # ConvInteger with padding convinteger_node_with_padding = onnx.helper.make_node( "ConvInteger", inputs=["x", "w", "x_zero_point", "w_zero_points"], outputs=["y"], pads=[1, 1, 1, 1], ) expect( convinteger_node_with_padding, inputs=[x, w, x_zero_point, w_zero_points], outputs=[y], name="test_convinteger_with_padding", ) onnx-onnx-bca0315/onnx/backend/test/case/node/convtranspose.py000066400000000000000000000515741511334557700245510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ConvTranspose(Base): @staticmethod def export() -> None: x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], # (1, 2, 5, 5) [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose") @staticmethod def export_convtranspose_1d() -> None: x = np.array([[[0.0, 1.0, 2.0]]]).astype(np.float32) # (1, 1, 3) W = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]).astype( # (1, 2, 3) np.float32 ) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [[[0.0, 1.0, 3.0, 3.0, 2.0], [0.0, 1.0, 3.0, 3.0, 2.0]]] # (1, 2, 5) ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_1d") @staticmethod def export_convtranspose_3d() -> None: x = np.array( [ [ [ [ [0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 3, 4, 5) [5.0, 6.0, 7.0, 8.0, 9.0], [10.0, 11.0, 12.0, 13.0, 14.0], [15.0, 16.0, 17.0, 18.0, 19.0], ], [ [20.0, 21.0, 22.0, 23.0, 24.0], [25.0, 26.0, 27.0, 28.0, 29.0], [30.0, 31.0, 32.0, 33.0, 34.0], [35.0, 36.0, 37.0, 38.0, 39.0], ], [ [40.0, 41.0, 42.0, 43.0, 44.0], [45.0, 46.0, 47.0, 48.0, 49.0], [50.0, 51.0, 52.0, 53.0, 54.0], [55.0, 56.0, 57.0, 58.0, 59.0], ], ] ] ] ).astype(np.float32) W = np.array( [ [ [ [ [1.0, 1.0, 1.0], # (1, 2, 3, 3, 3) [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], ], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"]) y = np.array( [ [ [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], # (1, 2, 5, 6, 7) [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], [ [ [0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], [5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0], [15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0], [30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0], [25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0], [15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0], ], [ [20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0], [50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0], [90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0], [120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0], [90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0], [50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0], ], [ [60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0], [135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0], [225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0], [270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0], [195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0], [105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0], ], [ [60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0], [130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0], [210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0], [240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0], [170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0], [90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0], ], [ [40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0], [85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0], [135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0], [150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0], [105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0], [55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0], ], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_3d") @staticmethod def export_convtranspose_attributes() -> None: x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], # (1, 2, 10, 8) [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_shape=[10, 8] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_output_shape") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_padding=[1, 1] ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pad") node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], name="test", strides=[3, 2], output_shape=[10, 8], kernel_shape=[3, 3], output_padding=[1, 1], ) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_kernel_shape") @staticmethod def export_convtranspose_pads() -> None: x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], pads=[1, 2, 1, 2] ) y = np.array( [ [ [ [1.0, 1.0, 3.0], # (1, 2, 7, 3) [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], [ [1.0, 1.0, 3.0], [1.0, 1.0, 3.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [7.0, 4.0, 9.0], [13.0, 7.0, 15.0], [13.0, 7.0, 15.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pads") @staticmethod def export_convtranspose_dilations() -> None: x = np.array( [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2] ) y = np.array( [ [ [ [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5] [63.0, 35.0, 67.0, 10.0, 14.0], [24.0, 22.0, 76.0, 76.0, 21.0], [9.0, 5.0, 88.0, 45.0, 63.0], [3.0, 2.0, 33.0, 18.0, 54.0], ] ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_dilations") @staticmethod def export_convtranspose_autopad_same() -> None: x = np.array( [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3) ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3) [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ] ] ).astype(np.float32) node = onnx.helper.make_node( "ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2] ) y = np.array( [ [ [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], [ [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [0.0, 0.0, 1.0, 1.0, 3.0, 2.0], [3.0, 3.0, 8.0, 5.0, 12.0, 7.0], [3.0, 3.0, 7.0, 4.0, 9.0, 5.0], [9.0, 9.0, 20.0, 11.0, 24.0, 13.0], [6.0, 6.0, 13.0, 7.0, 15.0, 8.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_autopad_same") @staticmethod def export_convtranspose_group_2() -> None: x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ] ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2") @staticmethod def export_convtranspose_group_2_image_3() -> None: x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]], ], ] ).astype(np.float32) W = np.array( [ [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], [ [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], ], ] ).astype(np.float32) node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], group=2) y = np.array( [ [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [18.0, 37.0, 57.0, 39.0, 20.0], [39.0, 80.0, 123.0, 84.0, 43.0], [63.0, 129.0, 198.0, 135.0, 69.0], [45.0, 92.0, 141.0, 96.0, 49.0], [24.0, 49.0, 75.0, 51.0, 26.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], [ [ [0.0, 1.0, 3.0, 3.0, 2.0], [3.0, 8.0, 15.0, 12.0, 7.0], [9.0, 21.0, 36.0, 27.0, 15.0], [9.0, 20.0, 33.0, 24.0, 13.0], [6.0, 13.0, 21.0, 15.0, 8.0], ], [ [9.0, 19.0, 30.0, 21.0, 11.0], [21.0, 44.0, 69.0, 48.0, 25.0], [36.0, 75.0, 117.0, 81.0, 42.0], [27.0, 56.0, 87.0, 60.0, 31.0], [15.0, 31.0, 48.0, 33.0, 17.0], ], ], ] ).astype(np.float32) expect( node, inputs=[x, W], outputs=[y], name="test_convtranspose_group_2_image_3" ) onnx-onnx-bca0315/onnx/backend/test/case/node/cos.py000066400000000000000000000013111511334557700224110ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Cos(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Cos", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cos(x) expect(node, inputs=[x], outputs=[y], name="test_cos") onnx-onnx-bca0315/onnx/backend/test/case/node/cosh.py000066400000000000000000000014011511334557700225610ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Cosh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Cosh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.cosh(x) # expected output [1.54308069, 1., 1.54308069] expect(node, inputs=[x], outputs=[y], name="test_cosh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.cosh(x) expect(node, inputs=[x], outputs=[y], name="test_cosh") onnx-onnx-bca0315/onnx/backend/test/case/node/cumsum.py000066400000000000000000000103371511334557700231460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class CumSum(Base): @staticmethod def export_cumsum_1d() -> None: node = onnx.helper.make_node("CumSum", inputs=["x", "axis"], outputs=["y"]) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([1.0, 3.0, 6.0, 10.0, 15.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d") @staticmethod def export_cumsum_1d_exclusive() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([0.0, 1.0, 3.0, 6.0, 10.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_exclusive") @staticmethod def export_cumsum_1d_reverse() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([15.0, 14.0, 12.0, 9.0, 5.0]).astype(np.float64) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse") @staticmethod def export_cumsum_1d_reverse_exclusive() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1, exclusive=1 ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64) axis = np.int32(0) y = np.array([14.0, 12.0, 9.0, 5.0, 0.0]).astype(np.float64) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse_exclusive" ) @staticmethod def export_cumsum_2d_axis_0() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(0) y = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 9.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_0") @staticmethod def export_cumsum_2d_axis_1() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_1") @staticmethod def export_cumsum_2d_negative_axis() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3)) axis = np.int32(-1) y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis") @staticmethod def export_cumsum_2d_int32() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], ) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.int32).reshape((2, 3)) axis = np.int32(0) y = np.array([1, 2, 3, 5, 7, 9]).astype(np.int32).reshape((2, 3)) expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_int32") @staticmethod def export_cumsum_1d_int32_exclusive() -> None: node = onnx.helper.make_node( "CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1 ) x = np.array([1, 2, 3, 4, 5]).astype(np.int32) axis = np.int32(0) y = np.array([0, 1, 3, 6, 10]).astype(np.int32) expect( node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_int32_exclusive" ) onnx-onnx-bca0315/onnx/backend/test/case/node/deformconv.py000066400000000000000000000123701511334557700237760ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class DeformConv(Base): @staticmethod def export() -> None: X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) # Convolution with padding offset_with_padding = np.zeros((1, 8, 4, 4), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_with_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [1, 2] offset_with_padding[0, 5, 1, 2] = -0.1 node_with_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_with_padding"], outputs=["Y_with_padding"], kernel_shape=[2, 2], pads=[1, 1, 1, 1], ) Y_with_padding = np.array( [ [ [ [0.0, 1.0, 3.0, 2.0], # (1, 1, 4, 4) output tensor [3.0, 8.0, 11.9, 7.0], [9.0, 20.0, 24.0, 13.0], [6.0, 13.0, 15.0, 8.0], ] ] ] ).astype(np.float32) expect( node_with_padding, inputs=[X, W, offset_with_padding], outputs=[Y_with_padding], name="test_basic_deform_conv_with_padding", ) # Convolution without padding offset_without_padding = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset_without_padding[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset_without_padding[0, 5, 0, 1] = -0.1 node_without_padding = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset_without_padding"], outputs=["Y_without_padding"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y_without_padding = np.array( [ [ [ [9.5, 11.9], # (1, 1, 2, 2) output tensor [20.0, 24.0], ] ] ] ).astype(np.float32) expect( node_without_padding, inputs=[X, W, offset_without_padding], outputs=[Y_without_padding], name="test_basic_deform_conv_without_padding", ) @staticmethod def export_deformconv_with_mask_bias() -> None: X = np.arange(9).astype(np.float32) X.shape = (1, 1, 3, 3) W = np.ones((1, 1, 2, 2), dtype=np.float32) B = np.ones((1,), dtype=np.float32) offset = np.zeros((1, 8, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel, at output position [0, 1] offset[0, 5, 0, 1] = -0.1 mask = np.ones((1, 4, 2, 2), dtype=np.float32) mask[0, 2, 1, 1] = 0.2 # [1, 0] element of kernel at output position [1, 1] node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset", "B", "mask"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], ) Y = np.array( [ [ [ [10.5, 12.9], # (1, 1, 2, 2) output tensor [21.0, 19.4], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset, B, mask], outputs=[Y], name="test_deform_conv_with_mask_bias", ) @staticmethod def export_deformconv_with_multiple_offset_groups() -> None: X = np.zeros((1, 2, 3, 3), dtype=np.float32) X[0, 0] = np.reshape(np.arange(9).astype(np.float32), (3, 3)) X[0, 1] = np.reshape(np.arange(8, -1, -1).astype(np.float32), (3, 3)) X.shape = (1, 2, 3, 3) W = np.ones((1, 2, 2, 2), dtype=np.float32) offset = np.zeros((1, 16, 2, 2), dtype=np.float32) # h-coord of [0, 0] element of kernel in channel 0, at output position [0, 0] offset[0, 0, 0, 0] = 0.5 # w-coord of [1, 0] element of kernel in channel 1, at output position [0, 1] offset[0, 13, 0, 1] = -0.1 node = onnx.helper.make_node( "DeformConv", inputs=["X", "W", "offset"], outputs=["Y"], kernel_shape=[2, 2], pads=[0, 0, 0, 0], offset_group=2, ) Y = np.array( [ [ [ [33.5, 32.1], # (1, 1, 2, 2) output tensor [32.0, 32.0], ] ] ] ).astype(np.float32) expect( node, inputs=[X, W, offset], outputs=[Y], name="test_deform_conv_with_multiple_offset_groups", ) onnx-onnx-bca0315/onnx/backend/test/case/node/depthtospace.py000077500000000000000000000067621511334557700243320ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class DepthToSpace(Base): @staticmethod def export_default_mode_example() -> None: node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="DCR" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 18.0, 1.0, 19.0, 2.0, 20.0], [36.0, 54.0, 37.0, 55.0, 38.0, 56.0], [3.0, 21.0, 4.0, 22.0, 5.0, 23.0], [39.0, 57.0, 40.0, 58.0, 41.0, 59.0], ], [ [9.0, 27.0, 10.0, 28.0, 11.0, 29.0], [45.0, 63.0, 46.0, 64.0, 47.0, 65.0], [12.0, 30.0, 13.0, 31.0, 14.0, 32.0], [48.0, 66.0, 49.0, 67.0, 50.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_example") @staticmethod def export_crd_mode_example() -> None: node = onnx.helper.make_node( "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="CRD" ) # (1, 8, 2, 3) input tensor x = np.array( [ [ [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]], [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]], [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]], [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]], [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]], [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]], [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]], [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]], ] ] ).astype(np.float32) # (1, 2, 4, 6) output tensor y = np.array( [ [ [ [0.0, 9.0, 1.0, 10.0, 2.0, 11.0], [18.0, 27.0, 19.0, 28.0, 20.0, 29.0], [3.0, 12.0, 4.0, 13.0, 5.0, 14.0], [21.0, 30.0, 22.0, 31.0, 23.0, 32.0], ], [ [36.0, 45.0, 37.0, 46.0, 38.0, 47.0], [54.0, 63.0, 55.0, 64.0, 56.0, 65.0], [39.0, 48.0, 40.0, 49.0, 41.0, 50.0], [57.0, 66.0, 58.0, 67.0, 59.0, 68.0], ], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_depthtospace_crd_mode_example") onnx-onnx-bca0315/onnx/backend/test/case/node/dequantizelinear.py000066400000000000000000000256471511334557700252130ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx import TensorProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import make_tensor class DequantizeLinear(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # scalar zero point and scale x = np.array([0, 3, 128, 255]).astype(np.uint8) x_scale = np.float32(2) x_zero_point = np.uint8(128) y = np.array([-256, -250, 0, 254], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear", ) @staticmethod def export_axis() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) # 1-D tensor zero point and scale of size equal to axis 1 of the input tensor x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array([2, 4, 5], dtype=np.float32) x_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = ( x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32) ) * x_scale.reshape(1, 3, 1, 1) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_axis", ) @staticmethod def export_e4m3fn() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn", ) @staticmethod def export_e4m3fn_float16() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) x_scale = np.float16(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float16) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e4m3fn_float16", ) @staticmethod def export_e4m3fn_zero_point() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104]) zero_point = make_tensor("zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32) expect( node, inputs=[x, x_scale, zero_point], outputs=[y], name="test_dequantizelinear_e4m3fn_zero_point", ) @staticmethod def export_e5m2() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, -96]) x_scale = np.float32(2) y = np.array([0.0, 1.0, 2.0, 98304.0, -192.0], dtype=np.float32) expect( node, inputs=[x, x_scale], outputs=[y], name="test_dequantizelinear_e5m2", ) @staticmethod def export_uint16() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([30000, 31000, 32768, 33000]).astype(np.uint16) x_scale = np.float32(2) x_zero_point = np.uint16(32767) y = np.array([-5534.0, -3534.0, 2.0, 466.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint16", ) @staticmethod def export_int16() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], ) x = np.array([-300, -30, -1025, 1270]).astype(np.int16) x_scale = np.float32(2) x_zero_point = np.int16(-1024) y = np.array([1448.0, 1988.0, -2.0, 4588.0], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int16", ) @staticmethod def export_uint4() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT4, [5], [0, 1, 7, 10, 15]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT4, (1,), [1]) y = np.array([-2, 0, 12, 18, 28], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint4", ) @staticmethod def export_int4() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT4, [5], [0, 1, 7, -4, -8]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT4, (1,), [1]) y = np.array([-2, 0, 12, -10, -18], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int4", ) @staticmethod def export_uint2() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.UINT2, [4], [0, 1, 2, 3]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.UINT2, (1,), [1]) y = np.array([-2, 0, 2, 4], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_uint2", ) @staticmethod def export_int2() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.INT2, [4], [0, 1, -1, -2]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.INT2, (1,), [1]) y = np.array([-2, 0, -4, -6], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_int2", ) @staticmethod def export_float4e2m1() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=0, ) # scalar zero point and scale x = make_tensor("x", TensorProto.FLOAT4E2M1, [5], [0, 1, -1, 1.5, -4]) x_scale = np.float32(2) x_zero_point = make_tensor("x_zero_point", TensorProto.FLOAT4E2M1, (1,), [0]) y = np.array([0, 2, -2, 3, -8], dtype=np.float32) expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_float4e2m1", ) @staticmethod def export_blocked() -> None: node = onnx.helper.make_node( "DequantizeLinear", inputs=["x", "x_scale", "x_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [ [[3, 89], [34, 200], [74, 59]], [[5, 24], [24, 87], [32, 13]], [[5, 12], [12, 33], [65, 42]], [[245, 99], [4, 142], [121, 102]], ], ], dtype=np.uint8, ) x_scale = np.array( [ [ [[3.0, 2.0], [4.0, 1.0], [2.0, 2.0]], [[5.0, 2.0], [4.0, 3.0], [5.0, 2.0]], ], ], dtype=np.float32, ) x_zero_point = np.array( [ [ [[1, 0], [0, 1], [2, 20]], [[3, 2], [4, 3], [15, 2]], ], ], dtype=np.uint8, ) # x.shape = (1, 4, 3, 2) # x_scale.shape = (1, 2, 3, 2) assert x_scale.shape == x_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // x_scale.shape[i] for i in range(len(x.shape))] = (1, 2, 1, 1) assert all( x.shape[i] == x_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % x_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // x_scale.shape[block_axis] # Create element-wise scale and zero point x_scale_elementwise = np.repeat(x_scale, repeats=repeats, axis=block_axis) x_zero_point_elementwise = np.repeat( x_zero_point, repeats=repeats, axis=block_axis ) y = ( x.astype(np.float32) - x_zero_point_elementwise.astype(np.float32) ) * x_scale_elementwise expect( node, inputs=[x, x_scale, x_zero_point], outputs=[y], name="test_dequantizelinear_blocked", ) onnx-onnx-bca0315/onnx/backend/test/case/node/det.py000066400000000000000000000017621511334557700224130ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Det(Base): @staticmethod def export_2d() -> None: node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.arange(4).reshape(2, 2).astype(np.float32) y = np.linalg.det(x) # expect -2 expect(node, inputs=[x], outputs=[y], name="test_det_2d") @staticmethod def export_nd() -> None: node = onnx.helper.make_node( "Det", inputs=["x"], outputs=["y"], ) x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype( np.float32 ) y = np.linalg.det(x) # expect array([-2., -3., -8.]) expect(node, inputs=[x], outputs=[y], name="test_det_nd") onnx-onnx-bca0315/onnx/backend/test/case/node/dft.py000066400000000000000000000064271511334557700224170ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class DFT(Base): @staticmethod def export_opset19() -> None: node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_axis_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) node = onnx.helper.make_node( "DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape( 10, 10, ) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect( node, inputs=[x], outputs=[y], name="test_dft_inverse_opset19", opset_imports=[onnx.helper.make_opsetid("", 19)], ) @staticmethod def export() -> None: node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(1, dtype=np.int64) y = np.fft.fft(x, axis=0) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft") node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"]) x = np.arange(0, 100).reshape(10, 10).astype(np.float32) axis = np.array(2, dtype=np.int64) y = np.fft.fft(x, axis=1) x = x.reshape(1, 10, 10, 1) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_axis") node = onnx.helper.make_node( "DFT", inputs=["x", "", "axis"], outputs=["y"], inverse=1 ) x = np.arange(0, 100, dtype=np.complex64).reshape(10, 10) axis = np.array(1, dtype=np.int64) y = np.fft.ifft(x, axis=0) x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2) expect(node, inputs=[x, axis], outputs=[y], name="test_dft_inverse") onnx-onnx-bca0315/onnx/backend/test/case/node/div.py000066400000000000000000000050611511334557700224150ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Div(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.array([3, 4]).astype(np.float32) y = np.array([1, 2]).astype(np.float32) z = x / y # expected output [3., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_div_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) + 1 z = x // y expect(node, inputs=[x, y], outputs=[z], name="test_div_uint64") @staticmethod def export_div_broadcast() -> None: node = onnx.helper.make_node( "Div", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.rand(5).astype(np.float32) + 1.0 z = x / y expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/dropout.py000066400000000000000000000143051511334557700233300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx import helper from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def dropout(X, drop_probability=0.5, seed=0, training_mode=False, return_mask=False): if drop_probability == 0 or training_mode is False: if return_mask is True: return X, np.ones(X.shape, dtype=bool) return X np.random.seed(seed) mask = np.random.uniform(0, 1.0, X.shape) >= drop_probability scale = 1 / (1 - drop_probability) if return_mask: return mask * X * scale, mask.astype(bool) return mask * X * scale class Dropout(Base): # Inferencing tests. @staticmethod def export_default() -> None: seed = np.int64(0) node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x) expect(node, inputs=[x], outputs=[y], name="test_dropout_default") @staticmethod def export_default_ratio() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y = dropout(x, r) expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio") @staticmethod def export_default_mask() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, return_mask=True) expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask") @staticmethod def export_default_mask_ratio() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed ) r = np.float32(0.1) x = np.random.randn(3, 4, 5).astype(np.float32) y, z = dropout(x, r, return_mask=True) expect( node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio" ) # Training tests. @staticmethod def export_training_default() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default" ) @staticmethod def export_training_default_ratio_mask() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.5) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_default_mask", ) @staticmethod def export_training() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout") @staticmethod def export_training_ratio_mask() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.75) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask" ) @staticmethod def export_training_default_zero_ratio() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y = dropout(x, r, training_mode=t) expect( node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio" ) @staticmethod def export_training_default_zero_ratio_mask() -> None: seed = np.int64(0) node = onnx.helper.make_node( "Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed ) x = np.random.randn(3, 4, 5).astype(np.float32) r = np.float32(0.0) t = np.bool_(True) y, z = dropout(x, r, training_mode=t, return_mask=True) expect( node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_zero_ratio_mask", ) # Old dropout tests @staticmethod def export_default_old() -> None: node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_default_old", opset_imports=[helper.make_opsetid("", 11)], ) @staticmethod def export_random_old() -> None: node = onnx.helper.make_node( "Dropout", inputs=["x"], outputs=["y"], ratio=0.2, ) x = np.random.randn(3, 4, 5).astype(np.float32) y = x expect( node, inputs=[x], outputs=[y], name="test_dropout_random_old", opset_imports=[helper.make_opsetid("", 11)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/dynamicquantizelinear.py000066400000000000000000000046551511334557700262430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class DynamicQuantizeLinear(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "DynamicQuantizeLinear", inputs=["x"], outputs=["y", "y_scale", "y_zero_point"], ) # expected scale 0.0196078438 and zero point 153 X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear", ) # expected scale 0.0156862754 and zero point 255 X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32) x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_max_adjusted", ) X = ( np.array([1, 2.1, 1.3, 2.5, 3.34, 4.0, 1.5, 2.6, 3.9, 4.0, 3.0, 2.345]) .astype(np.float32) .reshape((3, 4)) ) # expected scale 0.0156862754 and zero point 0 x_min = np.minimum(0, np.min(X)) x_max = np.maximum(0, np.max(X)) Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255] Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8) Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8) expect( node, inputs=[X], outputs=[Y, Y_Scale, Y_ZeroPoint], name="test_dynamicquantizelinear_min_adjusted", ) onnx-onnx-bca0315/onnx/backend/test/case/node/einsum.py000066400000000000000000000052051511334557700231330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def einsum_reference_implementation( Eqn: str, Operands: tuple[np.ndarray, ...] ) -> np.ndarray: return np.einsum(Eqn, *Operands) class Einsum(Base): @staticmethod def export_einsum_transpose() -> None: Eqn = "ij->ji" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Y = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose") @staticmethod def export_einsum_sum() -> None: Eqn = "ij->i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 4) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum") @staticmethod def export_einsum_batch_diagonal() -> None: Eqn = "...ii ->...i" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.random.randn(3, 5, 5) Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal") @staticmethod def export_einsum_inner_prod() -> None: Eqn = "i,i" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5) Y = np.random.randn(5) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod") @staticmethod def export_einsum_batch_matmul() -> None: Eqn = "bij, bjk -> bik" node = onnx.helper.make_node( "Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn ) X = np.random.randn(5, 2, 3) Y = np.random.randn(5, 3, 4) Z = einsum_reference_implementation(Eqn, (X, Y)) expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul") @staticmethod def export_einsum_scalar() -> None: Eqn = "->" node = onnx.helper.make_node( "Einsum", inputs=["x"], outputs=["y"], equation=Eqn ) X = np.array(5.0) # scalar input Z = einsum_reference_implementation(Eqn, (X,)) expect(node, inputs=[X], outputs=[Z], name="test_einsum_scalar") onnx-onnx-bca0315/onnx/backend/test/case/node/elu.py000066400000000000000000000023531511334557700224210ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Elu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node("Elu", inputs=["x"], outputs=["y"], alpha=2.0) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-1.2642411, 0., 1.] y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 expect(node, inputs=[x], outputs=[y], name="test_elu") @staticmethod def export_elu_default() -> None: default_alpha = 1.0 node = onnx.helper.make_node( "Elu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_elu_default") onnx-onnx-bca0315/onnx/backend/test/case/node/equal.py000066400000000000000000000063531511334557700227470ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Equal(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(3, 4, 5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal") x = (np.random.randn(3, 4, 5) * 10).astype(np.int8) y = (np.random.randn(3, 4, 5) * 10).astype(np.int8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int8") x = (np.random.randn(3, 4, 5) * 10).astype(np.int16) y = (np.random.randn(3, 4, 5) * 10).astype(np.int16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint64") @staticmethod def export_equal_broadcast() -> None: node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = (np.random.randn(3, 4, 5) * 10).astype(np.int32) y = (np.random.randn(5) * 10).astype(np.int32) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast") @staticmethod def export_equal_string() -> None: node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1", "string3"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string") @staticmethod def export_equal_string_broadcast() -> None: node = onnx.helper.make_node( "Equal", inputs=["x", "y"], outputs=["z"], ) x = np.array(["string1", "string2"], dtype=np.dtype(object)) y = np.array(["string1"], dtype=np.dtype(object)) z = np.equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_equal_string_broadcast") onnx-onnx-bca0315/onnx/backend/test/case/node/erf.py000066400000000000000000000011541511334557700224060ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import math import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Erf(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Erf", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) y = np.vectorize(math.erf)(x).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_erf") onnx-onnx-bca0315/onnx/backend/test/case/node/exp.py000066400000000000000000000013711511334557700224270ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Exp(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Exp", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.exp(x) # expected output [0.36787945, 1., 2.71828175] expect(node, inputs=[x], outputs=[y], name="test_exp_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.exp(x) expect(node, inputs=[x], outputs=[y], name="test_exp") onnx-onnx-bca0315/onnx/backend/test/case/node/expand.py000066400000000000000000000036331511334557700231150ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Expand(Base): @staticmethod def export_dim_changed() -> None: node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] new_shape = [2, 1, 6] expanded = data * np.ones(new_shape, dtype=np.float32) # print(expanded) # [[[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]], # # [[1., 1., 1., 1., 1., 1.], # [2., 2., 2., 2., 2., 2.], # [3., 3., 3., 3., 3., 3.]]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_changed", ) @staticmethod def export_dim_unchanged() -> None: node = onnx.helper.make_node( "Expand", inputs=["data", "new_shape"], outputs=["expanded"], ) shape = [3, 1] new_shape = [3, 4] data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[1.], [2.], [3.]] expanded = np.tile(data, 4) # print(expanded) # [[1., 1., 1., 1.], # [2., 2., 2., 2.], # [3., 3., 3., 3.]] new_shape = np.array(new_shape, dtype=np.int64) expect( node, inputs=[data, new_shape], outputs=[expanded], name="test_expand_dim_unchanged", ) onnx-onnx-bca0315/onnx/backend/test/case/node/eyelike.py000066400000000000000000000033251511334557700232630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class EyeLike(Base): @staticmethod def export_without_dtype() -> None: shape = (4, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.int32) expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype") @staticmethod def export_with_dtype() -> None: shape = (3, 4) node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], dtype=onnx.TensorProto.DOUBLE, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], dtype=np.float64) expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype") @staticmethod def export_populate_off_main_diagonal() -> None: shape = (4, 5) off_diagonal_offset = 1 node = onnx.helper.make_node( "EyeLike", inputs=["x"], outputs=["y"], k=off_diagonal_offset, dtype=onnx.TensorProto.FLOAT, ) x = np.random.randint(0, 100, size=shape, dtype=np.int32) y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32) expect( node, inputs=[x], outputs=[y], name="test_eyelike_populate_off_main_diagonal", ) onnx-onnx-bca0315/onnx/backend/test/case/node/flatten.py000066400000000000000000000036141511334557700232720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Flatten(Base): @staticmethod def export() -> None: shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(len(shape)): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i)) @staticmethod def export_flatten_with_default_axis() -> None: node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], # Default value for axis: axis=1 ) shape = (5, 4, 3, 2) a = np.random.random_sample(shape).astype(np.float32) new_shape = (5, 24) b = np.reshape(a, new_shape) expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis") @staticmethod def export_flatten_negative_axis() -> None: shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(np.float32) for i in range(-len(shape), 0): node = onnx.helper.make_node( "Flatten", inputs=["a"], outputs=["b"], axis=i, ) new_shape = (np.prod(shape[0:i]).astype(int), -1) b = np.reshape(a, new_shape) expect( node, inputs=[a], outputs=[b], name="test_flatten_negative_axis" + str(abs(i)), ) onnx-onnx-bca0315/onnx/backend/test/case/node/floor.py000066400000000000000000000013721511334557700227550ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Floor(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Floor", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2, 2]).astype(np.float32) y = np.floor(x) # expected output [-2., 1., 2.] expect(node, inputs=[x], outputs=[y], name="test_floor_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.floor(x) expect(node, inputs=[x], outputs=[y], name="test_floor") onnx-onnx-bca0315/onnx/backend/test/case/node/gather.py000066400000000000000000000045121511334557700231050ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Gather(Base): @staticmethod def export_gather_0() -> None: node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=0) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_0", ) @staticmethod def export_gather_1() -> None: node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(5, 4, 3, 2).astype(np.float32) indices = np.array([0, 1, 3]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_1", ) @staticmethod def export_gather_2d_indices() -> None: node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=1, ) data = np.random.randn(3, 3).astype(np.float32) indices = np.array([[0, 2]]) y = np.take(data, indices, axis=1) expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_2d_indices", ) @staticmethod def export_gather_negative_indices() -> None: node = onnx.helper.make_node( "Gather", inputs=["data", "indices"], outputs=["y"], axis=0, ) data = np.arange(10).astype(np.float32) indices = np.array([0, -9, -10]) y = np.take(data, indices, axis=0) # print(y) # [0. 1. 0.] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_negative_indices", ) onnx-onnx-bca0315/onnx/backend/test/case/node/gatherelements.py000066400000000000000000000051551511334557700246460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # The below GatherElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360 def gather_elements(data, indices, axis=0): data_swapped = np.swapaxes(data, 0, axis) index_swapped = np.swapaxes(indices, 0, axis) gathered = np.choose(index_swapped, data_swapped, mode="wrap") return np.swapaxes(gathered, 0, axis) class GatherElements(Base): @staticmethod def export_gather_elements_0() -> None: axis = 1 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2], [3, 4]], dtype=np.float32) indices = np.array([[0, 0], [1, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[1, 1], # [4, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_0", ) @staticmethod def export_gather_elements_1() -> None: axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[4, 8, 3], # [7, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_1", ) @staticmethod def export_gather_elements_negative_indices() -> None: axis = 0 node = onnx.helper.make_node( "GatherElements", inputs=["data", "indices"], outputs=["y"], axis=axis, ) data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32) indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32) y = gather_elements(data, indices, axis) # print(y) produces # [[7, 5, 3], # [4, 2, 3]] expect( node, inputs=[data, indices.astype(np.int64)], outputs=[y], name="test_gather_elements_negative_indices", ) onnx-onnx-bca0315/onnx/backend/test/case/node/gathernd.py000066400000000000000000000077111511334557700234330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def gather_nd_impl( data: np.ndarray, indices: np.ndarray, batch_dims: int ) -> np.ndarray: # Note the data rank - will be reused multiple times later data_rank = len(data.shape) # Check input tensors' shape/rank condition assert indices.shape[-1] <= data_rank # The list of data/indice shape of batch_dims batch_dims_shape = [] # The number of elements in the batch_dims for data/indice array batch_dims_size = 1 # Check the shape of indice and data are identical for batch dims. for i in range(batch_dims): batch_dims_shape.append(indices.shape[i]) batch_dims_size *= indices.shape[i] # Compute output of the op as below # Compute shape of output array output_shape = ( batch_dims_shape + list(indices.shape)[batch_dims:-1] if (indices.shape[-1] == data_rank - batch_dims) else batch_dims_shape + list(indices.shape)[batch_dims:-1] + list(data.shape)[batch_dims + indices.shape[-1] :] ) # Placeholder for output data output_data_buffer = [] # Flatten 'indices' to 2D array reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1]) # Flatten 'data' to array of shape (batch_dim_size, data.shape[batch_dimes:]) reshaped_data = data.reshape((batch_dims_size, *data.shape[batch_dims:])) # gather each scalar value from 'data' for batch_dim in range(reshaped_indices.shape[0]): for outer_dim in range(reshaped_indices.shape[1]): gather_index = tuple(reshaped_indices[batch_dim][outer_dim]) output_data_buffer.append(reshaped_data[(batch_dim, *gather_index)]) return np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape) class GatherND(Base): @staticmethod def export_int32() -> None: node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[0, 1], [2, 3]], dtype=np.int32) indices = np.array([[0, 0], [1, 1]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([0, 3], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32", ) @staticmethod def export_float32() -> None: node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32) indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64) output = gather_nd_impl(data, indices, 0) expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_float32", ) @staticmethod def export_int32_batchdim_1() -> None: node = onnx.helper.make_node( "GatherND", inputs=["data", "indices"], outputs=["output"], batch_dims=1, ) data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32) indices = np.array([[1], [0]], dtype=np.int64) output = gather_nd_impl(data, indices, 1) expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32) assert np.array_equal(output, expected_output) expect( node, inputs=[data, indices], outputs=[output], name="test_gathernd_example_int32_batch_dim1", ) onnx-onnx-bca0315/onnx/backend/test/case/node/gelu.py000066400000000000000000000034161511334557700225710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import math import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Gelu(Base): @staticmethod def export_gelu_tanh() -> None: node = onnx.helper.make_node( "Gelu", inputs=["x"], outputs=["y"], approximate="tanh" ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.158808, 0., 0.841192] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.9963627, 3.99993, 4.9999995] y = ( 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_2") @staticmethod def export_gelu_default() -> None: node = onnx.helper.make_node("Gelu", inputs=["x"], outputs=["y"]) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.15865526, 0., 0.84134474] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_1") x = np.random.randn(3, 4, 5).astype(np.float32) # expected output [2.99595031, 3.99987331, 4.99999857] y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_gelu_default_2") onnx-onnx-bca0315/onnx/backend/test/case/node/gemm.py000066400000000000000000000134621511334557700225640ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def gemm_reference_implementation( A: np.ndarray, B: np.ndarray, C: np.ndarray | None = None, alpha: float = 1.0, beta: float = 1.0, transA: int = 0, transB: int = 0, ) -> np.ndarray: A = A if transA == 0 else A.T B = B if transB == 0 else B.T C = C if C is not None else np.array(0) Y = alpha * np.dot(A, B) + beta * C return Y.astype(A.dtype) class Gemm(Base): @staticmethod def export_default_zero_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias") @staticmethod def export_default_no_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"]) a = np.random.ranf([2, 10]).astype(np.float32) b = np.random.ranf([10, 3]).astype(np.float32) y = gemm_reference_implementation(a, b) expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias") @staticmethod def export_default_scalar_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 3]).astype(np.float32) b = np.random.ranf([3, 4]).astype(np.float32) c = np.array(3.14).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias" ) @staticmethod def export_default_single_elem_vector_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 7]).astype(np.float32) b = np.random.ranf([7, 3]).astype(np.float32) c = np.random.ranf([1]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_single_elem_vector_bias", ) @staticmethod def export_default_vector_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias" ) @staticmethod def export_default_matrix_bias() -> None: node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"]) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.random.ranf([3, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c) expect( node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias" ) @staticmethod def export_transposeA() -> None: node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1 ) a = np.random.ranf([6, 3]).astype(np.float32) b = np.random.ranf([6, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transA=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA") @staticmethod def export_transposeB() -> None: node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1 ) a = np.random.ranf([3, 6]).astype(np.float32) b = np.random.ranf([4, 6]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, transB=1) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB") @staticmethod def export_alpha() -> None: node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5 ) a = np.random.ranf([3, 5]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.zeros([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, alpha=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha") @staticmethod def export_beta() -> None: node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5 ) a = np.random.ranf([2, 7]).astype(np.float32) b = np.random.ranf([7, 4]).astype(np.float32) c = np.random.ranf([1, 4]).astype(np.float32) y = gemm_reference_implementation(a, b, c, beta=0.5) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta") @staticmethod def export_all_attributes() -> None: node = onnx.helper.make_node( "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.25, beta=0.35, transA=1, transB=1, ) a = np.random.ranf([4, 3]).astype(np.float32) b = np.random.ranf([5, 4]).astype(np.float32) c = np.random.ranf([1, 5]).astype(np.float32) y = gemm_reference_implementation( a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35 ) expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes") onnx-onnx-bca0315/onnx/backend/test/case/node/globalaveragepool.py000066400000000000000000000023721511334557700253220ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class GlobalAveragePool(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool") @staticmethod def export_globalaveragepool_precomputed() -> None: node = onnx.helper.make_node( "GlobalAveragePool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[5]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed") onnx-onnx-bca0315/onnx/backend/test/case/node/globalmaxpool.py000066400000000000000000000023411511334557700244710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class GlobalMaxPool(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.random.randn(1, 3, 5, 5).astype(np.float32) y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool") @staticmethod def export_globalmaxpool_precomputed() -> None: node = onnx.helper.make_node( "GlobalMaxPool", inputs=["x"], outputs=["y"], ) x = np.array( [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] ] ] ).astype(np.float32) y = np.array([[[[9]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool_precomputed") onnx-onnx-bca0315/onnx/backend/test/case/node/greater.py000066400000000000000000000046241511334557700232700ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Greater(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64") @staticmethod def export_greater_broadcast() -> None: node = onnx.helper.make_node( "Greater", inputs=["x", "y"], outputs=["greater"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/greater_equal.py000066400000000000000000000050161511334557700244530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Greater(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64") @staticmethod def export_greater_broadcast() -> None: node = onnx.helper.make_node( "GreaterOrEqual", inputs=["x", "y"], outputs=["greater_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.greater_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/gridsample.py000066400000000000000000000464051511334557700237710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class GridSample(Base): @staticmethod def export_gridsample() -> None: node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", padding_mode="zeros", align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] X = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ] ] ], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.6000, -1.0000], [-0.2000, -1.0000], [0.2000, -1.0000], [0.6000, -1.0000], [1.0000, -1.0000], ], [ [-1.0000, -0.6000], [-0.6000, -0.6000], [-0.2000, -0.6000], [0.2000, -0.6000], [0.6000, -0.6000], [1.0000, -0.6000], ], [ [-1.0000, -0.2000], [-0.6000, -0.2000], [-0.2000, -0.2000], [0.2000, -0.2000], [0.6000, -0.2000], [1.0000, -0.2000], ], [ [-1.0000, 0.2000], [-0.6000, 0.2000], [-0.2000, 0.2000], [0.2000, 0.2000], [0.6000, 0.2000], [1.0000, 0.2000], ], [ [-1.0000, 0.6000], [-0.6000, 0.6000], [-0.2000, 0.6000], [0.2000, 0.6000], [0.6000, 0.6000], [1.0000, 0.6000], ], [ [-1.0000, 1.0000], [-0.6000, 1.0000], [-0.2000, 1.0000], [0.2000, 1.0000], [0.6000, 1.0000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] Y = np.array( [ [ [ [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500], [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000], [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000], [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000], [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000], [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500], ] ] ], dtype=np.float32, ) expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample") @staticmethod def export_gridsample_paddingmode() -> None: # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-10.0000, -10.0000], [-5.0000, -5.0000], [-0.2000, -0.2000], [10.0000, 10.0000], ], [ [10.0000, 10.0000], [-0.2000, -0.2000], [5.0000, 5.0000], [10.0000, 10.0000], ], ] ], dtype=np.float32, ) # setting padding_mode = 'zeros' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="zeros", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_zeros = np.array( [[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_zeros], name="test_gridsample_zeros_padding", ) # setting padding_mode = 'border' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="border", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_border = np.array( [[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_border], name="test_gridsample_border_padding", ) # setting padding_mode = 'reflection' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="reflection", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_reflection = np.array( [[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_reflection], name="test_gridsample_reflection_padding", ) @staticmethod def export_gridsample_mode_aligncorners() -> None: # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.5000, -0.5000], [-0.2000, -0.2000], [0.0000, 0.0000], ], [ [0.0000, 0.0000], [-0.2000, -0.2000], [0.5000, 0.5000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # setting mode = 'bilinear', default align_corners = 0 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear", ) # setting mode = 'bilinear', align_corners = 1 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_align_corners = np.array( [[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_align_corners], name="test_gridsample_aligncorners_true", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest" ) # setting mode = 'bicubic' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic" ) # ============================================================================ # Additional tests # The reference output tensors were generated using PyTorch 2.0. Grid = np.array( [ [ [[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]], [[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_0_additional_1", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_1_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_1_additional_1", ) # These two new bicubic tests produces slightly higher error ~5e-5 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [-0.173250, 0.284265, 1.923106, 2.568000], [5.170375, 2.284414, 4.744844, 1.046875], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [0.304001, 1.128750, 2.266270, 3.144844], [4.531500, 2.455360, 4.599819, 4.000000], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_1_additional_1", ) @staticmethod def export_volumeetric_gridsample_mode_aligncorners() -> None: X = np.array( [ [ [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], [[9.0, 10.0], [11.0, 12.0]], ] ] ], dtype=np.float32, ) Grid = np.array( [ [ [ [[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]], [[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]], [[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]], [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]], [[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]], [[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]], [[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]], ], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [0.1250, 3.4000], [2.0000, 0.4500], [4.7000, 10.9000], [6.5000, 3.0000], ], [ [6.5000, 1.7500], [4.7000, 3.3000], [11.0000, 2.5200], [1.5000, 5.4900], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [1.0000, 6.7000], [3.7500, 2.4000], [5.4000, 9.3000], [6.5000, 6.0000], ], [ [6.5000, 7.0000], [5.4000, 6.6000], [9.2500, 8.4000], [12.0000, 6.1000], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_1", ) """ For someone who want to test by script. Comment it cause github ONNX CI do not have the torch python package. @staticmethod def export_gridsample_torch(): # type: () -> None node = onnx.helper.make_node( 'GridSample', inputs=['X', 'Grid'], outputs=['Y'], mode='bilinear', padding_mode='zeros', align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] import torch X = torch.arange(3 * 3).view(1, 1, 3, 3).float() d = torch.linspace(-1, 1, 6) meshx, meshy = torch.meshgrid((d, d)) grid = torch.stack((meshy, meshx), 2) Grid = grid.unsqueeze(0) Y = torch.nn.functional.grid_sample(X, Grid, mode='bilinear', padding_mode='zeros', align_corners=False) expect(node, inputs=[X.numpy(), Grid.numpy()], outputs=[Y.numpy()], name='test_gridsample_torch') """ onnx-onnx-bca0315/onnx/backend/test/case/node/groupnormalization.py000066400000000000000000000047021511334557700255770ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # Group normalization's reference implementation def _group_normalization(x, num_groups, scale, bias, epsilon=1e-5): # Assume channel is first dim assert x.shape[1] % num_groups == 0 group_size = x.shape[1] // num_groups # Reshape to [N, group_size, C/group_size, H, W, ...] new_shape = [x.shape[0], num_groups, group_size, *list(x.shape[2:])] x_reshaped = x.reshape(new_shape) axes = tuple(range(2, len(new_shape))) mean = np.mean(x_reshaped, axis=axes, keepdims=True) var = np.var(x_reshaped, axis=axes, keepdims=True) x_normalized = ((x_reshaped - mean) / np.sqrt(var + epsilon)).reshape(x.shape) dim_ones = (1,) * (len(x.shape) - 2) scale = scale.reshape(-1, *dim_ones) bias = bias.reshape(-1, *dim_ones) return scale * x_normalized + bias class GroupNormalization(Base): @staticmethod def export() -> None: c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) y = _group_normalization(x, num_groups, scale, bias).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_example", ) @staticmethod def export_epsilon() -> None: c = 4 num_groups = 2 x = np.random.randn(3, c, 2, 2).astype(np.float32) scale = np.random.randn(c).astype(np.float32) bias = np.random.randn(c).astype(np.float32) epsilon = 1e-2 y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "GroupNormalization", inputs=["x", "scale", "bias"], outputs=["y"], epsilon=epsilon, num_groups=num_groups, ) expect( node, inputs=[x, scale, bias], outputs=[y], name="test_group_normalization_epsilon", ) onnx-onnx-bca0315/onnx/backend/test/case/node/gru.py000066400000000000000000000172571511334557700224420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class GRUHelper: def __init__(self, **params: Any) -> None: # GRU Input Names X = "X" W = "W" R = "R" B = "B" H_0 = "initial_h" LBR = "linear_before_reset" LAYOUT = "layout" number_of_gates = 3 required_inputs = [X, W, R] for i in required_inputs: assert i in params, f"Missing Required Input: {i}" self.num_directions = params[W].shape[0] if self.num_directions == 1: for k, v in params.items(): if k != X: params[k] = np.squeeze(v, axis=0) hidden_size = params[R].shape[-1] batch_size = params[X].shape[1] layout = params.get(LAYOUT, 0) x = params[X] x = x if layout == 0 else np.swapaxes(x, 0, 1) b = ( params[B] if B in params else np.zeros(2 * number_of_gates * hidden_size) ) h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size)) lbr = params.get(LBR, 0) self.X = x self.W = params[W] self.R = params[R] self.B = b self.H_0 = h_0 self.LBR = lbr self.LAYOUT = layout else: raise NotImplementedError() def f(self, x: np.ndarray) -> np.ndarray: return 1 / (1 + np.exp(-x)) def g(self, x: np.ndarray) -> np.ndarray: return np.tanh(x) def step(self) -> tuple[np.ndarray, np.ndarray]: seq_length = self.X.shape[0] hidden_size = self.H_0.shape[-1] batch_size = self.X.shape[1] Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size]) h_list = [] [w_z, w_r, w_h] = np.split(self.W, 3) [r_z, r_r, r_h] = np.split(self.R, 3) [w_bz, w_br, w_bh, r_bz, r_br, r_bh] = np.split(self.B, 6) gates_w = np.transpose(np.concatenate((w_z, w_r))) gates_r = np.transpose(np.concatenate((r_z, r_r))) gates_b = np.add(np.concatenate((w_bz, w_br)), np.concatenate((r_bz, r_br))) H_t = self.H_0 for x in np.split(self.X, self.X.shape[0], axis=0): gates = np.dot(x, gates_w) + np.dot(H_t, gates_r) + gates_b z, r = np.split(gates, 2, -1) z = self.f(z) r = self.f(r) h_default = self.g( np.dot(x, np.transpose(w_h)) + np.dot(r * H_t, np.transpose(r_h)) + w_bh + r_bh ) h_linear = self.g( np.dot(x, np.transpose(w_h)) + r * (np.dot(H_t, np.transpose(r_h)) + r_bh) + w_bh ) h = h_linear if self.LBR else h_default H = (1 - z) * h + z * H_t h_list.append(H) H_t = H concatenated = np.concatenate(h_list) if self.num_directions == 1: Y[:, 0, :, :] = concatenated if self.LAYOUT == 0: Y_h = Y[-1] else: Y = np.transpose(Y, [2, 0, 1, 3]) Y_h = Y[:, :, -1, :] return Y, Y_h class GRU(Base): @staticmethod def export_defaults() -> None: input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 5 weight_scale = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R) _, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_gru_defaults", ) @staticmethod def export_initial_bias() -> None: input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 3 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_with_initial_bias", ) @staticmethod def export_seq_length() -> None: input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 number_of_gates = 3 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype( np.float32 ) R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype( np.float32 ) # Adding custom bias W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) gru = GRUHelper(X=input, W=W, R=R, B=B) _, Y_h = gru.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_gru_seq_length", ) @staticmethod def export_batchwise() -> None: input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 6 number_of_gates = 3 weight_scale = 0.2 layout = 1 node = onnx.helper.make_node( "GRU", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) gru = GRUHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = gru.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_gru_batchwise", ) onnx-onnx-bca0315/onnx/backend/test/case/node/hammingwindow.py000066400000000000000000000024101511334557700244760ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class HammingWindow(Base): @staticmethod def export() -> None: # Test periodic window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow", ) # Test symmetric window node = onnx.helper.make_node( "HammingWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 25 / 46 a1 = 1 - a0 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hammingwindow_symmetric", ) onnx-onnx-bca0315/onnx/backend/test/case/node/hannwindow.py000066400000000000000000000023061511334557700240060ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class HannWindow(Base): @staticmethod def export() -> None: # Test periodic window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow" ) # Test symmetric window node = onnx.helper.make_node( "HannWindow", inputs=["x"], outputs=["y"], periodic=0 ) size = np.int32(10) a0 = 0.5 a1 = 0.5 y = a0 - a1 * np.cos( 2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1) ) expect( node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow_symmetric", ) onnx-onnx-bca0315/onnx/backend/test/case/node/hardmax.py000066400000000000000000000051431511334557700232600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def hardmax(x: np.ndarray, axis: int = -1) -> np.ndarray: x_argmax = np.argmax(x, axis=axis) y = np.zeros_like(x) np.put_along_axis(y, np.expand_dims(x_argmax, axis=axis), 1, axis=axis) return y class Hardmax(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype( np.float32 ) # expect result: # [[1. 0. 0. 0.] # [0. 1. 0. 0.] # [0. 0. 1. 0.] # [0. 0. 0. 1.]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_example") # For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output x = np.array([[3, 3, 3, 1]]).astype(np.float32) # expect result: # [[1, 0, 0, 0]] y = hardmax(x) expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot") @staticmethod def export_hardmax_axis() -> None: x = np.random.randn(3, 4, 5).astype(np.float32) node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=0, ) y = hardmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=1, ) y = hardmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=2, ) y = hardmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2") node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], axis=-1, ) y = hardmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Hardmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis") onnx-onnx-bca0315/onnx/backend/test/case/node/hardsigmoid.py000066400000000000000000000023501511334557700241230ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class HardSigmoid(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6 ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.] expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * 0.5 + 0.6, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid") @staticmethod def export_hardsigmoid_default() -> None: default_alpha = 0.2 default_beta = 0.5 node = onnx.helper.make_node( "HardSigmoid", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x * default_alpha + default_beta, 0, 1) expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default") onnx-onnx-bca0315/onnx/backend/test/case/node/hardswish.py000066400000000000000000000013361511334557700236300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def hardswish(x: np.ndarray) -> np.ndarray: alfa = float(1 / 6) beta = 0.5 return x * np.maximum(0, np.minimum(1, alfa * x + beta)) class HardSwish(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "HardSwish", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = hardswish(x) expect(node, inputs=[x], outputs=[y], name="test_hardswish") onnx-onnx-bca0315/onnx/backend/test/case/node/identity.py000066400000000000000000000045011511334557700234620ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Identity(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) expect(node, inputs=[data], outputs=[data], name="test_identity") @staticmethod def export_sequence() -> None: node = onnx.helper.make_node( "Identity", inputs=["x"], outputs=["y"], ) data = [ np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ), np.array( [ [ [ [2, 3], [1, 5], ] ] ], dtype=np.float32, ), ] expect(node, inputs=[data], outputs=[data], name="test_identity_sequence") @staticmethod def export_identity_opt() -> None: ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) identity_node = onnx.helper.make_node( "Identity", inputs=["opt_in"], outputs=["opt_out"] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] expect( identity_node, inputs=[x], outputs=[x], name="test_identity_opt", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[opt_in_tp], output_type_protos=[opt_in_tp], ) onnx-onnx-bca0315/onnx/backend/test/case/node/if.py000066400000000000000000000145131511334557700222330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def compute_if_outputs(x, cond): if cond: return [] return x class If(Base): @staticmethod def export_if() -> None: # Given a bool scalar input cond. # return constant tensor x if cond is True, otherwise return constant tensor y. then_out = onnx.helper.make_tensor_value_info( "then_out", onnx.TensorProto.FLOAT, [5] ) else_out = onnx.helper.make_tensor_value_info( "else_out", onnx.TensorProto.FLOAT, [5] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([5, 4, 3, 2, 1]).astype(np.float32) then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["then_out"], value=onnx.numpy_helper.from_array(x), ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["else_out"], value=onnx.numpy_helper.from_array(y), ) then_body = onnx.helper.make_graph( [then_const_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if", opset_imports=[onnx.helper.make_opsetid("", 11)], ) @staticmethod def export_if_seq() -> None: # Given a bool scalar input cond. # return constant sequence x if cond is True, otherwise return constant sequence y. then_out = onnx.helper.make_tensor_sequence_value_info( "then_out", onnx.TensorProto.FLOAT, shape=[5] ) else_out = onnx.helper.make_tensor_sequence_value_info( "else_out", onnx.TensorProto.FLOAT, shape=[5] ) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)] then_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) then_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["then_out"] ) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["y"], value=onnx.numpy_helper.from_array(y[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["y"], outputs=["else_out"] ) then_body = onnx.helper.make_graph( [then_const_node, then_seq_node], "then_body", [], [then_out] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node], "else_body", [], [else_out] ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["res"], then_branch=then_body, else_branch=else_body, ) cond = np.array(1).astype(bool) res = x if cond else y expect( if_node, inputs=[cond], outputs=[res], name="test_if_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], ) @staticmethod def export_if_optional() -> None: # Given a bool scalar input cond, return an empty optional sequence of # tensor if True, return an optional sequence with value x # (the input optional sequence) otherwise. ten_in_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) then_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp) then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp) then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp) else_out_tensor_tp = onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, shape=[5] ) else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp) else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp) else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp) x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)] cond = np.array(0).astype(bool) res = compute_if_outputs(x, cond) opt_empty_in = onnx.helper.make_node( "Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp ) then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out]) else_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.numpy_helper.from_array(x[0]), ) else_seq_node = onnx.helper.make_node( "SequenceConstruct", inputs=["x"], outputs=["else_seq"] ) else_optional_seq_node = onnx.helper.make_node( "Optional", inputs=["else_seq"], outputs=["else_opt"] ) else_body = onnx.helper.make_graph( [else_const_node, else_seq_node, else_optional_seq_node], "else_body", [], [else_out], ) if_node = onnx.helper.make_node( "If", inputs=["cond"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) expect( if_node, inputs=[cond], outputs=[res], name="test_if_opt", output_type_protos=[else_out_opt_tp], opset_imports=[onnx.helper.make_opsetid("", 16)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/image_decoder.py000066400000000000000000000163251511334557700244070ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import io import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import _image_decoder_data, expect def generate_checkerboard(width: int, height: int, square_size: int) -> np.ndarray: # Create an empty RGB image image = np.zeros((height, width, 3), dtype=np.uint8) # Calculate the number of squares in each dimension num_squares_x = width // square_size num_squares_y = height // square_size # Generate a random color for each square colors = np.random.randint( 0, 256, size=(num_squares_y, num_squares_x, 3), dtype=np.uint8 ) # Iterate over each square for i in range(num_squares_y): for j in range(num_squares_x): # Calculate the position of the current square x = j * square_size y = i * square_size # Get the color for the current square color = colors[i, j] # Fill the square with the corresponding color image[y : y + square_size, x : x + square_size, :] = color return image def _generate_test_data( format_: str, frozen_data: _image_decoder_data.ImageDecoderData, pixel_format: str = "RGB", height: int = 32, width: int = 32, tile_sz: int = 5, ) -> tuple[np.ndarray, np.ndarray]: try: import PIL.Image # noqa: PLC0415 except ImportError: # Since pillow is not installed to generate test data for the ImageDecoder operator # directly use the frozen data from _image_decoder_data.py. return frozen_data.data, frozen_data.output np.random.seed(12345) image = generate_checkerboard(height, width, tile_sz) image_pil = PIL.Image.fromarray(image) with io.BytesIO() as f: image_pil.save(f, format=format_) data = f.getvalue() data_array = np.frombuffer(data, dtype=np.uint8) if pixel_format == "BGR": output_pil = PIL.Image.open(io.BytesIO(data)) output = np.array(output_pil)[:, :, ::-1] elif pixel_format == "RGB": output_pil = PIL.Image.open(io.BytesIO(data)) output = np.array(output_pil) elif pixel_format == "Grayscale": output_pil = PIL.Image.open(io.BytesIO(data)).convert("L") output = np.array(output_pil)[:, :, np.newaxis] else: raise ValueError(f"Unsupported pixel format: {pixel_format}") return data_array, output class ImageDecoder(Base): @staticmethod def export_image_decoder_decode_jpeg_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_rgb", ) @staticmethod def export_image_decoder_decode_jpeg_grayscale() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="Grayscale", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_grayscale, "Grayscale" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_grayscale", ) @staticmethod def export_image_decoder_decode_jpeg_bgr() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="BGR", ) data, output = _generate_test_data( "jpeg", _image_decoder_data.image_decoder_decode_jpeg_bgr, "BGR" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg_bgr", ) @staticmethod def export_image_decoder_decode_jpeg2k_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "jpeg2000", _image_decoder_data.image_decoder_decode_jpeg2k_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_jpeg2k_rgb", ) @staticmethod def export_image_decoder_decode_bmp_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "bmp", _image_decoder_data.image_decoder_decode_bmp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_bmp_rgb", ) @staticmethod def export_image_decoder_decode_png_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "png", _image_decoder_data.image_decoder_decode_png_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_png_rgb", ) @staticmethod def export_image_decoder_decode_tiff_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "tiff", _image_decoder_data.image_decoder_decode_tiff_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_tiff_rgb", ) @staticmethod def export_image_decoder_decode_webp_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "webp", _image_decoder_data.image_decoder_decode_webp_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_webp_rgb", ) @staticmethod def export_image_decoder_decode_pnm_rgb() -> None: node = onnx.helper.make_node( "ImageDecoder", inputs=["data"], outputs=["output"], pixel_format="RGB", ) data, output = _generate_test_data( "ppm", _image_decoder_data.image_decoder_decode_pnm_rgb, "RGB" ) expect( node, inputs=[data], outputs=[output], name="test_image_decoder_decode_pnm_rgb", ) onnx-onnx-bca0315/onnx/backend/test/case/node/instancenorm.py000066400000000000000000000036311511334557700243340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class InstanceNormalization(Base): @staticmethod def export() -> None: def _instancenorm_test_mode(x, s, bias, epsilon=1e-5): # type: ignore dims_x = len(x.shape) axis = tuple(range(2, dims_x)) mean = np.mean(x, axis=axis, keepdims=True) var = np.var(x, axis=axis, keepdims=True) dim_ones = (1,) * (dims_x - 2) s = s.reshape(-1, *dim_ones) bias = bias.reshape(-1, *dim_ones) return s * (x - mean) / np.sqrt(var + epsilon) + bias # input size: (1, 2, 1, 3) x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32) s = np.array([1.0, 1.5]).astype(np.float32) bias = np.array([0, 1]).astype(np.float32) y = _instancenorm_test_mode(x, s, bias).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], ) # output size: (1, 2, 1, 3) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example") # input size: (2, 3, 4, 5) x = np.random.randn(2, 3, 4, 5).astype(np.float32) s = np.random.randn(3).astype(np.float32) bias = np.random.randn(3).astype(np.float32) epsilon = 1e-2 y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32) node = onnx.helper.make_node( "InstanceNormalization", inputs=["x", "s", "bias"], outputs=["y"], epsilon=epsilon, ) # output size: (2, 3, 4, 5) expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon") onnx-onnx-bca0315/onnx/backend/test/case/node/isinf.py000066400000000000000000000032731511334557700227460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class IsInf(Base): @staticmethod def export_infinity() -> None: node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf") @staticmethod def export_positive_infinity_only() -> None: node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_negative=0 ) x = np.array([-1.7, np.nan, np.inf, 3.6, -np.inf, np.inf], dtype=np.float32) y = np.isposinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_positive") @staticmethod def export_negative_infinity_only() -> None: node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], detect_positive=0 ) x = np.array([-1.7, np.nan, np.inf, -3.6, -np.inf, np.inf], dtype=np.float32) y = np.isneginf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_negative") @staticmethod def export_infinity_float16() -> None: node = onnx.helper.make_node( "IsInf", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isinf(x) expect(node, inputs=[x], outputs=[y], name="test_isinf_float16") onnx-onnx-bca0315/onnx/backend/test/case/node/isnan.py000066400000000000000000000017021511334557700227410ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class IsNaN(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan") @staticmethod def export_float16() -> None: node = onnx.helper.make_node( "IsNaN", inputs=["x"], outputs=["y"], ) x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16) y = np.isnan(x) expect(node, inputs=[x], outputs=[y], name="test_isnan_float16") onnx-onnx-bca0315/onnx/backend/test/case/node/layernormalization.py000066400000000000000000000142551511334557700255630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # Layer normalization's reference implementation def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5): X_shape = X.shape X_rank = len(X_shape) if axis < 0: # If axis = -1 and rank of X is 4, # the axis is changed to -1 + 4 = 3, # which means the last axis. axis = axis + X_rank unsqueezed_rank = X_rank - axis reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank # Parameter used to convert N-D tensor layer # normalization to equivalent 2-D matrix operations. row_number = 1 col_number = 1 for i in range(X_rank): if i < axis: row_number *= X_shape[i] else: col_number *= X_shape[i] # After reshaping input tensor X into a matrix, # layer normalization is equivalent to conducting # standardization on each column vector (s.t. each # column has zero mean and unit variance). x_mat = np.reshape(X, (row_number, col_number)) # This computes mean for every x_mat's column. x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number x_diff = x_mat - x_mean x_squared_diff = x_diff * x_diff # This computes variance for every x_mat's column. variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number variance_eps = variance + epsilon std_dev = np.sqrt(variance_eps) inv_std_dev = np.reciprocal(std_dev) # Standardization step. y_mat is zero-mean and unit-variance. y_mat = x_diff * inv_std_dev # Apply affine transform on normalization outcome. # W is linear coefficient while B is bias. Y = np.reshape(y_mat, X_shape) * W + B # Matrix-level operations' outputs should be reshaped # to compensate the initial tensor-to-matrix reshape. X_mean = np.reshape(x_mean, reduction_shape) X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape) return Y, X_mean, X_inv_std_dev def calculate_normalized_shape(X_shape, axis): X_rank = len(X_shape) if axis < 0: axis = axis + X_rank return X_shape[axis:] class LayerNormalization(Base): @staticmethod def export() -> None: X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_4d_axis_negative_{-axis}" else: name = f"test_layer_normalization_4d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) @staticmethod def export_default_axis() -> None: X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in LayerNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y, mean, inv_std_dev = _layer_normalization(X, W, B) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], ) expect( node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name="test_layer_normalization_default_axis", ) @staticmethod def export2d() -> None: X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, ) if axis < 0: name = f"test_layer_normalization_2d_axis_negative_{-axis}" else: name = f"test_layer_normalization_2d_axis{axis}" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) @staticmethod def export3d_epsilon() -> None: epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) B = np.random.randn(*normalized_shape).astype(np.float32) Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon) node = onnx.helper.make_node( "LayerNormalization", inputs=["X", "W", "B"], outputs=["Y", "Mean", "InvStdDev"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_layer_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) onnx-onnx-bca0315/onnx/backend/test/case/node/leakyrelu.py000066400000000000000000000023741511334557700236340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class LeakyRelu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], alpha=0.1 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-0.1, 0., 1.] y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1 expect(node, inputs=[x], outputs=[y], name="test_leakyrelu") @staticmethod def export_leakyrelu_default() -> None: default_alpha = 0.01 node = onnx.helper.make_node( "LeakyRelu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default") onnx-onnx-bca0315/onnx/backend/test/case/node/less.py000066400000000000000000000045221511334557700226020ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Less(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64") @staticmethod def export_less_broadcast() -> None: node = onnx.helper.make_node( "Less", inputs=["x", "y"], outputs=["less"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/less_equal.py000066400000000000000000000047141511334557700237740ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Less(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal") x = np.random.randn(3, 4, 5).astype(np.int8) y = np.random.randn(3, 4, 5).astype(np.int8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int8") x = np.random.randn(3, 4, 5).astype(np.int16) y = np.random.randn(3, 4, 5).astype(np.int16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint8") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint16") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint32") x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64") @staticmethod def export_less_broadcast() -> None: node = onnx.helper.make_node( "LessOrEqual", inputs=["x", "y"], outputs=["less_equal"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = np.less_equal(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/log.py000066400000000000000000000013621511334557700224140ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Log(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Log", inputs=["x"], outputs=["y"], ) x = np.array([1, 10]).astype(np.float32) y = np.log(x) # expected output [0., 2.30258512] expect(node, inputs=[x], outputs=[y], name="test_log_example") x = np.exp(np.random.randn(3, 4, 5).astype(np.float32)) y = np.log(x) expect(node, inputs=[x], outputs=[y], name="test_log") onnx-onnx-bca0315/onnx/backend/test/case/node/logsoftmax.py000066400000000000000000000053251511334557700240210ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def logsoftmax(x: np.ndarray, axis: int = -1) -> np.ndarray: x_max = np.max(x, axis=axis, keepdims=True) tmp = np.exp(x - x_max) s = np.sum(tmp, axis=axis, keepdims=True) return (x - x_max) - np.log(s) class LogSoftmax(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output # [[-2.4076061 -1.407606 -0.407606 ]] y = logsoftmax(x) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1") @staticmethod def export_logsoftmax_axis() -> None: x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[-3.4401896 -2.4401896 -1.4401896 -0.44018966] # [-3.4401896 -2.4401896 -1.4401896 -0.44018966]] y = logsoftmax(x) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=0, ) y = logsoftmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=1, ) y = logsoftmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=2, ) y = logsoftmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2") node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], axis=-1, ) y = logsoftmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "LogSoftmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis") onnx-onnx-bca0315/onnx/backend/test/case/node/loop.py000066400000000000000000000346131511334557700226110ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def compute_loop_outputs(x, seq, trip_count): for i in range(trip_count): if seq is None: seq = [] seq += [x[: int(i + 1)]] return seq class Loop(Base): @staticmethod def export_loop_11() -> None: # Given a tensor x of values [x1, ..., xN], and initial tensor y # sum up its elements using a scan # returning the final state (y+x1+x2+...+xN) as well the scan_output # [y+x1, y+x1+x2, ..., y+x1+x2+...+xN] y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1]) y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1]) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [1] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) y = np.array([-2]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) i_add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) start_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) y_add_node = onnx.helper.make_node( "Add", inputs=["y_in", "slice_out"], outputs=["y_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) scan_identity_node = onnx.helper.make_node( "Identity", inputs=["y_out"], outputs=["scan_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, i_add_node, start_unsqueeze_node, end_unsqueeze_node, slice_node, y_add_node, scan_identity_node, ], "loop_body", [iter_count, cond_in, y_in], [cond_out, y_out, scan_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "y"], outputs=["res_y", "res_scan"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) res_y = np.array([13]).astype(np.float32) cond = np.array(1).astype(bool) res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1)) expect( node, inputs=[trip_count, cond, y], outputs=[res_y, res_scan], name="test_loop11", opset_imports=[onnx.helper.make_opsetid("", 11)], ) @staticmethod def export_loop_13() -> None: # Given a tensor x of values [x1, ..., xN], # Return a sequence of tensors of # [[x1], [x1, x2], ..., [x1, ..., xN]] seq_in = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, None ) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, None ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, seq_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "seq_empty"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) seq_empty: list[Any] = [] seq_res = [x[: int(i)] for i in x] cond = np.array(1).astype(bool) expect( node, inputs=[trip_count, cond, seq_empty], outputs=[seq_res], name="test_loop13_seq", opset_imports=[onnx.helper.make_opsetid("", 13)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) ), ], ) @staticmethod def export_loop_16_none() -> None: # Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0], # Return a concatenated sequence of tensors of # [x0, [x1], [x1, x2], ..., [x1, ..., xN]] ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, []) seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp) opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp) opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp) seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_out", onnx.TensorProto.FLOAT, [] ) cond_in = onnx.helper.make_tensor_value_info( "cond_in", onnx.TensorProto.BOOL, [] ) cond_out = onnx.helper.make_tensor_value_info( "cond_out", onnx.TensorProto.BOOL, [] ) iter_count = onnx.helper.make_tensor_value_info( "iter_count", onnx.TensorProto.INT64, [] ) x0 = np.array(0).astype(np.float32) x = np.array([1, 2, 3, 4, 5]).astype(np.float32) optional_has_elem_node = onnx.helper.make_node( "OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"] ) optional_is_none = onnx.helper.make_node( "Not", inputs=["optional_has_elem"], outputs=["optional_is_none"] ) optional_get_elem = onnx.helper.make_node( "OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"] ) constant_in = onnx.helper.make_node( "Constant", inputs=[], outputs=["constant_in"], value=onnx.helper.make_tensor( name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0] ), ) seq_const_in = onnx.helper.make_node( "SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"] ) then_seq_out = onnx.helper.make_tensor_sequence_value_info( "init_seq_in", onnx.TensorProto.FLOAT, [] ) then_body = onnx.helper.make_graph( [constant_in, seq_const_in], "then_body", [], [then_seq_out] ) else_seq_out = onnx.helper.make_tensor_sequence_value_info( "seq_in", onnx.TensorProto.FLOAT, [] ) else_body = onnx.helper.make_graph( [optional_get_elem], "else_body", [], [else_seq_out] ) if_node = onnx.helper.make_node( "If", inputs=["optional_is_none"], outputs=["sequence"], then_branch=then_body, else_branch=else_body, ) x_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["x"], value=onnx.helper.make_tensor( name="const_tensor_x", data_type=onnx.TensorProto.FLOAT, dims=x.shape, vals=x.flatten().astype(float), ), ) one_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["one"], value=onnx.helper.make_tensor( name="const_tensor_one", data_type=onnx.TensorProto.INT64, dims=(), vals=[1], ), ) zero_const_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["slice_start"], value=onnx.helper.make_tensor( name="const_tensor_zero", data_type=onnx.TensorProto.INT64, dims=(1,), vals=[0], ), ) axes_node = onnx.helper.make_node( "Constant", inputs=[], outputs=["axes"], value=onnx.helper.make_tensor( name="const_tensor_axes", data_type=onnx.TensorProto.INT64, dims=(), vals=[0], ), ) add_node = onnx.helper.make_node( "Add", inputs=["iter_count", "one"], outputs=["end"] ) end_unsqueeze_node = onnx.helper.make_node( "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"] ) slice_node = onnx.helper.make_node( "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"] ) insert_node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"] ) identity_node = onnx.helper.make_node( "Identity", inputs=["cond_in"], outputs=["cond_out"] ) loop_body = onnx.helper.make_graph( [ identity_node, optional_has_elem_node, optional_is_none, if_node, x_const_node, one_const_node, zero_const_node, add_node, axes_node, end_unsqueeze_node, slice_node, insert_node, ], "loop_body", [iter_count, cond_in, opt_in], [cond_out, seq_out], ) node = onnx.helper.make_node( "Loop", inputs=["trip_count", "cond", "opt_seq"], outputs=["seq_res"], body=loop_body, ) trip_count = np.array(5).astype(np.int64) cond = np.array(1).astype(bool) seq_res = compute_loop_outputs(x, [x0], trip_count) opt_seq_in: list[Any] = [x0] expect( node, inputs=[trip_count, cond, opt_seq_in], outputs=[seq_res], name="test_loop16_seq_none", opset_imports=[onnx.helper.make_opsetid("", 16)], input_type_protos=[ onnx.helper.make_tensor_type_proto( onnx.TensorProto.INT64, trip_count.shape ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape), opt_in_tp, ], ) onnx-onnx-bca0315/onnx/backend/test/case/node/lpnormalization.py000066400000000000000000000057761511334557700250720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class LpNormalization(Base): @staticmethod def export_l2normalization_axis_0() -> None: node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=2 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l2_norm_axis_0 = np.sqrt(np.sum(x**2, axis=0, keepdims=True)) y = x / l2_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_0") @staticmethod def export_l2normalization_axis_1() -> None: node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=2 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l2_norm_axis_1 = np.sqrt(np.sum(x**2, axis=1, keepdims=True)) y = x / l2_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_1") @staticmethod def export_l1normalization_axis_0() -> None: node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=1 ) x = np.array([3.0, 4.0], dtype=np.float32) l1_norm_axis_0 = np.sum(abs(x), axis=0, keepdims=True) y = x / l1_norm_axis_0 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_0") @staticmethod def export_l1normalization_axis_1() -> None: node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=1 ) x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32) l1_norm_axis_1 = np.sum(abs(x), axis=1, keepdims=True) y = x / l1_norm_axis_1 expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_1") @staticmethod def export_l1normalization_axis_last() -> None: node = onnx.helper.make_node( "LpNormalization", inputs=["x"], outputs=["y"], axis=-1, p=1 ) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) l1_norm_axis_last = np.sum(abs(x), axis=-1, keepdims=True) y = x / l1_norm_axis_last expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_last") @staticmethod def export_default() -> None: node = onnx.helper.make_node("LpNormalization", inputs=["x"], outputs=["y"]) x = np.array( [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]], dtype=np.float32, ) lp_norm_default = np.sqrt(np.sum(x**2, axis=-1, keepdims=True)) y = x / lp_norm_default expect(node, inputs=[x], outputs=[y], name="test_lpnormalization_default") onnx-onnx-bca0315/onnx/backend/test/case/node/lppool.py000066400000000000000000000214571511334557700231470ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_pool_common import ( get_output_shape_auto_pad, get_output_shape_explicit_padding, get_pad_shape, pool, ) class LpPool(Base): @staticmethod def export_lppool_1d_default() -> None: """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ p = 3 kernel_shape = [2] strides = [1] node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, p=p, ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_1d_default") @staticmethod def export_lppool_2d_default() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_default") @staticmethod def export_lppool_3d_default() -> None: """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], p=p, ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_3d_default") @staticmethod def export_lppool_2d_same_upper() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper") @staticmethod def export_lppool_2d_same_lower() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ p = 4 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=0, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower") @staticmethod def export_lppool_2d_pads() -> None: """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ p = 3 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], p=p, ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ( (0, 0), (0, 0), (extra_pads[0], extra_pads[2]), (extra_pads[1], extra_pads[3]), ), mode="constant", constant_values=0, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads_required=extra_pads, pads=pads, p=p, ) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads") @staticmethod def export_lppool_2d_strides() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3], p=p, ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_strides") @staticmethod def export_lppool_2d_dilations() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ p = 2 node = onnx.helper.make_node( "LpPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], p=p, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [14.560219778561036, 16.24807680927192], [21.633307652783937, 23.49468024894146], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_dilations") onnx-onnx-bca0315/onnx/backend/test/case/node/lrn.py000066400000000000000000000040601511334557700224240ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import math import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class LRN(Base): @staticmethod def export() -> None: alpha = 0.0002 beta = 0.5 bias = 2.0 nsize = 3 node = onnx.helper.make_node( "LRN", inputs=["x"], outputs=["y"], alpha=alpha, beta=beta, bias=bias, size=nsize, ) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn") @staticmethod def export_default() -> None: alpha = 0.0001 beta = 0.75 bias = 1.0 nsize = 3 node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3) x = np.random.randn(5, 5, 5, 5).astype(np.float32) square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32) for n, c, h, w in np.ndindex(x.shape): square_sum[n, c, h, w] = sum( x[ n, max(0, c - math.floor((nsize - 1) / 2)) : min( 5, c + math.ceil((nsize - 1) / 2) + 1 ), h, w, ] ** 2 ) y = x / ((bias + (alpha / nsize) * square_sum) ** beta) expect(node, inputs=[x], outputs=[y], name="test_lrn_default") onnx-onnx-bca0315/onnx/backend/test/case/node/lstm.py000066400000000000000000000201171511334557700226110ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class LSTMHelper: def __init__(self, **params: Any) -> None: # LSTM Input Names X = "X" W = "W" R = "R" B = "B" H_0 = "initial_h" C_0 = "initial_c" P = "P" LAYOUT = "layout" number_of_gates = 4 number_of_peepholes = 3 required_inputs = [X, W, R] for i in required_inputs: assert i in params, f"Missing Required Input: {i}" self.num_directions = params[W].shape[0] if self.num_directions == 1: for k, v in params.items(): if k != X: params[k] = np.squeeze(v, axis=0) hidden_size = params[R].shape[-1] batch_size = params[X].shape[1] layout = params.get(LAYOUT, 0) x = params[X] x = x if layout == 0 else np.swapaxes(x, 0, 1) b = ( params[B] if B in params else np.zeros(2 * number_of_gates * hidden_size, dtype=np.float32) ) p = ( params[P] if P in params else np.zeros(number_of_peepholes * hidden_size, dtype=np.float32) ) h_0 = ( params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32) ) c_0 = ( params[C_0] if C_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32) ) self.X = x self.W = params[W] self.R = params[R] self.B = b self.P = p self.H_0 = h_0 self.C_0 = c_0 self.LAYOUT = layout else: raise NotImplementedError() def f(self, x: np.ndarray) -> np.ndarray: return 1 / (1 + np.exp(-x)) def g(self, x: np.ndarray) -> np.ndarray: return np.tanh(x) def h(self, x: np.ndarray) -> np.ndarray: return np.tanh(x) def step(self) -> tuple[np.ndarray, np.ndarray]: seq_length = self.X.shape[0] hidden_size = self.H_0.shape[-1] batch_size = self.X.shape[1] Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size]) h_list = [] [p_i, p_o, p_f] = np.split(self.P, 3) H_t = self.H_0 C_t = self.C_0 for x in np.split(self.X, self.X.shape[0], axis=0): gates = ( np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(*np.split(self.B, 2)) ) i, o, f, c = np.split(gates, 4, -1) i = self.f(i + p_i * C_t) f = self.f(f + p_f * C_t) c = self.g(c) C = f * C_t + i * c o = self.f(o + p_o * C) H = o * self.h(C) h_list.append(H) H_t = H C_t = C concatenated = np.concatenate(h_list) if self.num_directions == 1: Y[:, 0, :, :] = concatenated if self.LAYOUT == 0: Y_h = Y[-1] else: Y = np.transpose(Y, [2, 0, 1, 3]) Y_h = Y[:, :, -1, :] return Y, Y_h class LSTM(Base): @staticmethod def export_defaults() -> None: input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R) _, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_lstm_defaults", ) @staticmethod def export_initial_bias() -> None: input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 4 weight_scale = 0.1 custom_bias = 0.1 number_of_gates = 4 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype( np.float32 ) R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), 1) lstm = LSTMHelper(X=input, W=W, R=R, B=B) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_initial_bias", ) @staticmethod def export_peepholes() -> None: input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype( np.float32 ) input_size = 4 hidden_size = 3 weight_scale = 0.1 number_of_gates = 4 number_of_peepholes = 3 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"], outputs=["", "Y_h"], hidden_size=hidden_size, ) # Initializing Inputs W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32) seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32) init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32) P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype( np.float32 ) lstm = LSTMHelper( X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h ) _, Y_h = lstm.step() expect( node, inputs=[input, W, R, B, seq_lens, init_h, init_c, P], outputs=[Y_h.astype(np.float32)], name="test_lstm_with_peepholes", ) @staticmethod def export_batchwise() -> None: input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 7 weight_scale = 0.3 number_of_gates = 4 layout = 1 node = onnx.helper.make_node( "LSTM", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones( (1, number_of_gates * hidden_size, input_size) ).astype(np.float32) R = weight_scale * np.ones( (1, number_of_gates * hidden_size, hidden_size) ).astype(np.float32) lstm = LSTMHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = lstm.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_lstm_batchwise", ) onnx-onnx-bca0315/onnx/backend/test/case/node/matmul.py000066400000000000000000000037701511334557700231370ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class MatMul(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "MatMul", inputs=["a", "b"], outputs=["c"], ) # 2d a = np.random.randn(3, 4).astype(np.float32) b = np.random.randn(4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d") # 3d a = np.random.randn(2, 3, 4).astype(np.float32) b = np.random.randn(2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d") # 4d a = np.random.randn(1, 2, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d") # broadcasting a = np.random.randn(3, 1, 3, 4).astype(np.float32) b = np.random.randn(1, 2, 4, 2).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_bcast") # 1d + 3d a = np.random.randn(4).astype(np.float32) b = np.random.randn(2, 4, 1).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_3d") # 3d + 1d a = np.random.randn(1, 2, 4, 3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d_1d") # 1d + 1d a = np.random.randn(3).astype(np.float32) b = np.random.randn(3).astype(np.float32) c = np.matmul(a, b) expect(node, inputs=[a, b], outputs=[c], name="test_matmul_1d_1d") onnx-onnx-bca0315/onnx/backend/test/case/node/matmulinteger.py000066400000000000000000000024631511334557700245130ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class MatMulInteger(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "MatMulInteger", inputs=["A", "B", "a_zero_point", "b_zero_point"], outputs=["Y"], ) A = np.array( [ [11, 7, 3], [10, 6, 2], [9, 5, 1], [8, 4, 0], ], dtype=np.uint8, ) a_zero_point = np.array([12], dtype=np.uint8) B = np.array( [ [1, 4], [2, 5], [3, 6], ], dtype=np.uint8, ) b_zero_point = np.array([0], dtype=np.uint8) output = np.array( [ [-38, -83], [-44, -98], [-50, -113], [-56, -128], ], dtype=np.int32, ) expect( node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output], name="test_matmulinteger", ) onnx-onnx-bca0315/onnx/backend/test/case/node/max.py000066400000000000000000000037741511334557700224310ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.backend.test.case.utils import all_numeric_dtypes class Max(Base): @staticmethod def export() -> None: data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 3]).astype(np.float32) result = np.array([3, 5, 4]).astype(np.float32) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_max_example", ) node = onnx.helper.make_node( "Max", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input") result = np.maximum(data_0, data_1) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs" ) @staticmethod def export_max_all_numeric_types() -> None: for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([3, 4, 4]).astype(op_dtype) node = onnx.helper.make_node( "Max", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_max_{np.dtype(op_dtype).name}", ) onnx-onnx-bca0315/onnx/backend/test/case/node/maxpool.py000066400000000000000000000531371511334557700233210ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_pool_common import ( get_output_shape_auto_pad, get_output_shape_explicit_padding, get_pad_shape, pool, ) class MaxPool(Base): @staticmethod def export_maxpool_2d_uint8() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.uint8) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.uint8) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8") @staticmethod def export_maxpool_2d_precomputed_pads() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads") @staticmethod def export_maxpool_with_argmax_2d_precomputed_pads() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 5, 5] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[5, 5], pads=[2, 2, 2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array( [ [ [ [13, 14, 15, 15, 15], [18, 19, 20, 20, 20], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], [23, 24, 25, 25, 25], ] ] ] ).astype(np.float32) z = np.array( [ [ [ [12, 13, 14, 14, 14], [17, 18, 19, 19, 19], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], [22, 23, 24, 24, 24], ] ] ] ).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_pads", ) @staticmethod def export_maxpool_2d_precomputed_strides() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2] ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides" ) @staticmethod def export_maxpool_with_argmax_2d_precomputed_strides() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y", "z"], kernel_shape=[2, 2], strides=[2, 2], storage_order=1, ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32) z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64) expect( node, inputs=[x], outputs=[y, z], name="test_maxpool_with_argmax_2d_precomputed_strides", ) @staticmethod def export_maxpool_2d_precomputed_same_upper() -> None: """input_shape: [1, 1, 5, 5] output_shape: [1, 1, 3, 3] pad_shape: [2, 2] -> [1, 1, 1, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER", ) x = np.array( [ [ [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], ] ] ] ).astype(np.float32) y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper" ) @staticmethod def export_maxpool_1d_default() -> None: """input_shape: [1, 3, 32] output_shape: [1, 3, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2], ) x = np.random.randn(1, 3, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2] strides = [1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default") @staticmethod def export_maxpool_2d_default() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (2, 2) strides = (1, 1) out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default") @staticmethod def export_maxpool_3d_default() -> None: """input_shape: [1, 3, 32, 32, 32] output_shape: [1, 3, 31, 31, 31] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], ) x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = [2, 2, 2] strides = [1, 1, 1] out_shape, _ = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default") @staticmethod def export_maxpool_2d_same_upper() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [0, 1, 0, 1] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_UPPER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_UPPER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape ) pad_top = pad_shape[0] // 2 pad_bottom = pad_shape[0] - pad_top pad_left = pad_shape[1] // 2 pad_right = pad_shape[1] - pad_left padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper") @staticmethod def export_maxpool_2d_same_lower() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 32, 32] pad_shape: [1, 1] -> [1, 0, 1, 0] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], auto_pad="SAME_LOWER", ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) kernel_shape = (2, 2) strides = (1, 1) out_shape = get_output_shape_auto_pad( "SAME_LOWER", x_shape[2:], kernel_shape, strides ) pad_shape = get_pad_shape( "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape ) pad_bottom = pad_shape[0] // 2 pad_top = pad_shape[0] - pad_bottom pad_right = pad_shape[1] // 2 pad_left = pad_shape[1] - pad_right padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) pads = [pad_top, pad_left, pad_bottom, pad_right] y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower") @staticmethod def export_maxpool_2d_pads() -> None: """input_shape: [1, 3, 28, 28] output_shape: [1, 3, 30, 30] pad_shape: [4, 4] -> [2, 2, 2, 2] by axis """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], pads=[2, 2, 2, 2], ) x = np.random.randn(1, 3, 28, 28).astype(np.float32) x_shape = np.shape(x) kernel_shape = (3, 3) strides = (1, 1) pad_bottom = pad_top = pad_right = pad_left = 2 pads = [pad_top, pad_left, pad_bottom, pad_right] out_shape, extra_pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = np.pad( x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode="constant", constant_values=np.nan, ) y = pool( padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads_required=extra_pads, pads=pads, ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads") @staticmethod def export_maxpool_2d_strides() -> None: """input_shape: [1, 3, 32, 32] output_shape: [1, 3, 10, 10] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3] ) x = np.random.randn(1, 3, 32, 32).astype(np.float32) x_shape = np.shape(x) pads = None kernel_shape = (5, 5) strides = (3, 3) out_shape, pads = get_output_shape_explicit_padding( pads, x_shape[2:], kernel_shape, strides ) padded = x y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX") expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides") @staticmethod def export_maxpool_2d_ceil() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True, ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil") @staticmethod def export_maxpool_2d_ceil_output_size_reduce_by_one() -> None: """input_shape: [1, 1, 2, 2] output_shape: [1, 1, 1, 1] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[1, 1], strides=[2, 2], ceil_mode=True, ) x = np.array([[[[1, 2], [3, 4]]]]).astype(np.float32) y = np.array([[[[1]]]]).astype(np.float32) expect( node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil_output_size_reduce_by_one", ) @staticmethod def export_maxpool_2d_dilations() -> None: """input_shape: [1, 1, 4, 4] output_shape: [1, 1, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[1, 1], dilations=[2, 2], ) x = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ] ).astype(np.float32) y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations") @staticmethod def export_maxpool_3d_dilations() -> None: """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=[2, 2, 2], ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) y = np.array([[[[[11, 12], [15, 16]], [[11, 12], [15, 16]]]]]).astype( np.float32 ) expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations") @staticmethod def export_maxpool_3d_dilations_use_ref_impl() -> None: """input_shape: [1, 1, 4, 4, 4] output_shape: [1, 1, 2, 2, 2] """ dilations = [2, 2, 2] kernel_shape = [2, 2, 2] strides = [1, 1, 1] ceil_mode = False node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2, 2], strides=[1, 1, 1], dilations=dilations, ) x = np.array( [ [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ], ] ] ] ).astype(np.float32) x_shape = x.shape[2:] out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = x y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl" ) @staticmethod def export_maxpool_3d_dilations_use_ref_impl_large() -> None: x_shape = (32, 32, 32) dilations = (2, 2, 2) kernel_shape = (5, 5, 5) strides = (3, 3, 3) ceil_mode = True node = onnx.helper.make_node( "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=kernel_shape, strides=strides, dilations=dilations, ceil_mode=ceil_mode, ) x = np.random.randn(1, 1, *x_shape).astype(np.float32) out_shape, pads = get_output_shape_explicit_padding( None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode ) padded = np.pad( x, ( (0, 0), (0, 0), (pads[0], pads[3]), (pads[1], pads[4]), (pads[2], pads[5]), ), mode="constant", constant_values=0, ) y = pool( padded, (1, 1, *x_shape), kernel_shape, strides, out_shape, "MAX", pads_required=pads, pads=None, dilations=dilations, ) expect( node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl_large", ) onnx-onnx-bca0315/onnx/backend/test/case/node/maxunpool.py000066400000000000000000000036421511334557700236600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class MaxUnpool(Base): @staticmethod def export_without_output_shape() -> None: node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) y = np.array( [[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]], dtype=np.float32, ) expect( node, inputs=[xT, xI], outputs=[y], name="test_maxunpool_export_without_output_shape", ) @staticmethod def export_with_output_shape() -> None: node = onnx.helper.make_node( "MaxUnpool", inputs=["xT", "xI", "output_shape"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2], ) xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32) xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64) output_shape = np.array((1, 1, 5, 5), dtype=np.int64) y = np.array( [ [ [ [0, 0, 0, 0, 0], [0, 5, 0, 6, 0], [0, 0, 0, 0, 0], [0, 7, 0, 8, 0], [0, 0, 0, 0, 0], ] ] ], dtype=np.float32, ) expect( node, inputs=[xT, xI, output_shape], outputs=[y], name="test_maxunpool_export_with_output_shape", ) onnx-onnx-bca0315/onnx/backend/test/case/node/mean.py000066400000000000000000000025271511334557700225570ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Mean(Base): @staticmethod def export() -> None: data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([2, 3, 4]).astype(np.float32) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_mean_example", ) node = onnx.helper.make_node( "Mean", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input") result = np.divide(np.add(data_0, data_1), 2.0) node = onnx.helper.make_node( "Mean", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs" ) onnx-onnx-bca0315/onnx/backend/test/case/node/meanvariancenormalization.py000066400000000000000000000032721511334557700270750ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class MeanVarianceNormalization(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "MeanVarianceNormalization", inputs=["X"], outputs=["Y"] ) input_data = np.array( [ [ [[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]], ], [ [[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]], ], [ [[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]], ], ], dtype=np.float32, ) # Calculate expected output data data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1) data_mean_squared = np.power(data_mean, 2) data_squared = np.power(input_data, 2) data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1) std = np.sqrt(data_squared_mean - data_mean_squared) expected_output = (input_data - data_mean) / (std + 1e-9) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn") onnx-onnx-bca0315/onnx/backend/test/case/node/melweightmatrix.py000066400000000000000000000071641511334557700250530ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class MelWeightMatrix(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "MelWeightMatrix", inputs=[ "num_mel_bins", "dft_length", "sample_rate", "lower_edge_hertz", "upper_edge_hertz", ], outputs=["output"], ) num_mel_bins = np.int32(8) dft_length = np.int32(16) sample_rate = np.int32(8192) lower_edge_hertz = np.float32(0) upper_edge_hertz = np.float32(8192 / 2) num_spectrogram_bins = dft_length // 2 + 1 frequency_bins = np.arange(0, num_mel_bins + 2) low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700) high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700) mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0] frequency_bins = frequency_bins * mel_step + low_frequency_mel frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1) frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate frequency_bins = frequency_bins.astype(int) output = np.zeros((num_spectrogram_bins, num_mel_bins)) output.flags.writeable = True for i in range(num_mel_bins): lower_frequency_value = frequency_bins[i] # left center_frequency_point = frequency_bins[i + 1] # center higher_frequency_point = frequency_bins[i + 2] # right low_to_center = center_frequency_point - lower_frequency_value if low_to_center == 0: output[center_frequency_point, i] = 1 else: for j in range(lower_frequency_value, center_frequency_point + 1): output[j, i] = float(j - lower_frequency_value) / float( low_to_center ) center_to_high = higher_frequency_point - center_frequency_point if center_to_high > 0: for j in range(center_frequency_point, higher_frequency_point): output[j, i] = float(higher_frequency_point - j) / float( center_to_high ) # Expected output # 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, output = output.astype(np.float32) expect( node, inputs=[ num_mel_bins, dft_length, sample_rate, lower_edge_hertz, upper_edge_hertz, ], outputs=[output], name="test_melweightmatrix", ) onnx-onnx-bca0315/onnx/backend/test/case/node/min.py000066400000000000000000000037741511334557700224270ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.backend.test.case.utils import all_numeric_dtypes class Min(Base): @staticmethod def export() -> None: data_0 = np.array([3, 2, 1]).astype(np.float32) data_1 = np.array([1, 4, 4]).astype(np.float32) data_2 = np.array([2, 5, 0]).astype(np.float32) result = np.array([1, 2, 0]).astype(np.float32) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_min_example", ) node = onnx.helper.make_node( "Min", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input") result = np.minimum(data_0, data_1) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs" ) @staticmethod def export_min_all_numeric_types() -> None: for op_dtype in all_numeric_dtypes: data_0 = np.array([3, 2, 1]).astype(op_dtype) data_1 = np.array([1, 4, 4]).astype(op_dtype) result = np.array([1, 2, 1]).astype(op_dtype) node = onnx.helper.make_node( "Min", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name=f"test_min_{np.dtype(op_dtype).name}", ) onnx-onnx-bca0315/onnx/backend/test/case/node/mish.py000066400000000000000000000012171511334557700225720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Mish(Base): @staticmethod def export() -> None: node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"]) input_data = np.linspace(-10, 10, 10000, dtype=np.float32) # Calculate expected output data expected_output = input_data * np.tanh(np.log1p(np.exp(input_data))) expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish") onnx-onnx-bca0315/onnx/backend/test/case/node/mod.py000066400000000000000000000141531511334557700224140ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Mod(Base): @staticmethod def export_mod_mixed_sign_float64() -> None: node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64) z = np.fmod(x, y) # expected output [-0.1, 0.4, 5. , 0.1, -0.4, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64") @staticmethod def export_mod_mixed_sign_float32() -> None: node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32) z = np.fmod( x, y ) # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32") @staticmethod def export_mod_mixed_sign_float16() -> None: node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16) y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16) z = np.fmod( x, y ) # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 , 3.] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16") @staticmethod def export_mod_mixed_sign_int64() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64") @staticmethod def export_mod_mixed_sign_int32() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32") @staticmethod def export_mod_mixed_sign_int16() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16") @staticmethod def export_mod_mixed_sign_int8() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8) z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8") @staticmethod def export_mod_uint8() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint8) y = np.array([2, 3, 8]).astype(np.uint8) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8") @staticmethod def export_mod_uint16() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint16) y = np.array([2, 3, 8]).astype(np.uint16) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16") @staticmethod def export_mod_uint32() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint32) y = np.array([2, 3, 8]).astype(np.uint32) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32") @staticmethod def export_mod_uint64() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.array([4, 7, 5]).astype(np.uint64) y = np.array([2, 3, 8]).astype(np.uint64) z = np.mod(x, y) # expected output [0, 1, 5] expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64") @staticmethod def export_mod_int64_fmod() -> None: node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1) x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64) y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64) z = np.fmod(x, y) # expected output [ 0, 1, 5, 0, -1, 3] expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod") @staticmethod def export_mod_broadcast() -> None: node = onnx.helper.make_node( "Mod", inputs=["x", "y"], outputs=["z"], ) x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32) y = np.array([7]).astype(np.int32) z = np.mod(x, y) # array([[[0, 1, 2, 3, 4], # [5, 6, 0, 1, 2]], # [[3, 4, 5, 6, 0], # [1, 2, 3, 4, 5]], # [[6, 0, 1, 2, 3], # [4, 5, 6, 0, 1]]], dtype=int32) expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast") onnx-onnx-bca0315/onnx/backend/test/case/node/momentum.py000066400000000000000000000122551511334557700234770ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN def apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta): # Add gradient of regularization term. g_regularized = norm_coefficient * x + g # Coefficient of gradient should be 1 at the first iteration. beta_adjusted = beta if t > 0 else 1 # Update momentum. v_new = alpha * v + beta_adjusted * g_regularized # Apply SG with momentum update rule. x_new = x - r * v_new return x_new, v_new def apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta): # Add gradient of regularization term. g_regularized = norm_coefficient * x + g # Coefficient of gradient should be 1 at the first iteration. beta_adjusted = beta if t > 0 else 1 # Update momentum. v_new = alpha * v + beta_adjusted * g_regularized # Apply Nesterov with momentum update rule. x_new = x - r * (g_regularized + alpha * v_new) return x_new, v_new class Momentum(Base): @staticmethod def export_momentum() -> None: # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.1 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) @staticmethod def export_nesterov_momentum() -> None: # Define operator attributes. norm_coefficient = 0.01 alpha = 0.95 beta = 1.0 # Create operator. node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="nesterov", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x = np.array([1.2, 2.8], dtype=np.float32) g = np.array([-0.94, -2.5], dtype=np.float32) v = np.array([1.7, 3.6], dtype=np.float32) # Compute expected outputs of Momentum. x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x, g, v], outputs=[x_new, v_new], name="test_nesterov_momentum", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) @staticmethod def export_momentum_multiple() -> None: # Define operator attributes. norm_coefficient = 0.001 alpha = 0.95 beta = 0.85 node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"], outputs=["X1_new", "X2_new", "V1_new", "V2_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="standard", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ) # Define operator inputs. r = np.array(0.1, dtype=np.float32) # scalar t = np.array(0, dtype=np.int64) # scalar x1 = np.array([1.0], dtype=np.float32) g1 = np.array([-1.0], dtype=np.float32) v1 = np.array([2.0], dtype=np.float32) x2 = np.array([1.0, 2.0], dtype=np.float32) g2 = np.array([-1.0, -3.0], dtype=np.float32) v2 = np.array([4.0, 1.0], dtype=np.float32) # Compute expected outputs of Momentum. x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta) x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta) # Check results. expect( node, inputs=[r, t, x1, x2, g1, g2, v1, v2], outputs=[x1_new, x2_new, v1_new, v2_new], name="test_momentum_multiple", opset_imports=[ onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1) ], ) onnx-onnx-bca0315/onnx/backend/test/case/node/mul.py000066400000000000000000000050171511334557700224310ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Mul(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = x * y # expected output [4., 10., 18.] expect(node, inputs=[x, y], outputs=[z], name="test_mul_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_int16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint16") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint32") x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint64") @staticmethod def export_mul_broadcast() -> None: node = onnx.helper.make_node( "Mul", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x * y expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/neg.py000066400000000000000000000013561511334557700224070ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Neg(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Neg", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.negative(x) # expected output [4., -2.], expect(node, inputs=[x], outputs=[y], name="test_neg_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.negative(x) expect(node, inputs=[x], outputs=[y], name="test_neg") onnx-onnx-bca0315/onnx/backend/test/case/node/negativeloglikelihoodloss.py000066400000000000000000000462421511334557700271120ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def compute_negative_log_likelihood_loss( input, target, weight=None, reduction="mean", ignore_index=None ): input_shape = input.shape if len(input_shape) == 1: raise RuntimeError("Unsupported shape") target_shape = target.shape N = input_shape[0] C = input_shape[1] # initialize the positional weights when required gather_weight = None if weight is not None: # setting mode='clip' to deal with ignore_index > C or < 0 cases. # when the target value is > C or < 0, it doesn't matter which value we are # taking in gather_weight, since it will be set to 0 in the following if-block # use np.int32 to make it compatible with x86 machines gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip") # set `ignore_index`'s loss weight to 0. # The loss tensor will be multiplied by this weight tensor, # so `ignore_index`'s loss value will be eliminated. if ignore_index is not None: gather_weight = np.where(target == ignore_index, 0, gather_weight).astype( dtype=np.float32 ) elif ignore_index is not None: gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32) # if input is 4-d and above, make it 3-d if len(input_shape) != 3: input = input.reshape((N, C, -1)) target = target.reshape((N, -1)) # Get a dimension from the reshaped input. # If the original input shape is [N, C, H, W], # the D here should be H * W because we reshape # [N, C, H, W] to [N, C, H * W]. D = input.shape[2] neg_gather_element_input = np.zeros((N, D), dtype=np.float32) for i in range(N): for d in range(D): if target[i][d] != ignore_index: neg_gather_element_input[i][d] = -input[i][target[i][d]][d] loss = neg_gather_element_input # if the input was 4-d or above reshape to the right shape if len(input_shape) != 3: loss = loss.reshape(target_shape) # apply the weights when required if gather_weight is not None: loss = gather_weight * loss if reduction == "mean": return loss.sum() / gather_weight.sum() if reduction == "mean": loss = np.mean(loss) elif reduction == "sum": loss = np.sum(loss) return loss class NegativeLogLikelihoodLoss(Base): @staticmethod def export_input_shape_is_NC() -> None: reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N,)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NC", ) @staticmethod def export_input_shape_is_NCd1d2() -> None: reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2", ) @staticmethod def export_input_shape_is_NCd1d2_reduction_mean() -> None: reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_mean", ) @staticmethod def export_input_shape_is_NCd1d2_reduction_sum() -> None: reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_reduction_sum", ) @staticmethod def export_input_shape_is_NCd1d2_with_weight() -> None: reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight", ) @staticmethod def export_input_shape_is_NCd1d2_with_weight_reduction_mean() -> None: reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_mean", ) @staticmethod def export_input_shape_is_NCd1d2_with_weight_reduction_sum() -> None: reduction = "sum" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum", ) @staticmethod def export_input_shape_is_NCd1d2_with_weight_reduction_sum_ii() -> None: reduction = "sum" ignore_index = np.int64(0) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(0) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii", ) @staticmethod def export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() -> None: reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2 = 3, 5, 6, 6 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64) target[0][0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii", ) @staticmethod def export_input_shape_is_NCd1() -> None: reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1", ) @staticmethod def export_input_shape_is_NCd1_weight() -> None: reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight", ) @staticmethod def export_input_shape_is_NCd1_ii() -> None: reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=None, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_ii", ) @staticmethod def export_input_shape_is_NCd1_weight_ii() -> None: reduction = "mean" ignore_index = np.int64(1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, d1 = 3, 5, 2 np.random.seed(0) input = np.random.rand(N, C, d1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64) target[0][0] = np.int64(1) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_weight_ii", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None: reduction = "mean" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_mean_weight", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None: reduction = "none" node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) target = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3d4d5_none_no_weight", ) @staticmethod def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None: reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) input = np.random.rand(N, C, dim1).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) target[0][0] = -1 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1_mean_weight_negative_ii", ) @staticmethod def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None: reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) target[0][0][0][0] = -5 negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii", ) @staticmethod def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None: reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "NegativeLogLikelihoodLoss", inputs=["input", "target", "weight"], outputs=["loss"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) input = np.random.rand(N, C).astype(np.float32) target = np.random.randint(0, high=C, size=(N)).astype(np.int64) target[0] = 10 weight = np.random.rand(C).astype(np.float32) negative_log_likelihood_loss = compute_negative_log_likelihood_loss( input, target, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[input, target, weight], outputs=[negative_log_likelihood_loss], name="test_nllloss_NCd1d2d3_sum_weight_high_ii", ) onnx-onnx-bca0315/onnx/backend/test/case/node/nonmaxsuppression.py000066400000000000000000000331051511334557700254460ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class NonMaxSuppression(Base): @staticmethod def export_nonmaxsuppression_suppress_by_IOU() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU", ) @staticmethod def export_nonmaxsuppression_suppress_by_IOU_and_scores() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.4]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_suppress_by_IOU_and_scores", ) @staticmethod def export_nonmaxsuppression_flipped_coordinates() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [1.0, 1.0, 0.0, 0.0], [0.0, 0.1, 1.0, 1.1], [0.0, 0.9, 1.0, -0.1], [0.0, 10.0, 1.0, 11.0], [1.0, 10.1, 0.0, 11.1], [1.0, 101.0, 0.0, 100.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_flipped_coordinates", ) @staticmethod def export_nonmaxsuppression_limit_output_size() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_limit_output_size", ) @staticmethod def export_nonmaxsuppression_single_box() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32) scores = np.array([[[0.9]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_single_box", ) @staticmethod def export_nonmaxsuppression_identical_boxes() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]] ).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 0]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_identical_boxes", ) @staticmethod def export_nonmaxsuppression_center_point_box_format() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], center_point_box=1, ) boxes = np.array( [ [ [0.5, 0.5, 1.0, 1.0], [0.5, 0.6, 1.0, 1.0], [0.5, 0.4, 1.0, 1.0], [0.5, 10.5, 1.0, 1.0], [0.5, 10.6, 1.0, 1.0], [0.5, 100.5, 1.0, 1.0], ] ] ).astype(np.float32) scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32) max_output_boxes_per_class = np.array([3]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_center_point_box_format", ) @staticmethod def export_nonmaxsuppression_two_classes() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ] ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_classes", ) @staticmethod def export_nonmaxsuppression_two_batches() -> None: node = onnx.helper.make_node( "NonMaxSuppression", inputs=[ "boxes", "scores", "max_output_boxes_per_class", "iou_threshold", "score_threshold", ], outputs=["selected_indices"], ) boxes = np.array( [ [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], [ [0.0, 0.0, 1.0, 1.0], [0.0, 0.1, 1.0, 1.1], [0.0, -0.1, 1.0, 0.9], [0.0, 10.0, 1.0, 11.0], [0.0, 10.1, 1.0, 11.1], [0.0, 100.0, 1.0, 101.0], ], ] ).astype(np.float32) scores = np.array( [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]] ).astype(np.float32) max_output_boxes_per_class = np.array([2]).astype(np.int64) iou_threshold = np.array([0.5]).astype(np.float32) score_threshold = np.array([0.0]).astype(np.float32) selected_indices = np.array( [[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]] ).astype(np.int64) expect( node, inputs=[ boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, ], outputs=[selected_indices], name="test_nonmaxsuppression_two_batches", ) onnx-onnx-bca0315/onnx/backend/test/case/node/nonzero.py000066400000000000000000000013231511334557700233220ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class NonZero(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "NonZero", inputs=["condition"], outputs=["result"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) result = np.array( np.nonzero(condition), dtype=np.int64 ) # expected output [[0, 1, 1], [0, 0, 1]] expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example") onnx-onnx-bca0315/onnx/backend/test/case/node/not.py000066400000000000000000000015631511334557700224360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Not(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Not", inputs=["x"], outputs=["not"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d") onnx-onnx-bca0315/onnx/backend/test/case/node/onehot.py000066400000000000000000000075741511334557700231420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def one_hot(indices, depth, axis=-1, dtype=np.float32): """Compute one hot from indices at a specific axis""" values = np.asarray(indices) rank = len(values.shape) depth_range = np.arange(depth) if axis < 0: axis += rank + 1 ls = values.shape[0:axis] rs = values.shape[axis:rank] targets = np.reshape( depth_range, (1,) * len(ls) + depth_range.shape + (1,) * len(rs) ) values = np.reshape(np.mod(values, depth), (*ls, 1, *rs)) return np.asarray(targets == values, dtype=dtype) class OneHot(Base): @staticmethod def export_without_axis() -> None: on_value = 5 off_value = 2 output_type = np.int32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"] ) indices = np.array([0, 7, 8], dtype=np.int64) depth = np.float32(12) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_without_axis", ) @staticmethod def export_with_axis() -> None: axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_axis", ) @staticmethod def export_with_negative_indices() -> None: axisValue = 1 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([0, -7, -8], dtype=np.int64) # print(y) # [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.] # [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.] # [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]] depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_negative_indices", ) @staticmethod def export_with_negative_axis() -> None: axisValue = -2 on_value = 3 off_value = 1 output_type = np.float32 node = onnx.helper.make_node( "OneHot", inputs=["indices", "depth", "values"], outputs=["y"], axis=axisValue, ) indices = np.array([[1, 9], [2, 4]], dtype=np.float32) depth = np.float32(10) values = np.array([off_value, on_value], dtype=output_type) y = one_hot(indices, depth, axis=axisValue, dtype=output_type) y = y * (on_value - off_value) + off_value expect( node, inputs=[indices, depth, values], outputs=[y], name="test_onehot_with_negative_axis", ) onnx-onnx-bca0315/onnx/backend/test/case/node/optionalgetelement.py000066400000000000000000000047621511334557700255410ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def optional_get_element_reference_implementation(optional: Any | None) -> Any: assert optional is not None return optional class OptionalHasElement(Base): @staticmethod def export_get_element_tensor() -> None: optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_tensor", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[tensor_type_proto], name="test_optional_get_element_tensor", ) @staticmethod def export_get_element_sequence() -> None: optional = [np.array([1, 2, 3, 4]).astype(np.int32)] tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[ 4, ], ) seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto) optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto) node = onnx.helper.make_node( "OptionalGetElement", inputs=["optional_input"], outputs=["output"] ) output = optional_get_element_reference_implementation(optional) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name="test_optional_get_element_optional_sequence", ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[seq_type_proto], name="test_optional_get_element_sequence", ) onnx-onnx-bca0315/onnx/backend/test/case/node/optionalhaselement.py000066400000000000000000000064651511334557700255370ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def optional_has_element_reference_implementation( optional: np.ndarray | None, ) -> np.ndarray: if optional is None: return np.array(False) return np.array(True) class OptionalHasElement(Base): @staticmethod def export() -> None: optional = np.array([1, 2, 3, 4]).astype(np.float32) tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.FLOAT, shape=[ 4, ], ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_protos in [tensor_type_proto, optional_type_proto]: node = onnx.helper.make_node( "OptionalHasElement", inputs=["optional_input"], outputs=["output"] ) output = optional_has_element_reference_implementation(optional) test_name = "test_optional_has_element_" + ( "optional_input" if input_type_protos == optional_type_proto else "tensor_input" ) expect( node, inputs=[optional], outputs=[output], input_type_protos=[optional_type_proto], name=test_name, ) @staticmethod def export_empty() -> None: optional = None tensor_type_proto = onnx.helper.make_tensor_type_proto( elem_type=onnx.TensorProto.INT32, shape=[] ) optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto) # OptionalHasElement takes a tensor or optional as input for input_type_proto in [tensor_type_proto, optional_type_proto]: input_name_options = { "empty": "optional_input", "empty_no_input_name": "", "empty_no_input": None, } for test_name_surfix, input_name in input_name_options.items(): if input_type_proto == tensor_type_proto and input_name: # the input tensor cannot be empty if input name is provided. continue node = onnx.helper.make_node( "OptionalHasElement", inputs=[] if input_name is None else [input_name], outputs=["output"], ) output = optional_has_element_reference_implementation(optional) test_name = ( "test_optional_has_element_" + test_name_surfix + ( "_optional_input" if input_type_proto == optional_type_proto else "_tensor_input" ) ) expect( node, inputs=[optional] if input_name else [], outputs=[output], input_type_protos=[input_type_proto] if input_name else [], name=test_name, ) onnx-onnx-bca0315/onnx/backend/test/case/node/or.py000066400000000000000000000046621511334557700222610ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Or(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or4d") @staticmethod def export_or_broadcast() -> None: node = onnx.helper.make_node( "Or", inputs=["x", "y"], outputs=["or"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_or(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d") onnx-onnx-bca0315/onnx/backend/test/case/node/pad.py000066400000000000000000000076031511334557700224030ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def pad_impl(data, raw_pads, mode, constant_values=0.0, axes=None): input_rank = data.ndim if axes is None: axes = list(range(input_rank)) else: axes = [axis if axis >= 0 else axis + input_rank for axis in axes] num_axes = len(axes) if num_axes * 2 != raw_pads.size: raise ValueError("The number of elements in raw_pads should be 2 * num_axes") pad_width = [] for _ in range(input_rank): pad_width += [[0, 0]] # init to zero # re-order to np.pad accepted order ((x1_begin, x1_end), (x2_begin, x2_end), ...) for i in range(num_axes): axis = axes[i] if axis < 0: axis = input_rank + axis pad_width[axis] = [raw_pads[i], raw_pads[i + num_axes]] if mode == "constant": return np.pad( data, pad_width=pad_width, mode=mode, constant_values=constant_values, ) return np.pad( data, pad_width=pad_width, mode=mode, ) class Pad(Base): @staticmethod def export_constant_pad() -> None: node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) y = pad_impl(x, pads, "constant", 1.2) expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad") @staticmethod def export_reflection_edge_and_wrap_pad() -> None: for mode in ("edge", "reflect", "wrap"): node = onnx.helper.make_node( "Pad", inputs=["x", "pads"], outputs=["y"], mode=mode ) x = np.random.randn(1, 3, 4, 5).astype(np.int32) pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] y = pad_impl(x, pads, mode) expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad") @staticmethod def export_constant_pad_axes() -> None: node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([1, 3], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [1, 3], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_axes", ) @staticmethod def export_constant_pad_negative_axes() -> None: node = onnx.helper.make_node( "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant" ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) pads = np.array([0, 3, 0, 4]).astype( np.int64 ) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...] value = np.float32(1.2) axes = np.array([-3, -1], dtype=np.int64) y = pad_impl( x, pads, "constant", 1.2, [-3, -1], ) expect( node, inputs=[x, pads, value, axes], outputs=[y], name="test_constant_pad_negative_axes", ) onnx-onnx-bca0315/onnx/backend/test/case/node/pow.py000066400000000000000000000074461511334557700224510ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def pow(x, y): # type: ignore # noqa: A001 return np.power(x, y).astype(x.dtype) class Pow(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_example") x = np.arange(60).reshape(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow") @staticmethod def export_pow_broadcast() -> None: node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array(2).astype(np.float32) z = pow(x, y) # expected output [1., 4., 9.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar") node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32) y = np.array([1, 2, 3]).astype(np.float32) # expected output [[1, 4, 27], [4, 25, 216]] z = pow(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array") @staticmethod def export_types() -> None: node = onnx.helper.make_node( "Pow", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.float32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint64) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64") x = np.array([1, 2, 3]).astype(np.float32) y = np.array([4, 5, 6]).astype(np.uint32) z = pow(x, y) # expected output [1., 32., 729.] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32") x = np.array([1, 2, 3]).astype(np.int64) y = np.array([4, 5, 6]).astype(np.int64) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64") x = np.array([1, 2, 3]).astype(np.int32) y = np.array([4, 5, 6]).astype(np.int32) z = pow(x, y) # expected output [1, 32, 729] expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32") onnx-onnx-bca0315/onnx/backend/test/case/node/prelu.py000066400000000000000000000022041511334557700227560ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class PRelu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example") @staticmethod def export_prelu_broadcast() -> None: node = onnx.helper.make_node( "PRelu", inputs=["x", "slope"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) slope = np.random.randn(5).astype(np.float32) y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast") onnx-onnx-bca0315/onnx/backend/test/case/node/qlinearconv.py000066400000000000000000000043671511334557700241640ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class QLinearConv(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "QLinearConv", inputs=[ "x", "x_scale", "x_zero_point", "w", "w_scale", "w_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) x = np.array( [ [255, 174, 162, 25, 203, 168, 58], [15, 59, 237, 95, 129, 0, 64], [56, 242, 153, 221, 168, 12, 166], [232, 178, 186, 195, 237, 162, 237], [188, 39, 124, 77, 80, 102, 43], [127, 230, 21, 83, 41, 40, 134], [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) x_scale = np.float32(0.00369204697) x_zero_point = np.uint8(132) w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1)) w_scale = np.array([0.00172794575], dtype=np.float32) w_zero_point = np.array([255], dtype=np.uint8) y_scale = np.float32(0.00162681262) y_zero_point = np.uint8(123) output = np.array( [ [0, 81, 93, 230, 52, 87, 197], [240, 196, 18, 160, 126, 255, 191], [199, 13, 102, 34, 87, 243, 89], [23, 77, 69, 60, 18, 93, 18], [67, 216, 131, 178, 175, 153, 212], [128, 25, 234, 172, 214, 215, 121], [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8, ).reshape((1, 1, 7, 7)) expect( node, inputs=[ x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, ], outputs=[output], name="test_qlinearconv", ) onnx-onnx-bca0315/onnx/backend/test/case/node/qlinearmatmul.py000066400000000000000000000126631511334557700245140ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class QLinearMatMul(Base): @staticmethod def export_int() -> None: for quant_type_name in ["uint8", "int8"]: quant_type = getattr(np, quant_type_name) for dtype_name in ["float32", "float16"]: dtype = getattr(np, dtype_name) node = onnx.helper.make_node( "QLinearMatMul", inputs=[ "a", "a_scale", "a_zero_point", "b", "b_scale", "b_zero_point", "y_scale", "y_zero_point", ], outputs=["y"], ) # 2D a = np.array([[208, 236, 0, 238], [3, 214, 255, 29]]) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]] ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array( [114 - 127] if quant_type == np.int8 else [114], dtype=quant_type ) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: output = np.array([[41, -12, -9], [1, -75, 20]]) else: output = np.array([[168, 115, 255], [1, 66, 151]]) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_2D_{quant_type_name}_{dtype_name}", ) # 3D a = np.array( [ [[208, 236, 0, 238], [3, 214, 255, 29]], [[208, 236, 0, 238], [3, 214, 255, 29]], ], ) if quant_type == np.int8: a -= 127 a = a.astype(quant_type) a_scale = np.array([0.0066], dtype=dtype) a_zero_point = np.array( [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type ) b = np.array( [ [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]], ], ) if quant_type == np.int8: b -= 127 b = b.astype(quant_type) b_scale = np.array([0.00705], dtype=dtype) b_zero_point = np.array([114], dtype=quant_type) y_scale = np.array([0.0107], dtype=dtype) y_zero_point = np.array( [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type ) if quant_type == np.int8: if dtype == np.float32: output = np.array( [ [[-86, 117, 120], [115, 39, -121]], [[-86, 117, 120], [115, 39, -121]], ] ) else: output = np.array( [ [[-86, 116, 119], [115, 39, -121]], [[-86, 116, 119], [115, 39, -121]], ] ) else: output = np.array( [ [[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]], ] ) output = output.astype(quant_type) expect( node, inputs=[ a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point, ], outputs=[output], name=f"test_qlinearmatmul_3D_{quant_type_name}_{dtype_name}", ) onnx-onnx-bca0315/onnx/backend/test/case/node/quantizelinear.py000066400000000000000000000326701511334557700246740ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx import TensorProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.helper import make_tensor class QuantizeLinear(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32) y_scale = np.float32(2) y_zero_point = np.uint8(128) y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear", ) @staticmethod def export_axis() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ [ [[-162, 10], [-100, 232], [-20, -50]], [[-76, 0], [0, 252], [32, -44]], [[245, -485], [-960, -270], [-375, -470]], ], ], dtype=np.float32, ) y_scale = np.array([2, 4, 5], dtype=np.float32) y_zero_point = np.array([84, 24, 196], dtype=np.uint8) y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype( np.uint8 ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_axis", ) @staticmethod def export_e4m3fn() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E4M3FN, [1], [0]) y = make_tensor("y", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e4m3fn", ) @staticmethod def export_e5m2() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32) y_scale = np.float32(2) y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E5M2, [1], [0.0]) y = make_tensor("y", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96]) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_e5m2", ) @staticmethod def export_uint16() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -128.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65536.0, -65534.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.uint16(32767) y = np.array( [ 32767, 32703, 32769, 32765, 32768, 32766, 32769, 32765, 65535, 0, 65535, 0, ] ).astype(np.uint16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint16", ) @staticmethod def export_int16() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], ) x = np.array( [ 0.0, -514.0, 3.0, -3.0, 2.9, -2.9, 3.1, -3.1, 65022.0, -66046.0, 65023.0, -66047.0, 65024.0, -66048.0, 70000.0, -70000.0, ] ).astype(np.float32) y_scale = np.float32(2.0) y_zero_point = np.int16(256) y = np.array( [ 256, -1, 258, 254, 257, 255, 258, 254, 32767, -32767, 32767, -32768, 32767, -32768, 32767, -32768, ] ).astype(np.int16) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int16", ) @staticmethod def export_uint4() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT4, x.shape, [1, 2, 3, 5, 0, 0, 3, 4, 4, 5, 5, 11] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint4", ) @staticmethod def export_int4() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [12, 15, 16, 40], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT4, y_scale.shape, np.ones_like(y_scale) ) y = make_tensor( "y", TensorProto.INT4, x.shape, [1, 2, 3, 5, -8, -6, 3, 4, 4, 5, 5, 7] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int4", ) @staticmethod def export_uint2() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-2.0, -1.0, 1.0, 3.0], [4.0, 5.0, 6.0, 7.0], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.UINT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.UINT2, x.shape, [0, 1, 2, 3, 0, 0, 0, 1, 1, 1, 2, 2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_uint2", ) @staticmethod def export_int2() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-4.0, -3.0, 1.0, 2.0], [-0.0, -2.5, -4.8, -8.6], ], dtype=np.float32, ) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.INT2, y_scale.shape, np.zeros_like(y_scale) ) y = make_tensor( "y", TensorProto.INT2, x.shape, [0, 1, 1, 1, -1, -1, 0, 1, 0, -1, -1, -2] ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_int2", ) @staticmethod def export_float4e2m1() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=0, ) x = np.array( [ [0.0, 2.5, 4.8, 8.6], [-30, -20, 6, 9], [-0.0, -2.5, -4.8, -8.6], ] ).astype(np.float32) y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32) y_zero_point = make_tensor( "y_zero_point", TensorProto.FLOAT4E2M1, y_scale.shape, np.zeros_like(y_scale), ) y = make_tensor( "y", TensorProto.FLOAT4E2M1, x.shape, [0, 1, 2, 4, -6, -6, 2, 3, 0, -0.5, -1, -2], ) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_float4e2m1", ) @staticmethod def export_blocked_asymmetric() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale", "y_zero_point"], outputs=["y"], axis=1, block_size=2, ) x = np.array( [ [6.0, 12.0, 50.0, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) y_zero_point = np.array( [ [0, 1], [1, 0], [2, 3], ], dtype=np.uint8, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) assert y_scale.shape == y_zero_point.shape block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_zero_point_elementwise = np.repeat( y_zero_point, repeats=repeats, axis=block_axis ) y = np.rint(x / y_scale_elementwise + y_zero_point_elementwise).astype(np.uint8) expect( node, inputs=[x, y_scale, y_zero_point], outputs=[y], name="test_quantizelinear_blocked_asymmetric", ) @staticmethod def export_blocked_symmetric() -> None: node = onnx.helper.make_node( "QuantizeLinear", inputs=["x", "y_scale"], outputs=["y"], axis=1, block_size=2, output_dtype=TensorProto.INT16, ) x = np.array( [ [6.0, -8, -10, 5.0], [1.0, 8.0, 4.0, 5.0], [0.0, 20.0, 10.0, 4.0], ], dtype=np.float32, ) y_scale = np.array( [ [1.5, 2.5], [3.0, 4.9], [5.1, 6.9], ], dtype=np.float32, ) # x.shape = (3, 4) # y_scale.shape = (3, 2) block_axis = 1 # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2) assert all( x.shape[i] == y_scale.shape[i] for i in range(len(x.shape)) if i != block_axis ) assert x.shape[block_axis] % y_scale.shape[block_axis] == 0 repeats = x.shape[block_axis] // y_scale.shape[block_axis] # Create element-wise scale and zero point y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis) y_val = np.clip( np.rint(x / y_scale_elementwise), a_min=-32768, a_max=32767 ).astype(np.int16) y = make_tensor( "y", TensorProto.INT16, x.shape, y_val, ) expect( node, inputs=[x, y_scale], outputs=[y], name="test_quantizelinear_blocked_symmetric", ) onnx-onnx-bca0315/onnx/backend/test/case/node/rangeop.py000066400000000000000000000026771511334557700233000ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Range(Base): @staticmethod def export_range_float_type_positive_delta() -> None: node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.float32(1) limit = np.float32(5) delta = np.float32(2) output = np.arange( start, limit, delta, dtype=np.float32 ) # expected output [1.0, 3.0] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_float_type_positive_delta", ) @staticmethod def export_range_int32_type_negative_delta() -> None: node = onnx.helper.make_node( "Range", inputs=["start", "limit", "delta"], outputs=["output"], ) start = np.int32(10) limit = np.int32(6) delta = np.int32(-3) output = np.arange( start, limit, delta, dtype=np.int32 ) # expected output [10, 7] expect( node, inputs=[start, limit, delta], outputs=[output], name="test_range_int32_type_negative_delta", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reciprocal.py000066400000000000000000000014261511334557700237570ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Reciprocal(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Reciprocal", inputs=["x"], outputs=["y"], ) x = np.array([-4, 2]).astype(np.float32) y = np.reciprocal(x) # expected output [-0.25, 0.5], expect(node, inputs=[x], outputs=[y], name="test_reciprocal_example") x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5 y = np.reciprocal(x) expect(node, inputs=[x], outputs=[y], name="test_reciprocal") onnx-onnx-bca0315/onnx/backend/test/case/node/reduce_log_sum.py000066400000000000000000000060231511334557700246260ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceLogSum(Base): @staticmethod def export_nokeepdims() -> None: shape = [3, 4, 5] axes = np.array([2, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_desc_axes", ) axes = np.array([0, 1], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=0, ) data = np.random.ranf(shape).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_asc_axes", ) @staticmethod def export_keepdims() -> None: node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, keepdims=True)) axes = np.array([], dtype=np.int64) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_default", ) @staticmethod def export_negative_axes_keepdims() -> None: axes = np.array([-2], dtype=np.int64) node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"] ) data = np.random.ranf([3, 4, 5]).astype(np.float32) reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True)) # print(reduced) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_negative_axes", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reduce_log_sum_exp.py000066400000000000000000000132221511334557700255010ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceLogSumExp(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[20., 2.31326175] # [40.00004578, 2.31326175] # [60.00671387, 2.31326175]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_keepdims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[60.00671387]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double ) reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1)) # print(reduced) # [[[20., 2.31326175]] # [[40.00004578, 2.31326175]] # [[60.00671387, 2.31326175]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.double) reduced = np.log( np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_negative_axes_keepdims_random", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceLogSumExp", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = np.log(zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_log_sum_exp_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducel1.py000066400000000000000000000127441511334557700233450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceL1(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 7.], [11., 15.], [19., 23.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_keep_dims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_negative_axes_keep_dims_random", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l1_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducel2.py000066400000000000000000000135741511334557700233500ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceL2(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[2.23606798, 5.], # [7.81024968, 10.63014581], # [13.45362405, 16.2788206]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_keep_dims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) # print(reduced) # [[[25.49509757]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape) # print(data) # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]] reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) # print(reduced) # [[[2.23606798], [5.]] # [[7.81024968], [10.63014581]] # [[13.45362405], [16.2788206 ]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sqrt( np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1) ) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_negative_axes_keep_dims_random", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_l2_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducemax.py000066400000000000000000000151011511334557700236040ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceMax(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[20., 2.] # [40., 2.] # [60., 2.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdim_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_max_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[20., 2.]] # [[40., 2.]] # [[60., 2.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_bool_inputs() -> None: axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[True], # [True], # [True], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_bool_inputs", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMax", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = -(one / zero) # -inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_max_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducemean.py000066400000000000000000000113061511334557700237420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceMean(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[12.5, 1.5] # [35., 1.5] # [57.5, 1.5]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_keepdims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[18.25]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMean", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[12.5, 1.5]] # [[35., 1.5]] # [[57.5, 1.5]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_mean_negative_axes_keepdims_random", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducemin.py000066400000000000000000000151531511334557700236110ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceMin(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[5., 1.] # [30., 1.] # [55., 1.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_do_not_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[1.]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_min_default_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32, ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[5., 1.]] # [[30., 1.]] # [[55., 1.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_example", opset_imports=[onnx.helper.make_opsetid("", 18)], ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_negative_axes_keepdims_random", opset_imports=[onnx.helper.make_opsetid("", 18)], ) @staticmethod def export_bool_inputs() -> None: axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[True, True], [True, False], [False, True], [False, False]], ) reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=bool(keepdims)) # print(reduced) # [[ True], # [False], # [False], # [False]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_bool_inputs", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceMin", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) one = np.array(np.ones(reduced_shape, dtype=np.float32)) zero = np.array(np.zeros(reduced_shape, dtype=np.float32)) reduced = one / zero # inf expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_min_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reduceprod.py000066400000000000000000000122671511334557700237750ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceProd(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[3., 8.] # [35., 48.] # [99., 120.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_keepdims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = None keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) # print(reduced) # [[[4.790016e+08]]] expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=axes, keepdims=keepdims == 1) expect( node, inputs=[data], outputs=[reduced], name="test_reduce_prod_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[3., 8.]] # [[35., 48.]] # [[99., 120.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_negative_axes_keepdims_random", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceProd", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.ones(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_prod_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducesum.py000066400000000000000000000157631511334557700236410ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceSum(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[4., 6.] # [12., 14.] # [20., 22.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_keepdims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) # print(reduced) # [[[78.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) # print(reduced) # [[[4., 6.]] # [[12., 14.]] # [[20., 22.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_negative_axes_keepdims_random", ) @staticmethod def export_empty_axes_input_noop() -> None: shape = [3, 2, 2] keepdims = 1 node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, noop_with_empty_axes=True, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) axes = np.array([], dtype=np.int64) reduced = np.array(data) # print(reduced) # [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.array(data) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_axes_input_noop", ) @staticmethod def export_empty_set() -> None: """Test case with the reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set", ) @staticmethod def export_non_reduced_axis_zero() -> None: """Test case with the non-reduced-axis of size zero.""" shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 0, 1] node = onnx.helper.make_node( "ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([2], dtype=np.int64) reduced = np.array([], dtype=np.float32).reshape(reduced_shape) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_empty_set_non_reduced_axis_zero", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reducesumsquare.py000066400000000000000000000126661511334557700250610ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReduceSumSquare(Base): @staticmethod def export_do_not_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 0 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[10., 20.] # [74., 100.] # [202., 244.]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_do_not_keepdims_random", ) @staticmethod def export_keepdims() -> None: shape = [3, 2, 2] axes = np.array([1], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_keepdims_random", ) @staticmethod def export_default_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) # print(reduced) # [[[650.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_default_axes_keepdims_random", ) @staticmethod def export_negative_axes_keepdims() -> None: shape = [3, 2, 2] axes = np.array([-2], dtype=np.int64) keepdims = 1 node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32 ) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) # print(reduced) # [[[10., 20.s]] # [[74., 100.]] # [[202., 244.]]] expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_example", ) np.random.seed(0) data = np.random.uniform(-10, 10, shape).astype(np.float32) reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_negative_axes_keepdims_random", ) @staticmethod def export_empty_set() -> None: shape = [2, 0, 4] keepdims = 1 reduced_shape = [2, 1, 4] node = onnx.helper.make_node( "ReduceSumSquare", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims, ) data = np.array([], dtype=np.float32).reshape(shape) axes = np.array([1], dtype=np.int64) reduced = np.array(np.zeros(reduced_shape, dtype=np.float32)) expect( node, inputs=[data, axes], outputs=[reduced], name="test_reduce_sum_square_empty_set", ) onnx-onnx-bca0315/onnx/backend/test/case/node/regex_full_match.py000066400000000000000000000035721511334557700251500ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class RegexFullMatch(Base): @staticmethod def export_basic() -> None: node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"www\.[\w.-]+\.\bcom\b", ) x = np.array(["www.google.com", "www.facebook.com", "www.bbc.co.uk"]).astype( object ) result = np.array([True, True, False]) expect(node, inputs=[x], outputs=[result], name="test_regex_full_match_basic") @staticmethod def export_match_email_domain() -> None: node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array( [ ["account@gmail.com", "account@hotmail.com"], ["not email", "account2@yahoo.com"], ] ).astype(object) result = np.array([[True, False], [False, True]]) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_email_domain", ) @staticmethod def export_match_empty() -> None: node = onnx.helper.make_node( "RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)", ) x = np.array([[], []]).astype(object) result = np.array([[], []]).astype(bool) expect( node, inputs=[x], outputs=[result], name="test_regex_full_match_empty", ) onnx-onnx-bca0315/onnx/backend/test/case/node/relu.py000066400000000000000000000011061511334557700225760ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Relu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Relu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, 0, np.inf) expect(node, inputs=[x], outputs=[y], name="test_relu") onnx-onnx-bca0315/onnx/backend/test/case/node/reshape.py000066400000000000000000000055111511334557700232620ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def reshape_reference_implementation( data: np.ndarray, shape: np.ndarray, allowzero: int = 0 ) -> np.ndarray: # replace zeros with corresponding dim size # we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set new_shape = np.copy(shape) if allowzero == 0: zeros_index = np.where(shape == 0) new_shape[zeros_index] = np.array(data.shape)[zeros_index] return np.reshape(data, new_shape) class Reshape(Base): @staticmethod def export_reshape() -> None: original_shape = [2, 3, 4] test_cases = { "reordered_all_dims": np.array([4, 2, 3], dtype=np.int64), "reordered_last_dims": np.array([2, 4, 3], dtype=np.int64), "reduced_dims": np.array([2, 12], dtype=np.int64), "extended_dims": np.array([2, 3, 2, 2], dtype=np.int64), "one_dim": np.array([24], dtype=np.int64), "negative_dim": np.array([2, -1, 2], dtype=np.int64), "negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64), "zero_dim": np.array([2, 0, 4, 1], dtype=np.int64), "zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], ) reshaped = reshape_reference_implementation(data, shape) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) @staticmethod def export_allowzero() -> None: original_shape = [0, 3, 4] test_cases = { "allowzero_reordered": np.array([3, 4, 0], dtype=np.int64), } data = np.random.random_sample(original_shape).astype(np.float32) for test_name, shape in test_cases.items(): node = onnx.helper.make_node( "Reshape", inputs=["data", "shape"], outputs=["reshaped"], allowzero=1, # if allowzero=1, final shape = (3, 4, 0) # if allowzero=0, final shape = (3, 4, 4) ) reshaped = reshape_reference_implementation(data, shape, allowzero=1) expect( node, inputs=[data, shape], outputs=[reshaped], name="test_reshape_" + test_name, ) onnx-onnx-bca0315/onnx/backend/test/case/node/resize.py000066400000000000000000001447301511334557700231430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_resize import _cubic_coeffs as cubic_coeffs from onnx.reference.ops.op_resize import ( _cubic_coeffs_antialias as cubic_coeffs_antialias, ) from onnx.reference.ops.op_resize import _interpolate_nd as interpolate_nd from onnx.reference.ops.op_resize import _linear_coeffs as linear_coeffs from onnx.reference.ops.op_resize import ( _linear_coeffs_antialias as linear_coeffs_antialias, ) from onnx.reference.ops.op_resize import _nearest_coeffs as nearest_coeffs class Resize(Base): @staticmethod def export_resize_upsample_scales_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest", ) @staticmethod def export_resize_downsample_scales_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_nearest", ) @staticmethod def export_resize_upsample_sizes_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest", ) @staticmethod def export_resize_downsample_sizes_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 1, 3], dtype=np.int64) # [[[[1. 2. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest", ) @staticmethod def export_resize_upsample_scales_linear() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.25 1.75 2. ] # [1.5 1.75 2.25 2.5 ] # [2.5 2.75 3.25 3.5 ] # [3. 3.25 3.75 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear", ) @staticmethod def export_resize_upsample_scales_linear_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.33333333 1.66666667 2. ] # [1.66666667 2. 2.33333333 2.66666667] # [2.33333333 2.66666667 3. 3.33333333] # [3. 3.33333333 3.66666667 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_align_corners", ) @staticmethod def export_resize_downsample_scales_linear() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[2.6666665 4.3333331]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear", ) @staticmethod def export_resize_downsample_scales_linear_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.142857]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_align_corners", ) @staticmethod def export_resize_upsample_scales_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125 # 2.91015625 3.38671875 3.68359375] # [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875 # 4.09765625 4.57421875 4.87109375] # [ 3.56640625 3.86328125 4.33984375 4.96875 5.375 # 6.00390625 6.48046875 6.77734375] # [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625 # 8.51953125 8.99609375 9.29296875] # [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625 # 10.14453125 10.62109375 10.91796875] # [10.22265625 10.51953125 10.99609375 11.625 12.03125 # 12.66015625 13.13671875 13.43359375] # [12.12890625 12.42578125 12.90234375 13.53125 13.9375 # 14.56640625 15.04296875 15.33984375] # [13.31640625 13.61328125 14.08984375 14.71875 15.125 # 15.75390625 16.23046875 16.52734375]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic", ) @staticmethod def export_resize_upsample_scales_cubic_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394 # 3.19970845 3.65889213 4. ] # [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542 # 4.56413994 5.02332362 5.36443149] # [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012 # 6.40087464 6.86005831 7.20116618] # [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819 # 8.51749271 8.97667638 9.31778426] # [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968 # 9.8819242 10.34110787 10.68221574] # [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776 # 11.99854227 12.45772595 12.79883382] # [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245 # 13.83527697 14.29446064 14.63556851] # [13. 13.34110787 13.80029155 14.32944606 14.67055394 # 15.19970845 15.65889213 16. ]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_align_corners", ) @staticmethod def export_resize_downsample_scales_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.47119141 2.78125 4.08251953] # [ 6.71142578 8.02148438 9.32275391] # [11.91650391 13.2265625 14.52783203]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic", ) @staticmethod def export_resize_downsample_scales_cubic_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1. 2.39519159 3.79038317] # [ 6.58076634 7.97595793 9.37114951] # [12.16153268 13.55672427 14.95191585]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_align_corners", ) @staticmethod def export_resize_upsample_sizes_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 9, 10], dtype=np.int64) # [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922 # 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922] # [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963 # 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963] # [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693 # 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693] # [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069 # 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069] # [ 6.88975 7.07525 7.40625 7.85725 8.342 # 8.658 9.14275 9.59375 9.92475 10.11025 ] # [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931 # 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931] # [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307 # 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307] # [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037 # 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037] # [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078 # 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_cubic", ) @staticmethod def export_resize_downsample_sizes_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.63078704 3.00462963 4.37847222] # [ 7.12615741 8.5 9.87384259] # [12.62152778 13.99537037 15.36921296]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic", ) # TensorFlow v1 bicubic with half_pixel_centers=True @staticmethod def export_resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882 # 2.93713516 3.47917561 3.73529412] # [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285 # 3.96160918 4.50364964 4.75976814] # [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466 # 6.12977099 6.67181144 6.92792995] # [ 5.91176471 6.16788321 6.70992366 7.25 7.75 # 8.29007634 8.83211679 9.08823529] # [ 7.91176471 8.16788321 8.70992366 9.25 9.75 # 10.29007634 10.83211679 11.08823529] # [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534 # 12.45038168 12.99242213 13.24854064] # [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715 # 14.61854349 15.16058394 15.41670245] # [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118 # 15.64301751 16.18505796 16.44117647]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside", ) @staticmethod def export_resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.36812675 2.6695014 4.0133367 ] # [ 6.57362535 7.875 9.2188353 ] # [11.94896657 13.25034122 14.59417652]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside", ) # TensorFlow v1 bicubic with half_pixel_centers=False @staticmethod def export_resize_upsample_scales_cubic_asymmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="asymmetric", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4. # 4.09375] # [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625 # 5.71875] # [ 5. 5.40625 6. 6.5 7. 7.59375 8. # 8.09375] # [ 7. 7.40625 8. 8.5 9. 9.59375 10. # 10.09375] # [ 9. 9.40625 10. 10.5 11. 11.59375 12. # 12.09375] # [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375 # 14.46875] # [13. 13.40625 14. 14.5 15. 15.59375 16. # 16.09375] # [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375 # 16.46875]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.75), scale_factors=scales, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_asymmetric", ) @staticmethod def export_resize_tf_crop_and_resize() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize", ) @staticmethod def export_resize_tf_crop_and_resize_extrapolation_value() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 10. 10. ] # [12.400001 10. 10. ] # [10. 10. 10. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_extrapolation_value", ) @staticmethod def export_resize_downsample_sizes_linear_pytorch_half_pixel() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="pytorch_half_pixel", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 1], dtype=np.int64) # [[[[ 1.6666666] # [ 7. ] # [12.333333 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, coordinate_transformation_mode="pytorch_half_pixel", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_pytorch_half_pixel", ) @staticmethod def export_resize_upsample_sizes_nearest_floor_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="align_corners", nearest_mode="floor", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [13. 13. 13. 14. 14. 15. 15. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="floor"), output_size=sizes, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_floor_align_corners", ) @staticmethod def export_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="round_prefer_ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"), output_size=sizes, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", ) @staticmethod def export_resize_upsample_sizes_nearest_ceil_half_pixel() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="half_pixel", nearest_mode="ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_ceil_half_pixel", ) @staticmethod def export_resize_downsample_scales_linear_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.875 4.5 ] # [ 9.375 11. ]]]] output = interpolate_nd( data, linear_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_antialias", ) @staticmethod def export_resize_downsample_sizes_linear_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 2.3636363 3.590909 4.818182 ] # [ 7.2727275 8.5 9.727273 ] # [12.181818 13.409091 14.636364 ]]]] output = interpolate_nd( data, linear_coeffs_antialias, output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_antialias", ) @staticmethod def export_resize_downsample_scales_cubic_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.5180721 4.2858863] # [ 9.589329 11.357142 ]]]] output = interpolate_nd( data, cubic_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_antialias", ) @staticmethod def export_resize_downsample_sizes_cubic_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.7750092 3.1200073 4.4650054] # [ 7.1550016 8.5 9.844998 ] # [12.534994 13.8799925 15.224991 ]]]] output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype( np.float32 ) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic_antialias", ) @staticmethod def export_resize_upsample_scales_nearest_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_2_3", ) @staticmethod def export_resize_upsample_scales_nearest_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([3.0, 2.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_3_2", ) @staticmethod def export_resize_upsample_sizes_nearest_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_2_3", ) @staticmethod def export_resize_upsample_sizes_nearest_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([8, 7], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_3_2", ) @staticmethod def export_resize_tf_crop_and_resize_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_2_3", ) @staticmethod def export_resize_tf_crop_and_resize_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_3_2", ) @staticmethod def export_resize_upsample_sizes_nearest_not_larger() -> None: keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7 # [[[[1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_larger", ) @staticmethod def export_resize_upsample_sizes_nearest_not_smaller() -> None: keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8 # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_smaller", ) @staticmethod def export_resize_downsample_sizes_nearest_not_larger() -> None: keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2 # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_larger", ) @staticmethod def export_resize_downsample_sizes_nearest_not_smaller() -> None: keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3 # [[[[1. 2. 4.] # [5. 6. 8.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_smaller", ) @staticmethod def export_resize_downsample_scales_linear_half_pixel_symmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32) # [[[[1.6666667, 3.3333333]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_half_pixel_symmetric", ) @staticmethod def export_resize_upsample_scales_linear_half_pixel_symmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32) # [[[[1. , 1.15986395, 1.5 , 1.84013605, 2. ], # [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738], # [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262], # [3. , 3.15986395, 3.5 , 3.84013605, 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_half_pixel_symmetric", ) onnx-onnx-bca0315/onnx/backend/test/case/node/reversesequence.py000066400000000000000000000043001511334557700250320ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ReverseSequence(Base): @staticmethod def export_reversesequence_time() -> None: node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=0, batch_axis=1, ) x = np.array( [ [0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64) y = np.array( [ [3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_time", ) @staticmethod def export_reversesequence_batch() -> None: node = onnx.helper.make_node( "ReverseSequence", inputs=["x", "sequence_lens"], outputs=["y"], time_axis=1, batch_axis=0, ) x = np.array( [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ], dtype=np.float32, ) sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64) y = np.array( [ [0.0, 1.0, 2.0, 3.0], [5.0, 4.0, 6.0, 7.0], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0], ], dtype=np.float32, ) expect( node, inputs=[x, sequence_lens], outputs=[y], name="test_reversesequence_batch", ) onnx-onnx-bca0315/onnx/backend/test/case/node/rmsnormalization.py000066400000000000000000000075101511334557700252440ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_rms_normalization import _rms_normalization def calculate_normalized_shape(x_shape, axis): rank = len(x_shape) if axis < 0: axis = axis + rank return x_shape[axis:] class RMSNormalization(Base): @staticmethod def export() -> None: X = np.random.randn(2, 3, 4, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_4d_axis_negative_{-axis}" else: name = f"test_rms_normalization_4d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) @staticmethod def export_default_axis() -> None: X = np.random.randn(2, 3, 4, 5).astype(np.float32) # Default axis in RMSNormalization is -1. normalized_shape = calculate_normalized_shape(X.shape, -1) W = np.random.randn(*normalized_shape).astype(np.float32) # Axis is default to -1 in the reference implementation. Y = _rms_normalization(X, W) # Not specifying axis attribute means -1. node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], ) expect( node, inputs=[X, W], outputs=[Y], name="test_rms_normalization_default_axis", ) @staticmethod def export2d() -> None: X = np.random.randn(3, 4).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, ) if axis < 0: name = f"test_rms_normalization_2d_axis_negative_{-axis}" else: name = f"test_rms_normalization_2d_axis{axis}" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) @staticmethod def export3d_epsilon() -> None: epsilon = 1e-1 X = np.random.randn(2, 3, 5).astype(np.float32) def case(axis: int) -> None: normalized_shape = calculate_normalized_shape(X.shape, axis) W = np.random.randn(*normalized_shape).astype(np.float32) Y = _rms_normalization(X, W, axis=axis, epsilon=epsilon) node = onnx.helper.make_node( "RMSNormalization", inputs=["X", "W"], outputs=["Y"], axis=axis, epsilon=epsilon, ) if axis < 0: name = f"test_rms_normalization_3d_axis_negative_{-axis}_epsilon" else: name = f"test_rms_normalization_3d_axis{axis}_epsilon" expect(node, inputs=[X, W], outputs=[Y], name=name) for i in range(len(X.shape)): case(i) case(i - len(X.shape)) onnx-onnx-bca0315/onnx/backend/test/case/node/rnn.py000066400000000000000000000144331511334557700224330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class RNNHelper: def __init__(self, **params: Any) -> None: # RNN Input Names X = "X" W = "W" R = "R" B = "B" H_0 = "initial_h" LAYOUT = "layout" required_inputs = [X, W, R] for i in required_inputs: assert i in params, f"Missing Required Input: {i}" self.num_directions = params[str(W)].shape[0] if self.num_directions == 1: for k, v in params.items(): if k != X: params[k] = np.squeeze(v, axis=0) hidden_size = params[R].shape[-1] batch_size = params[X].shape[1] layout = params.get(LAYOUT, 0) x = params[X] x = x if layout == 0 else np.swapaxes(x, 0, 1) b = ( params[B] if B in params else np.zeros(2 * hidden_size, dtype=np.float32) ) h_0 = ( params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size), dtype=np.float32) ) self.X = x self.W = params[W] self.R = params[R] self.B = b self.H_0 = h_0 self.LAYOUT = layout else: raise NotImplementedError() def f(self, x: np.ndarray) -> np.ndarray: return np.tanh(x) def step(self) -> tuple[np.ndarray, np.ndarray]: seq_length = self.X.shape[0] hidden_size = self.H_0.shape[-1] batch_size = self.X.shape[1] Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size]) h_list = [] H_t = self.H_0 for x in np.split(self.X, self.X.shape[0], axis=0): H = self.f( np.dot(x, np.transpose(self.W)) + np.dot(H_t, np.transpose(self.R)) + np.add(*np.split(self.B, 2)) ) h_list.append(H) H_t = H concatenated = np.concatenate(h_list) if self.num_directions == 1: Y[:, 0, :, :] = concatenated if self.LAYOUT == 0: Y_h = Y[-1] else: Y = np.transpose(Y, [2, 0, 1, 3]) Y_h = Y[:, :, -1, :] return Y, Y_h class RNN(Base): @staticmethod def export_defaults() -> None: input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R) _, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_defaults", ) @staticmethod def export_initial_bias() -> None: input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype( np.float32 ) input_size = 3 hidden_size = 5 custom_bias = 0.1 weight_scale = 0.1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) # Adding custom bias W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32) R_B = np.zeros((1, hidden_size)).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_simple_rnn_with_initial_bias", ) @staticmethod def export_seq_length() -> None: input = np.array( [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]], ] ).astype(np.float32) input_size = 3 hidden_size = 5 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R", "B"], outputs=["", "Y_h"], hidden_size=hidden_size, ) W = np.random.randn(1, hidden_size, input_size).astype(np.float32) R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32) # Adding custom bias W_B = np.random.randn(1, hidden_size).astype(np.float32) R_B = np.random.randn(1, hidden_size).astype(np.float32) B = np.concatenate((W_B, R_B), axis=1) rnn = RNNHelper(X=input, W=W, R=R, B=B) _, Y_h = rnn.step() expect( node, inputs=[input, W, R, B], outputs=[Y_h.astype(np.float32)], name="test_rnn_seq_length", ) @staticmethod def export_batchwise() -> None: input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32) input_size = 2 hidden_size = 4 weight_scale = 0.5 layout = 1 node = onnx.helper.make_node( "RNN", inputs=["X", "W", "R"], outputs=["Y", "Y_h"], hidden_size=hidden_size, layout=layout, ) W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32) R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32) rnn = RNNHelper(X=input, W=W, R=R, layout=layout) Y, Y_h = rnn.step() expect( node, inputs=[input, W, R], outputs=[Y.astype(np.float32), Y_h.astype(np.float32)], name="test_simple_rnn_batchwise", ) onnx-onnx-bca0315/onnx/backend/test/case/node/roialign.py000066400000000000000000000350701511334557700234420ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def get_roi_align_input_values(): X = np.array( [ [ [ [ 0.2764, 0.7150, 0.1958, 0.3416, 0.4638, 0.0259, 0.2963, 0.6518, 0.4856, 0.7250, ], [ 0.9637, 0.0895, 0.2919, 0.6753, 0.0234, 0.6132, 0.8085, 0.5324, 0.8992, 0.4467, ], [ 0.3265, 0.8479, 0.9698, 0.2471, 0.9336, 0.1878, 0.4766, 0.4308, 0.3400, 0.2162, ], [ 0.0206, 0.1720, 0.2155, 0.4394, 0.0653, 0.3406, 0.7724, 0.3921, 0.2541, 0.5799, ], [ 0.4062, 0.2194, 0.4473, 0.4687, 0.7109, 0.9327, 0.9815, 0.6320, 0.1728, 0.6119, ], [ 0.3097, 0.1283, 0.4984, 0.5068, 0.4279, 0.0173, 0.4388, 0.0430, 0.4671, 0.7119, ], [ 0.1011, 0.8477, 0.4726, 0.1777, 0.9923, 0.4042, 0.1869, 0.7795, 0.9946, 0.9689, ], [ 0.1366, 0.3671, 0.7011, 0.6234, 0.9867, 0.5585, 0.6985, 0.5609, 0.8788, 0.9928, ], [ 0.5697, 0.8511, 0.6711, 0.9406, 0.8751, 0.7496, 0.1650, 0.1049, 0.1559, 0.2514, ], [ 0.7012, 0.4056, 0.7879, 0.3461, 0.0415, 0.2998, 0.5094, 0.3727, 0.5482, 0.0502, ], ] ] ], dtype=np.float32, ) batch_indices = np.array([0, 0, 0], dtype=np.int64) rois = np.array([[0, 0, 9, 9], [0, 5, 4, 9], [5, 5, 9, 9]], dtype=np.float32) return X, batch_indices, rois class RoiAlign(Base): @staticmethod def export_roialign_aligned_false() -> None: node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.4664, 0.4466, 0.3405, 0.5688, 0.6068], [0.3714, 0.4296, 0.3835, 0.5562, 0.3510], [0.2768, 0.4883, 0.5222, 0.5528, 0.4171], [0.4713, 0.4844, 0.6904, 0.4920, 0.8774], [0.6239, 0.7125, 0.6289, 0.3355, 0.3495], ] ], [ [ [0.3022, 0.4305, 0.4696, 0.3978, 0.5423], [0.3656, 0.7050, 0.5165, 0.3172, 0.7015], [0.2912, 0.5059, 0.6476, 0.6235, 0.8299], [0.5916, 0.7389, 0.7048, 0.8372, 0.8893], [0.6227, 0.6153, 0.7097, 0.6154, 0.4585], ] ], [ [ [0.2384, 0.3379, 0.3717, 0.6100, 0.7601], [0.3767, 0.3785, 0.7147, 0.9243, 0.9727], [0.5749, 0.5826, 0.5709, 0.7619, 0.8770], [0.5355, 0.2566, 0.2141, 0.2796, 0.3600], [0.4365, 0.3504, 0.2887, 0.3661, 0.2349], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_false", ) @staticmethod def export_roialign_aligned_true() -> None: node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="half_pixel", ) X, batch_indices, rois = get_roi_align_input_values() # (num_rois, C, output_height, output_width) Y = np.array( [ [ [ [0.5178, 0.3434, 0.3229, 0.4474, 0.6344], [0.4031, 0.5366, 0.4428, 0.4861, 0.4023], [0.2512, 0.4002, 0.5155, 0.6954, 0.3465], [0.3350, 0.4601, 0.5881, 0.3439, 0.6849], [0.4932, 0.7141, 0.8217, 0.4719, 0.4039], ] ], [ [ [0.3070, 0.2187, 0.3337, 0.4880, 0.4870], [0.1871, 0.4914, 0.5561, 0.4192, 0.3686], [0.1433, 0.4608, 0.5971, 0.5310, 0.4982], [0.2788, 0.4386, 0.6022, 0.7000, 0.7524], [0.5774, 0.7024, 0.7251, 0.7338, 0.8163], ] ], [ [ [0.2393, 0.4075, 0.3379, 0.2525, 0.4743], [0.3671, 0.2702, 0.4105, 0.6419, 0.8308], [0.5556, 0.4543, 0.5564, 0.7502, 0.9300], [0.6626, 0.5617, 0.4813, 0.4954, 0.6663], [0.6636, 0.3721, 0.2056, 0.1928, 0.2478], ] ], ], dtype=np.float32, ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_aligned_true", ) @staticmethod def export_roialign_mode_max() -> None: X = np.array( [ [ [ [ 0.2764, 0.715, 0.1958, 0.3416, 0.4638, 0.0259, 0.2963, 0.6518, 0.4856, 0.725, ], [ 0.9637, 0.0895, 0.2919, 0.6753, 0.0234, 0.6132, 0.8085, 0.5324, 0.8992, 0.4467, ], [ 0.3265, 0.8479, 0.9698, 0.2471, 0.9336, 0.1878, 0.4766, 0.4308, 0.34, 0.2162, ], [ 0.0206, 0.172, 0.2155, 0.4394, 0.0653, 0.3406, 0.7724, 0.3921, 0.2541, 0.5799, ], [ 0.4062, 0.2194, 0.4473, 0.4687, 0.7109, 0.9327, 0.9815, 0.632, 0.1728, 0.6119, ], [ 0.3097, 0.1283, 0.4984, 0.5068, 0.4279, 0.0173, 0.4388, 0.043, 0.4671, 0.7119, ], [ 0.1011, 0.8477, 0.4726, 0.1777, 0.9923, 0.4042, 0.1869, 0.7795, 0.9946, 0.9689, ], [ 0.1366, 0.3671, 0.7011, 0.6234, 0.9867, 0.5585, 0.6985, 0.5609, 0.8788, 0.9928, ], [ 0.5697, 0.8511, 0.6711, 0.9406, 0.8751, 0.7496, 0.165, 0.1049, 0.1559, 0.2514, ], [ 0.7012, 0.4056, 0.7879, 0.3461, 0.0415, 0.2998, 0.5094, 0.3727, 0.5482, 0.0502, ], ] ] ], dtype=np.float32, ) rois = np.array( [[0.0, 0.0, 9.0, 9.0], [0.0, 5.0, 4.0, 9.0], [5.0, 5.0, 9.0, 9.0]], dtype=np.float32, ) batch_indices = np.array([0, 0, 0], dtype=np.int64) Y = np.array( [ [ [ [0.3445228, 0.37310338, 0.37865096, 0.446696, 0.37991184], [0.4133513, 0.5455125, 0.6651902, 0.55805874, 0.27110294], [0.21223956, 0.40924096, 0.8417618, 0.792561, 0.37196714], [0.46835402, 0.39741728, 0.8012819, 0.4969306, 0.5495158], [0.3595896, 0.5196813, 0.5403741, 0.23814403, 0.19992709], ] ], [ [ [0.30517197, 0.5086199, 0.3189761, 0.4054401, 0.47630402], [0.50862, 0.8477, 0.37808004, 0.24936005, 0.79384017], [0.17620805, 0.29368007, 0.44870415, 0.4987201, 0.63148826], [0.51066005, 0.8511, 0.5368801, 0.9406, 0.70008016], [0.4487681, 0.51066035, 0.5042561, 0.5643603, 0.42004836], ] ], [ [ [0.21062402, 0.3510401, 0.37416005, 0.5967599, 0.46507207], [0.32336006, 0.31180006, 0.6236001, 0.9946, 0.7751202], [0.35744014, 0.5588001, 0.35897616, 0.7030401, 0.6353923], [0.5996801, 0.27940005, 0.17948808, 0.35152006, 0.31769615], [0.3598083, 0.40752012, 0.2385281, 0.43856013, 0.26313624], ] ], ], dtype=np.float32, ) node = onnx.helper.make_node( "RoiAlign", inputs=["X", "rois", "batch_indices"], mode="max", outputs=["Y"], spatial_scale=1.0, output_height=5, output_width=5, sampling_ratio=2, coordinate_transformation_mode="output_half_pixel", ) expect( node, inputs=[X, rois, batch_indices], outputs=[Y], name="test_roialign_mode_max", ) onnx-onnx-bca0315/onnx/backend/test/case/node/rotaryembedding.py000066400000000000000000000172251511334557700250170ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect from onnx.reference.ops.op_rotary_embedding import rotary_embedding class RotaryEmbedding(Base): @staticmethod def export_rotary_embedding() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding", ) @staticmethod def export_rotary_embedding_3d_input() -> None: num_heads = 4 node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], num_heads=num_heads, ) input_data = np.random.rand(2, 3, 32).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, num_heads=num_heads, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_3d_input", ) @staticmethod def export_rotary_embedding_interleaved() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 4).astype(np.float32) cos_cache_data = np.random.rand(50, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_interleaved", ) @staticmethod def export_rotary_embedding_with_rotary_dim() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_rotary_dim", ) @staticmethod def export_rotary_embedding_with_interleaved_rotary_dim() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache", "position_ids"], outputs=["output"], rotary_embedding_dim=4, interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64) sin_cache_data = np.random.rand(50, 2).astype(np.float32) cos_cache_data = np.random.rand(50, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data, interleaved=1, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data], outputs=[expected_output], name="test_rotary_embedding_with_interleaved_rotary_dim", ) @staticmethod def export_rotary_embedding_no_position_ids() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding(input_data, cos_cache_data, sin_cache_data) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids", ) @staticmethod def export_rotary_embedding_no_position_ids_interleaved() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], interleaved=1, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, interleaved=1, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_interleaved", ) @staticmethod def export_rotary_embedding_no_position_ids_rotary_dim() -> None: node = onnx.helper.make_node( "RotaryEmbedding", inputs=["input", "cos_cache", "sin_cache"], outputs=["output"], rotary_embedding_dim=4, ) input_data = np.random.rand(2, 4, 3, 8).astype(np.float32) sin_cache_data = np.random.rand(2, 3, 2).astype(np.float32) cos_cache_data = np.random.rand(2, 3, 2).astype(np.float32) expected_output = rotary_embedding( input_data, cos_cache_data, sin_cache_data, rotary_embedding_dim=4, ) expect( node, inputs=[input_data, cos_cache_data, sin_cache_data], outputs=[expected_output], name="test_rotary_embedding_no_position_ids_rotary_dim", ) onnx-onnx-bca0315/onnx/backend/test/case/node/round.py000066400000000000000000000024521511334557700227630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Round(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Round", inputs=["x"], outputs=["y"], ) x = np.array( [ 0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8, ] ).astype(np.float32) # expected output y = np.array( [ 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, -1.0, -2.0, -2.0, -2.0, -2.0, -3.0, ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_round") onnx-onnx-bca0315/onnx/backend/test/case/node/scan.py000066400000000000000000000103301511334557700225520ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Scan(Base): @staticmethod def export_scan_8() -> None: # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node no_sequence_lens = "" # optional input, not supplied node = onnx.helper.make_node( "Scan", inputs=[no_sequence_lens, "initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for batch-size 1, sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((1, 2)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((1, 2)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan_sum", opset_imports=[onnx.helper.make_opsetid("", 8)], ) @staticmethod def export_scan_9() -> None: # Given an input sequence [x1, ..., xN], sum up its elements using a scan # returning the final state (x1+x2+...+xN) as well the scan_output # [x1, x1+x2, ..., x1+x2+...+xN] # # create graph to represent scan body sum_in = onnx.helper.make_tensor_value_info( "sum_in", onnx.TensorProto.FLOAT, [2] ) next_ = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2]) sum_out = onnx.helper.make_tensor_value_info( "sum_out", onnx.TensorProto.FLOAT, [2] ) scan_out = onnx.helper.make_tensor_value_info( "scan_out", onnx.TensorProto.FLOAT, [2] ) add_node = onnx.helper.make_node( "Add", inputs=["sum_in", "next"], outputs=["sum_out"] ) id_node = onnx.helper.make_node( "Identity", inputs=["sum_out"], outputs=["scan_out"] ) scan_body = onnx.helper.make_graph( [add_node, id_node], "scan_body", [sum_in, next_], [sum_out, scan_out] ) # create scan op node node = onnx.helper.make_node( "Scan", inputs=["initial", "x"], outputs=["y", "z"], num_scan_inputs=1, body=scan_body, ) # create inputs for sequence-length 3, inner dimension 2 initial = np.array([0, 0]).astype(np.float32).reshape((2,)) x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2)) # final state computed = [1 + 3 + 5, 2 + 4 + 6] y = np.array([9, 12]).astype(np.float32).reshape((2,)) # scan-output computed z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2)) expect( node, inputs=[initial, x], outputs=[y, z], name="test_scan9_sum", opset_imports=[onnx.helper.make_opsetid("", 9)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/scatter.py000066400000000000000000000063251511334557700233040ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx import helper from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # The below Scatter's numpy implementation is from https://stackoverflow.com/a/46204790/11767360 def scatter(data, indices, updates, axis=0): # type: ignore if axis < 0: axis = data.ndim + axis idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :] def make_slice(arr, axis, i): # type: ignore slc = [slice(None)] * arr.ndim slc[axis] = i return slc def unpack(packed): # type: ignore unpacked = packed[0] for i in range(1, len(packed)): unpacked = unpacked, packed[i] return unpacked # We use indices and axis parameters to create idx # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data idx = [ [ unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)), indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0], ] for i in range(indices.shape[axis]) ] idx = list(np.concatenate(idx, axis=1)) idx.insert(axis, idx.pop()) # updates_idx is a NumPy advanced indices for indexing of elements in the updates updates_idx = list(idx) updates_idx.pop(axis) updates_idx.insert( axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape)) ) scattered = np.copy(data) scattered[tuple(idx)] = updates[tuple(updates_idx)] return scattered class Scatter(Base): @staticmethod def export_scatter_without_axis() -> None: node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_without_axis", opset_imports=[helper.make_opsetid("", 10)], ) @staticmethod def export_scatter_with_axis() -> None: axis = 1 node = onnx.helper.make_node( "Scatter", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter(data, indices, updates, axis=axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_with_axis", opset_imports=[helper.make_opsetid("", 10)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/scatterelements.py000066400000000000000000000165171511334557700250450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # The below ScatterElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360 def scatter_elements(data, indices, updates, axis=0, reduction="none"): # type: ignore if axis < 0: axis = data.ndim + axis idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :] def make_slice(arr, axis, i): # type: ignore slc = [slice(None)] * arr.ndim slc[axis] = i return slc def unpack(packed): # type: ignore unpacked = packed[0] for i in range(1, len(packed)): unpacked = unpacked, packed[i] return unpacked def make_indices_for_duplicate(idx): # type: ignore final_idx = [] for i in range(len(idx[0])): final_idx.append( # noqa: PERF401 tuple(idx_element[i] for idx_element in idx) ) return list(final_idx) # We use indices and axis parameters to create idx # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data idx = [ [ unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)), indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0], ] for i in range(indices.shape[axis]) ] idx = list(np.concatenate(idx, axis=1)) idx.insert(axis, idx.pop()) # updates_idx is a NumPy advanced indices for indexing of elements in the updates updates_idx = list(idx) updates_idx.pop(axis) updates_idx.insert( axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape)) ) scattered = np.copy(data) if reduction == "none": scattered[tuple(idx)] = updates[tuple(updates_idx)] else: idx, updates_idx = ( make_indices_for_duplicate(idx), make_indices_for_duplicate(updates_idx), ) for iter_, idx_set in enumerate(idx): if reduction == "add": scattered[idx_set] += updates[updates_idx[iter_]] elif reduction == "mul": scattered[idx_set] *= updates[updates_idx[iter_]] elif reduction == "max": scattered[idx_set] = np.maximum( scattered[idx_set], updates[updates_idx[iter_]] ) elif reduction == "min": scattered[idx_set] = np.minimum( scattered[idx_set], updates[updates_idx[iter_]] ) return scattered class ScatterElements(Base): @staticmethod def export_scatter_elements_without_axis() -> None: node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.zeros((3, 3), dtype=np.float32) indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64) updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32) y = scatter_elements(data, indices, updates) # print(y) produces # [[2.0, 1.1, 0.0], # [1.0, 0.0, 2.2], # [0.0, 2.1, 1.2]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_without_axis", ) @staticmethod def export_scatter_elements_with_axis() -> None: axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 3.0, 2.1, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_axis", ) @staticmethod def export_scatter_elements_with_negative_indices() -> None: axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, -3]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis) # print(y) produces # [[1.0, 1.1, 2.1, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_negative_indices", ) @staticmethod def export_scatter_elements_with_duplicate_indices() -> None: axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="add", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="add") # print(y) produces # [[1.0, 5.2, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_duplicate_indices", ) @staticmethod def export_scatter_elements_with_reduction_max() -> None: axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="max", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="max") # print(y) produces # [[1.0, 2.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_max", ) @staticmethod def export_scatter_elements_with_reduction_min() -> None: axis = 1 node = onnx.helper.make_node( "ScatterElements", inputs=["data", "indices", "updates"], outputs=["y"], axis=axis, reduction="min", ) data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) indices = np.array([[1, 1]], dtype=np.int64) updates = np.array([[1.1, 2.1]], dtype=np.float32) y = scatter_elements(data, indices, updates, axis, reduction="min") # print(y) produces # [[1.0, 1.1, 3.0, 4.0, 5.0]] expect( node, inputs=[data, indices, updates], outputs=[y], name="test_scatter_elements_with_reduction_min", ) onnx-onnx-bca0315/onnx/backend/test/case/node/scatternd.py000066400000000000000000000207461511334557700236310ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def scatter_nd_impl(data, indices, updates, reduction="none"): # type: ignore # Check tensor shapes assert indices.shape[-1] <= len(data.shape) assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1] :] # Compute output output = np.copy(data) for i in np.ndindex(indices.shape[:-1]): # NOTE: The order of iteration in this loop is not specified. if reduction == "add": output[tuple(indices[i])] += updates[i] elif reduction == "mul": output[tuple(indices[i])] *= updates[i] elif reduction == "max": output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i]) elif reduction == "min": output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i]) else: output[tuple(indices[i])] = updates[i] return output class ScatterND(Base): @staticmethod def export_scatternd() -> None: node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [2]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates) expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd", ) @staticmethod def export_scatternd_add() -> None: node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="add", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="add") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_add", ) @staticmethod def export_scatternd_multiply() -> None: node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="mul", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="mul") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_multiply", ) @staticmethod def export_scatternd_max() -> None: node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="max", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="max") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_max", ) @staticmethod def export_scatternd_min() -> None: node = onnx.helper.make_node( "ScatterND", inputs=["data", "indices", "updates"], outputs=["y"], reduction="min", ) data = np.array( [ [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], ], dtype=np.float32, ) indices = np.array([[0], [0]], dtype=np.int64) updates = np.array( [ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], ], dtype=np.float32, ) # Expecting output as np.array( # [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]], # [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], # [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], # [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32) output = scatter_nd_impl(data, indices, updates, reduction="min") expect( node, inputs=[data, indices, updates], outputs=[output], name="test_scatternd_min", ) onnx-onnx-bca0315/onnx/backend/test/case/node/selu.py000066400000000000000000000030001511334557700225720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Selu(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0 ) x = np.array([-1, 0, 1]).astype(np.float32) # expected output [-3.79272318, 0., 3.] y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * 3.0 + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0 ) expect(node, inputs=[x], outputs=[y], name="test_selu") @staticmethod def export_selu_default() -> None: default_alpha = 1.67326319217681884765625 default_gamma = 1.05070102214813232421875 node = onnx.helper.make_node( "Selu", inputs=["x"], outputs=["y"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = ( np.clip(x, 0, np.inf) * default_gamma + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma ) expect(node, inputs=[x], outputs=[y], name="test_selu_default") onnx-onnx-bca0315/onnx/backend/test/case/node/sequence_map.py000077500000000000000000000253101511334557700243020ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class SequenceMap(Base): @staticmethod def export_sequence_map_identity_1_sequence(): # type: () -> None body = onnx.helper.make_graph( [onnx.helper.make_node("Identity", ["in0"], ["out0"])], "seq_map_body", [onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x"], outputs=["y"], body=body ) x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)] y = x input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x], outputs=[y], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence", ) @staticmethod def export_sequence_map_identity_2_sequences(): # type: () -> None body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] y0 = x0 y1 = x1 input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_2_sequences", ) @staticmethod def export_sequence_map_identity_1_sequence_1_tensor(): # type: () -> None body = onnx.helper.make_graph( [ onnx.helper.make_node("Identity", ["in0"], ["out0"]), onnx.helper.make_node("Identity", ["in1"], ["out1"]), ], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["M"] ), ], [ onnx.helper.make_tensor_value_info( "out0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "out1", onnx.TensorProto.FLOAT, ["M"] ), ], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body ) x0 = [ np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) for _ in range(3) ] x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32) y0 = x0 y1 = [x1 for _ in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0, y1], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_identity_1_sequence_1_tensor", ) @staticmethod def export_sequence_map_add_2_sequences(): # type: () -> None body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) N = [np.random.randint(1, 10) for _ in range(3)] x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)] y0 = [x0[k] + x1[k] for k in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_2_sequences", ) @staticmethod def export_sequence_map_add_1_sequence_1_tensor(): # type: () -> None body = onnx.helper.make_graph( [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "in0", onnx.TensorProto.FLOAT, ["N"] ), onnx.helper.make_tensor_value_info( "in1", onnx.TensorProto.FLOAT, ["N"] ), ], [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body ) x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)] x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32) y0 = [x0[i] + x1 for i in range(3)] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]) ), ] expect( node, inputs=[x0, x1], outputs=[y0], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_add_1_sequence_1_tensor", ) @staticmethod def export_sequence_map_extract_shapes(): # type: () -> None body = onnx.helper.make_graph( [onnx.helper.make_node("Shape", ["x"], ["shape"])], "seq_map_body", [ onnx.helper.make_tensor_value_info( "x", onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ], [onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])], ) node = onnx.helper.make_node( "SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body ) shapes = [ np.array([40, 30, 3], dtype=np.int64), np.array([20, 10, 3], dtype=np.int64), np.array([10, 5, 3], dtype=np.int64), ] x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes] input_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto( onnx.TensorProto.FLOAT, ["H", "W", "C"] ) ), ] output_type_protos = [ onnx.helper.make_sequence_type_proto( onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3]) ), ] expect( node, inputs=[x0], outputs=[shapes], input_type_protos=input_type_protos, output_type_protos=output_type_protos, name="test_sequence_map_extract_shapes", ) onnx-onnx-bca0315/onnx/backend/test/case/node/sequenceinsert.py000066400000000000000000000050351511334557700246710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def sequence_insert_reference_implementation( sequence: list[Any], tensor: np.ndarray, position: np.ndarray = None ) -> list[Any]: # make a copy of input sequence seq = list(sequence) if position is not None: # In these cases, insert_position will be between [-len(sequence), len(sequence)] # The position argument will be in the format np.array([pos_index]) insert_position = position[0] seq.insert(insert_position, tensor) else: # Default position of insertion is at the end of the sequence. seq.append(tensor) return seq class SequenceInsert(Base): @staticmethod def export() -> None: test_cases = { "at_back": [np.array([10, 11, 12]).astype(np.int64)], "at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)], } sequence = [ np.array([1, 2, 3, 4]).astype(np.int64), np.array([5, 6, 7]).astype(np.int64), np.array([8, 9]).astype(np.int64), ] for test_name, test_inputs in test_cases.items(): tensor = test_inputs[0].astype(np.int64) if len(test_inputs) > 1: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor", "position"], outputs=["output_sequence"], ) position = test_inputs[1] inserted = sequence_insert_reference_implementation( sequence, tensor, position ) expect( node, inputs=[sequence, tensor, position], outputs=[inserted], name="test_sequence_insert_" + test_name, ) else: node = onnx.helper.make_node( "SequenceInsert", inputs=["sequence", "tensor"], outputs=["output_sequence"], ) inserted = sequence_insert_reference_implementation(sequence, tensor) expect( node, inputs=[sequence, tensor], outputs=[inserted], name="test_sequence_insert_" + test_name, ) onnx-onnx-bca0315/onnx/backend/test/case/node/shape.py000066400000000000000000000031011511334557700227240ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect # Reference implementation of shape op def shape_reference_impl(x, start=None, end=None): # type: ignore dims = x.shape[start:end] return np.array(dims).astype(np.int64) def test_shape(testname, xval, start=None, end=None): # type: ignore node = onnx.helper.make_node( "Shape", inputs=["x"], outputs=["y"], start=start, end=end ) yval = shape_reference_impl(xval, start, end) expect(node, inputs=[xval], outputs=[yval], name="test_shape" + testname) class Shape(Base): @staticmethod def export() -> None: x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) test_shape("_example", x) # preserve names of original test cases x = np.random.randn(3, 4, 5).astype(np.float32) test_shape("", x) # preserve names of original test cases test_shape("_start_1", x, start=1) test_shape("_end_1", x, end=1) test_shape("_start_negative_1", x, start=-1) test_shape("_end_negative_1", x, end=-1) test_shape("_start_1_end_negative_1", x, start=1, end=-1) test_shape("_start_1_end_2", x, start=1, end=2) test_shape("_clip_start", x, start=-10) test_shape("_clip_end", x, end=10) test_shape("_start_greater_than_end", x, start=2, end=1) onnx-onnx-bca0315/onnx/backend/test/case/node/shrink.py000066400000000000000000000020351511334557700231270ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Shrink(Base): @staticmethod def export_hard_shrink() -> None: node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard") @staticmethod def export_soft_shrink() -> None: node = onnx.helper.make_node( "Shrink", inputs=["x"], outputs=["y"], lambd=1.5, bias=1.5, ) X = np.arange(-2.0, 2.1, dtype=np.float32) Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32) expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft") onnx-onnx-bca0315/onnx/backend/test/case/node/sigmoid.py000066400000000000000000000015251511334557700232670ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sigmoid(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sigmoid", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = 1.0 / ( 1.0 + np.exp(np.negative(x)) ) # expected output [0.26894143, 0.5, 0.7310586] expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = 1.0 / (1.0 + np.exp(np.negative(x))) expect(node, inputs=[x], outputs=[y], name="test_sigmoid") onnx-onnx-bca0315/onnx/backend/test/case/node/sign.py000066400000000000000000000010711511334557700225700ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sign(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sign", inputs=["x"], outputs=["y"], ) x = np.array(range(-5, 6)).astype(np.float32) y = np.sign(x) expect(node, inputs=[x], outputs=[y], name="test_sign") onnx-onnx-bca0315/onnx/backend/test/case/node/sin.py000066400000000000000000000013111511334557700224160ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sin(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sin", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sin(x) expect(node, inputs=[x], outputs=[y], name="test_sin") onnx-onnx-bca0315/onnx/backend/test/case/node/sinh.py000066400000000000000000000014021511334557700225670ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sinh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sinh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.sinh(x) # expected output [-1.17520118, 0., 1.17520118] expect(node, inputs=[x], outputs=[y], name="test_sinh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.sinh(x) expect(node, inputs=[x], outputs=[y], name="test_sinh") onnx-onnx-bca0315/onnx/backend/test/case/node/size.py000066400000000000000000000015131511334557700226030ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Size(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Size", inputs=["x"], outputs=["y"], ) x = np.array( [ [1, 2, 3], [4, 5, 6], ] ).astype(np.float32) y = np.array(6).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.array(x.size).astype(np.int64) expect(node, inputs=[x], outputs=[y], name="test_size") onnx-onnx-bca0315/onnx/backend/test/case/node/slice.py000066400000000000000000000122261511334557700227330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Slice(Base): @staticmethod def export_slice() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) y = x[0:3, 0:10] starts = np.array([0, 0], dtype=np.int64) ends = np.array([3, 10], dtype=np.int64) axes = np.array([0, 1], dtype=np.int64) steps = np.array([1, 1], dtype=np.int64) expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice" ) @staticmethod def export_slice_neg() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0], dtype=np.int64) ends = np.array([-1], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 0:-1] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg", ) @staticmethod def export_slice_start_out_of_bounds() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1000], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1000:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_start_out_of_bounds", ) @staticmethod def export_slice_end_out_of_bounds() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([1], dtype=np.int64) ends = np.array([1000], dtype=np.int64) axes = np.array([1], dtype=np.int64) steps = np.array([1], dtype=np.int64) y = x[:, 1:1000] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_end_out_of_bounds", ) @staticmethod def export_slice_default_axes() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes" ) @staticmethod def export_slice_default_steps() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_default_steps", ) @staticmethod def export_slice_neg_steps() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes", "steps"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([20, 10, 4], dtype=np.int64) ends = np.array([0, 0, 1], dtype=np.int64) axes = np.array([0, 1, 2], dtype=np.int64) steps = np.array([-1, -3, -2]).astype(np.int64) y = x[20:0:-1, 10:0:-3, 4:1:-2] expect( node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice_neg_steps", ) @staticmethod def export_slice_negative_axes() -> None: node = onnx.helper.make_node( "Slice", inputs=["x", "starts", "ends", "axes"], outputs=["y"], ) x = np.random.randn(20, 10, 5).astype(np.float32) starts = np.array([0, 0, 3], dtype=np.int64) ends = np.array([20, 10, 4], dtype=np.int64) axes = np.array([0, -2, -1], dtype=np.int64) y = x[:, :, 3:4] expect( node, inputs=[x, starts, ends, axes], outputs=[y], name="test_slice_negative_axes", ) onnx-onnx-bca0315/onnx/backend/test/case/node/softmax.py000066400000000000000000000051761511334557700233230ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray: x_max = np.max(x, axis=axis, keepdims=True) tmp = np.exp(x - x_max) s = np.sum(tmp, axis=axis, keepdims=True) return tmp / s class Softmax(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) x = np.array([[-1, 0, 1]]).astype(np.float32) # expected output [[0.09003058, 0.24472848, 0.66524094]] y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_example") @staticmethod def export_softmax_axis() -> None: x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32) # expected output # [[0.032058604 0.08714432 0.23688284 0.6439143 ] # [0.032058604 0.08714432 0.23688284 0.6439143 ]] y = softmax(x) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=0, ) y = softmax(x, axis=0) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=1, ) y = softmax(x, axis=1) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=2, ) y = softmax(x, axis=2) expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2") node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], axis=-1, ) y = softmax(x, axis=-1) expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis") # default axis is -1 node = onnx.helper.make_node( "Softmax", inputs=["x"], outputs=["y"], ) expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis") onnx-onnx-bca0315/onnx/backend/test/case/node/softmaxcrossentropy.py000066400000000000000000001053201511334557700260060ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def softmaxcrossentropy( x, target, weight=None, reduction="mean", ignore_index=None, get_log_prob=None ): # type: ignore input_shape = x.shape if len(input_shape) == 1: raise RuntimeError("Unsupported shape") target_shape = target.shape N = input_shape[0] C = input_shape[1] # compute log_softmax max_x = np.max(x, axis=1, keepdims=True) exp_x = np.exp(x - max_x) p = exp_x / np.sum(exp_x, axis=1, keepdims=True) inp = np.log(p) log_prob = None if get_log_prob is True: log_prob = np.copy(inp) # initialize the positional weights when required gather_weight = None if weight is not None: # setting mode='clip' to deal with ignore_index > C or < 0 cases. # when the target value is > C or < 0, it doesn't matter which value we are # taking in gather_weight, since it will be set to 0 in the following if-block # use np.int32 to make it compatible with x86 machines gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip") # set `ignore_index`'s loss weight to 0. # The loss tensor will be multiplied by this weight tensor, # so `ignore_index`'s loss value will be eliminated. if ignore_index is not None: gather_weight = np.where(target == ignore_index, 0, gather_weight).astype( dtype=np.float32 ) elif ignore_index is not None: gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32) # if input is 4-d and above, make it 3-d if len(input_shape) != 3: inp = inp.reshape((N, C, -1)) target = target.reshape((N, -1)) # Get a dimension from the reshaped input. # If the original input shape is [N, C, H, W], # the D here should be H * W because we reshape # [N, C, H, W] to [N, C, H * W]. D = inp.shape[2] neg_gather_element_input = np.zeros((N, D), dtype=np.float32) for i in range(N): for d in range(D): if target[i][d] != ignore_index: neg_gather_element_input[i][d] = -inp[i][target[i][d]][d] loss = neg_gather_element_input # if the input was 4-d or above reshape to the right shape if len(input_shape) != 3: loss = loss.reshape(target_shape) # apply the weights when required if gather_weight is not None: loss = gather_weight * loss if reduction == "mean": loss = loss.sum() / gather_weight.sum() if get_log_prob is True: return loss, log_prob return loss if reduction == "mean": loss = np.mean(loss) elif reduction == "sum": loss = np.sum(loss) if get_log_prob: return loss, log_prob return loss class SoftmaxCrossEntropyLoss(Base): @staticmethod def export_softmaxcrossentropy_none() -> None: # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="none") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none") @staticmethod def export_softmaxcrossentropy_none_log_prob() -> None: # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_none_log_prob", ) @staticmethod def export_softmaxcrossentropy_none_weights() -> None: # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none") # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_none_weights", ) @staticmethod def export_softmaxcrossentropy_none_weights_log_prob() -> None: # Define operator attributes. reduction = "none" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, reduction="none", get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_none_weights_log_prob", ) @staticmethod def export_softmaxcrossentropy_sum() -> None: # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, reduction="sum") # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum") @staticmethod def export_softmaxcrossentropy_sum_log_prob() -> None: # Define operator attributes. reduction = "sum" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction="sum", get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_sum_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels) # Check results expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean") @staticmethod def export_softmaxcrossentropy_mean_log_prob() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_3d() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, y) # Check results expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d") @staticmethod def export_softmaxcrossentropy_mean_3d_log_prob() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True) # Check results expect( node, inputs=[x, y], outputs=[loss, log_prob], name="test_sce_mean_3d_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_weights() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight", ) @staticmethod def export_softmaxcrossentropy_mean_weights_log_prob() -> None: # Define operator attributes. reduction = "mean" # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(0) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(0) weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii" ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5).astype(np.float32) labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64) labels[0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii_3d() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_3d", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii_3d_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(1) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(1) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_3d_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii_3d() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_3d", ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii_3d_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64) labels[0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_3d_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii_4d() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels, weights], outputs=[sce], name="test_sce_mean_weight_ii_4d", ) @staticmethod def export_softmaxcrossentropy_mean_weights_ii_4d_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index, get_log_prob=True, ) # Check results expect( node, inputs=[x, labels, weights], outputs=[loss, log_prob], name="test_sce_mean_weight_ii_4d_log_prob", ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii_4d() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) # Check results expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii_4d", ) @staticmethod def export_softmaxcrossentropy_mean_no_weights_ii_4d_log_prob() -> None: # Define operator attributes. reduction = "mean" ignore_index = np.int64(2) # Create operator. node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) # Define operator inputs. np.random.seed(0) x = np.random.rand(3, 5, 2, 7).astype(np.float32) labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64) labels[0][0][0] = np.int64(2) # Compute SoftmaxCrossEntropyLoss loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) # Check results expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_mean_no_weight_ii_4d_log_prob", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None: reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3d4d5_mean_weight", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() -> None: reduction = "mean" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None: reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) sce = softmaxcrossentropy(x, labels, reduction=reduction) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3d4d5_none_no_weight", ) @staticmethod def export_input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob() -> None: reduction = "none" node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ) N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32) labels = np.random.randint( 0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5) ).astype(np.int64) loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob", ) @staticmethod def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None: reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1_mean_weight_negative_ii", ) @staticmethod def export_input_shape_is_NCd1_mean_weight_negative_ii_log_prob() -> None: reduction = "mean" ignore_index = np.int64(-1) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1 = 3, 5, 6 np.random.seed(0) x = np.random.rand(N, C, dim1).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64) labels[0][0] = -1 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1_mean_weight_negative_ii_log_prob", ) @staticmethod def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None: reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 sce = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels], outputs=[sce], name="test_sce_NCd1d2d3_none_no_weight_negative_ii", ) @staticmethod def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob() -> None: reduction = "none" ignore_index = np.int64(-5) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5 np.random.seed(0) x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32) labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype( np.int64 ) labels[0][0][0][0] = -5 loss, log_prob = softmaxcrossentropy( x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True ) expect( node, inputs=[x, labels], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob", ) @staticmethod def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None: reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) sce = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index ) expect( node, inputs=[x, labels, weight], outputs=[sce], name="test_sce_NCd1d2d3_sum_weight_high_ii", ) @staticmethod def export_input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob() -> None: reduction = "sum" ignore_index = np.int64(10) node = onnx.helper.make_node( "SoftmaxCrossEntropyLoss", inputs=["x", "y", "w"], outputs=["z", "log_prob"], reduction=reduction, ignore_index=ignore_index, ) N, C = 3, 5 np.random.seed(0) x = np.random.rand(N, C).astype(np.float32) labels = np.random.randint(0, high=C, size=(N)).astype(np.int64) labels[0] = 10 weight = np.random.rand(C).astype(np.float32) loss, log_prob = softmaxcrossentropy( x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index, get_log_prob=True, ) expect( node, inputs=[x, labels, weight], outputs=[loss, log_prob], name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob", ) onnx-onnx-bca0315/onnx/backend/test/case/node/softplus.py000066400000000000000000000015031511334557700235070ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Softplus(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Softplus", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.log( np.exp(x) + 1 ) # expected output [0.31326166, 0.69314718, 1.31326163] expect(node, inputs=[x], outputs=[y], name="test_softplus_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.log(np.exp(x) + 1) expect(node, inputs=[x], outputs=[y], name="test_softplus") onnx-onnx-bca0315/onnx/backend/test/case/node/softsign.py000066400000000000000000000014111511334557700234620ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Softsign(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Softsign", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.array([-0.5, 0, 0.5]).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_softsign_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = x / (1 + np.abs(x)) expect(node, inputs=[x], outputs=[y], name="test_softsign") onnx-onnx-bca0315/onnx/backend/test/case/node/spacetodepth.py000066400000000000000000000036231511334557700243200ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class SpaceToDepth(Base): @staticmethod def export() -> None: b, c, h, w = shape = (2, 2, 6, 6) blocksize = 2 node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=blocksize, ) x = np.random.random_sample(shape).astype(np.float32) tmp = np.reshape( x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize] ) tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4]) y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize]) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth") @staticmethod def export_example() -> None: node = onnx.helper.make_node( "SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=2, ) # (1, 1, 4, 6) input tensor x = np.array( [ [ [ [0, 6, 1, 7, 2, 8], [12, 18, 13, 19, 14, 20], [3, 9, 4, 10, 5, 11], [15, 21, 16, 22, 17, 23], ] ] ] ).astype(np.float32) # (1, 4, 2, 3) output tensor y = np.array( [ [ [[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]], [[12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23]], ] ] ).astype(np.float32) expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example") onnx-onnx-bca0315/onnx/backend/test/case/node/split.py000066400000000000000000000271051511334557700227710ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Split(Base): @staticmethod def export_1d_opset13() -> None: node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) @staticmethod def export_2d_opset13() -> None: node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1 ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) @staticmethod def export_default_values_opset13() -> None: node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) @staticmethod def export_zero_size_splits_opset13() -> None: # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset13", opset_imports=[onnx.helper.make_opsetid("", 13)], ) @staticmethod def export_1d_opset18() -> None: node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=0, num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_1d_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=0, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_1d_opset18", ) @staticmethod def export_2d_opset18() -> None: node_input = np.array( [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1, num_outputs=2, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_2d", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"], axis=1, ) expected_outputs = [ np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32), np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype( np.float32 ), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_2d_opset18", ) @staticmethod def export_default_values_opset18() -> None: node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], num_outputs=3, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_equal_parts_default_axis_opset18", ) split = np.array([2, 4]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2"] ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_variable_parts_default_axis_opset18", ) @staticmethod def export_zero_size_splits_opset18() -> None: # 1-dimensional tensor with dimension_size=0 node_input = np.array([]).astype(np.float32) # Split empty tensor to tensors of size zero split = np.array([0, 0, 0]).astype(np.int64) node = onnx.helper.make_node( "Split", inputs=["input", "split"], outputs=["output_1", "output_2", "output_3"], ) expected_outputs = [ np.array([]).astype(np.float32), np.array([]).astype(np.float32), np.array([]).astype(np.float32), ] expect( node, inputs=[node_input, split], outputs=expected_outputs, name="test_split_zero_size_splits_opset18", ) @staticmethod def export_1d_uneven_split_opset18() -> None: node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32) # If axis is not specified, split is applied on default axis 0 node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3", "output_4"], num_outputs=4, ) expected_outputs = [ np.array([1.0, 2.0]).astype(np.float32), np.array([3.0, 4.0]).astype(np.float32), np.array([5.0, 6.0]).astype(np.float32), np.array([7.0]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_1d_uneven_split_opset18", ) @staticmethod def export_2d_uneven_split_opset18() -> None: node_input = np.array( [ [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0], ] ).astype(np.float32) node = onnx.helper.make_node( "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"], axis=1, num_outputs=3, ) expected_outputs = [ np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32), np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32), np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32), ] expect( node, inputs=[node_input], outputs=expected_outputs, name="test_split_2d_uneven_split_opset18", ) onnx-onnx-bca0315/onnx/backend/test/case/node/splittosequence.py000066400000000000000000000041211511334557700250560ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class SplitToSequence(Base): @staticmethod def export_with_split_1() -> None: data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array(2, dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=1 ) expected_outputs = [ [ np.array([[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]], dtype=np.float32), np.array([[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]], dtype=np.float32), np.array([[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]], dtype=np.float32), ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_1", ) @staticmethod def export_with_split_2() -> None: data = np.arange(18).reshape((3, 6)).astype(np.float32) split = np.array([1, 2], dtype=np.int64) node = onnx.helper.make_node( "SplitToSequence", ["data", "split"], ["seq"], axis=0 ) expected_outputs = [ [ data[:1], data[1:], ] ] expect( node, inputs=[data, split], outputs=expected_outputs, name="test_split_to_sequence_2", ) @staticmethod def export_nokeepdims() -> None: data = np.arange(18).reshape((3, 6)).astype(np.float32) node = onnx.helper.make_node( "SplitToSequence", ["data"], ["seq"], axis=1, keepdims=0, ) expected_outputs = [[data[:, i] for i in range(data.shape[1])]] expect( node, inputs=[data], outputs=expected_outputs, name="test_split_to_sequence_nokeepdims", ) onnx-onnx-bca0315/onnx/backend/test/case/node/sqrt.py000066400000000000000000000013661511334557700226300ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sqrt(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sqrt", inputs=["x"], outputs=["y"], ) x = np.array([1, 4, 9]).astype(np.float32) y = np.sqrt(x) # expected output [1., 2., 3.] expect(node, inputs=[x], outputs=[y], name="test_sqrt_example") x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) y = np.sqrt(x) expect(node, inputs=[x], outputs=[y], name="test_sqrt") onnx-onnx-bca0315/onnx/backend/test/case/node/squeeze.py000066400000000000000000000020771511334557700233200ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Squeeze(Base): @staticmethod def export_squeeze() -> None: node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 4, 5).astype(np.float32) axes = np.array([0], dtype=np.int64) y = np.squeeze(x, axis=0) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze") @staticmethod def export_squeeze_negative_axes() -> None: node = onnx.helper.make_node( "Squeeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2], dtype=np.int64) y = np.squeeze(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes") onnx-onnx-bca0315/onnx/backend/test/case/node/stft.py000066400000000000000000000045401511334557700226140ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class STFT(Base): @staticmethod def export() -> None: signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1) length = np.array(16).astype(np.int64) onesided_length = (length >> 1) + 1 step = np.array(8).astype(np.int64) no_window = "" # optional input, not supplied node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", no_window, "frame_length"], outputs=["output"], ) nstfts = ((signal.shape[1] - length) // step) + 1 # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) output = output.astype(signal.dtype) expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft") node = onnx.helper.make_node( "STFT", inputs=["signal", "frame_step", "window"], outputs=["output"], ) # Test with window a0 = 0.5 a1 = 0.5 window = a0 + a1 * np.cos( 2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length ) nstfts = 1 + (signal.shape[1] - window.shape[0]) // step # [batch_size][frames][frame_length][2] output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32) for i in range(nstfts): start = i * step stop = i * step + length complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[ 0:onesided_length ] output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1) window = window.astype(signal.dtype) output = output.astype(signal.dtype) expect( node, inputs=[signal, step, window], outputs=[output], name="test_stft_with_window", ) onnx-onnx-bca0315/onnx/backend/test/case/node/string_concat.py000066400000000000000000000036741511334557700245000ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class StringConcat(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "StringConcat", inputs=["x", "y"], outputs=["result"], ) x = np.array(["abc", "def"]).astype("object") y = np.array([".com", ".net"]).astype("object") result = np.array(["abc.com", "def.net"]).astype("object") expect(node, inputs=[x, y], outputs=[result], name="test_string_concat") x = np.array(["cat", "dog", "snake"]).astype("object") y = np.array(["s"]).astype("object") result = np.array(["cats", "dogs", "snakes"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_broadcasting", ) x = np.array("cat").astype("object") y = np.array("s").astype("object") result = np.array("cats").astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_zero_dimensional", ) x = np.array(["abc", ""]).astype("object") y = np.array(["", "abc"]).astype("object") result = np.array(["abc", "abc"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_empty_string", ) x = np.array(["įš„", "中"]).astype("object") y = np.array(["įš„", "中"]).astype("object") result = np.array(["įš„įš„", "中中"]).astype("object") expect( node, inputs=[x, y], outputs=[result], name="test_string_concat_utf8", ) onnx-onnx-bca0315/onnx/backend/test/case/node/string_split.py000066400000000000000000000100401511334557700243450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class StringSplit(Base): @staticmethod def export_basic() -> None: node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=".", maxsplit=None, ) x = np.array(["abc.com", "def.net"]).astype(object) substrings = np.array([["abc", "com"], ["def", "net"]]).astype(object) length = np.array([2, 2], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_basic", ) @staticmethod def export_maxsplit() -> None: node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], maxsplit=2, ) x = np.array( [["hello world", "def.net"], ["o n n x", "the quick brown fox"]] ).astype(object) substrings = np.array( [ [["hello", "world", ""], ["def.net", "", ""]], [["o", "n", "n x"], ["the", "quick", "brown fox"]], ] ).astype(object) length = np.array([[2, 1], [3, 3]], np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_maxsplit", ) @staticmethod def export_consecutive_delimiters() -> None: node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter="-", maxsplit=None, ) x = np.array(["o-n-n--x-", "o-n----nx"]).astype(object) substrings = np.array( [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]] ).astype(object) length = np.array([6, 6], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_consecutive_delimiters", ) @staticmethod def export_empty_string_delimiter() -> None: for delimiter, test_name in ( ("", "test_string_split_empty_string_delimiter"), (None, "test_string_split_no_delimiter"), ): node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=delimiter, maxsplit=None, ) x = np.array( ["hello world !", " hello world !", " hello world ! "] ).astype(object) substrings = np.array( [ ["hello", "world", "!"], ["hello", "world", "!"], ["hello", "world", "!"], ] ).astype(object) length = np.array([3, 3, 3], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name=test_name, ) @staticmethod def export_empty_string_split() -> None: node = onnx.helper.make_node( "StringSplit", inputs=["x"], outputs=["substrings", "length"], delimiter=None, maxsplit=None, ) x = np.array([]).astype(object) substrings = np.array([]).astype(object).reshape(0, 0) length = np.array([], dtype=np.int64) expect( node, inputs=[x], outputs=[substrings, length], name="test_string_split_empty_tensor", output_type_protos=[ onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, (0, None)), None, ], ) onnx-onnx-bca0315/onnx/backend/test/case/node/stringnormalizer.py000066400000000000000000000105141511334557700252430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class StringNormalizer(Base): @staticmethod def export_nostopwords_nochangecase() -> None: input = np.array(["monday", "tuesday"]).astype(object) output = input # No stopwords. This is a NOOP node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_nostopwords_nochangecase", ) @staticmethod def export_monday_casesensintive_nochangecase() -> None: input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_nochangecase", ) @staticmethod def export_monday_casesensintive_lower() -> None: input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["tuesday", "wednesday", "thursday"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="LOWER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_lower", ) @staticmethod def export_monday_casesensintive_upper() -> None: input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object) output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_casesensintive_upper", ) @staticmethod def export_monday_empty_output() -> None: input = np.array(["monday", "monday"]).astype(object) output = np.array([""]).astype(object) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", is_case_sensitive=1, stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_empty_output", ) @staticmethod def export_monday_insensintive_upper_twodim() -> None: input = ( np.array( ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"] ) .astype(object) .reshape([1, 6]) ) # It does upper case cecedille, accented E # and german umlaut but fails # with german eszett output = ( np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"]) .astype(object) .reshape([1, 4]) ) stopwords = ["monday"] node = onnx.helper.make_node( "StringNormalizer", inputs=["x"], outputs=["y"], case_change_action="UPPER", stopwords=stopwords, ) expect( node, inputs=[input], outputs=[output], name="test_strnormalizer_export_monday_insensintive_upper_twodim", ) onnx-onnx-bca0315/onnx/backend/test/case/node/sub.py000066400000000000000000000050541511334557700224260ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sub(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.array([1, 2, 3]).astype(np.float32) y = np.array([3, 2, 1]).astype(np.float32) z = x - y # expected output [-2., 0., 2.] expect(node, inputs=[x, y], outputs=[z], name="test_sub_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(3, 4, 5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.int16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.int16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_int16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint8") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint16) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint16) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint16") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint32) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint32") x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint64) y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint64) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint64") @staticmethod def export_sub_broadcast() -> None: node = onnx.helper.make_node( "Sub", inputs=["x", "y"], outputs=["z"], ) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.random.randn(5).astype(np.float32) z = x - y expect(node, inputs=[x, y], outputs=[z], name="test_sub_bcast") onnx-onnx-bca0315/onnx/backend/test/case/node/sum.py000066400000000000000000000025011511334557700224330ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Sum(Base): @staticmethod def export() -> None: data_0 = np.array([3, 0, 2]).astype(np.float32) data_1 = np.array([1, 3, 4]).astype(np.float32) data_2 = np.array([2, 6, 6]).astype(np.float32) result = np.array([6, 9, 12]).astype(np.float32) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1", "data_2"], outputs=["result"], ) expect( node, inputs=[data_0, data_1, data_2], outputs=[result], name="test_sum_example", ) node = onnx.helper.make_node( "Sum", inputs=["data_0"], outputs=["result"], ) expect(node, inputs=[data_0], outputs=[data_0], name="test_sum_one_input") result = np.add(data_0, data_1) node = onnx.helper.make_node( "Sum", inputs=["data_0", "data_1"], outputs=["result"], ) expect( node, inputs=[data_0, data_1], outputs=[result], name="test_sum_two_inputs" ) onnx-onnx-bca0315/onnx/backend/test/case/node/swish.py000066400000000000000000000015231511334557700227670ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def swish(x: np.ndarray, alpha: float) -> np.ndarray: return x * (1 / (1 + np.exp(-alpha * x))) class Swish(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Swish", inputs=["x"], outputs=["y"], alpha=1.0, # pass alpha as attribute ) x = np.array([3, 4, 5], dtype=np.float32) y = swish(x, alpha=1.0) expect( node, inputs=[x], outputs=[y], name="test_swish", opset_imports=[onnx.helper.make_opsetid("", 24)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/tan.py000066400000000000000000000013111511334557700224070ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Tan(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Tan", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tan(x) expect(node, inputs=[x], outputs=[y], name="test_tan") onnx-onnx-bca0315/onnx/backend/test/case/node/tanh.py000066400000000000000000000014001511334557700225560ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Tanh(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Tanh", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = np.tanh(x) # expected output [-0.76159418, 0., 0.76159418] expect(node, inputs=[x], outputs=[y], name="test_tanh_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.tanh(x) expect(node, inputs=[x], outputs=[y], name="test_tanh") onnx-onnx-bca0315/onnx/backend/test/case/node/tensorscatter.py000066400000000000000000000121271511334557700245340ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class TensorScatter(Base): @staticmethod def export_tensorscatter() -> None: node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="linear", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [[[5, 5, 5, 5, 5]]], [[[1, 1, 1, 1, 1]]], ], dtype=np.float32, ) write_indices = np.array([1, 2], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [1, 1, 1, 1, 1], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter", ) @staticmethod def export_tensorscatter_circular() -> None: node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], mode="circular", ) past_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]], ], dtype=np.float32, ) update = np.array( [ [ [ [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], ] ], [ [ [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], ] ], ], dtype=np.float32, ) write_indices = np.array([1, 3], dtype=np.int64) present_cache = np.array( [ [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [4, 3, 2, 1, 0]]], [[[2, 2, 2, 2, 2], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [1, 1, 1, 1, 1]]], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_circular", ) @staticmethod def export_tensorscatter_3d() -> None: node = onnx.helper.make_node( "TensorScatter", inputs=["past_cache", "update", "write_indices"], outputs=["present_cache"], ) past_cache = np.array( [ [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) update = np.array( [ [ [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], ], [ [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], ], ], dtype=np.float32, ) write_indices = np.array([1, 2, 0], dtype=np.int64) present_cache = np.array( [ [ [1, 2, 3, 4, 5], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [5, 4, 3, 2, 1], ], [ [1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [6, 6, 6, 6, 6], [7, 7, 7, 7, 7], ], [ [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [8, 7, 6, 5, 4], [5, 4, 3, 2, 1], ], ], dtype=np.float32, ) expect( node, inputs=[past_cache, update, write_indices], outputs=[present_cache], name="test_tensorscatter_3d", ) onnx-onnx-bca0315/onnx/backend/test/case/node/tfidfvectorizer.py000066400000000000000000000212441511334557700250450ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from typing import Any import numpy as np import onnx from onnx import NodeProto from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class TfIdfVectorizerHelper: def __init__(self, **params: Any) -> None: # Attr names mode = "mode" min_gram_length = "min_gram_length" max_gram_length = "max_gram_length" max_skip_count = "max_skip_count" ngram_counts = "ngram_counts" ngram_indexes = "ngram_indexes" pool_int64s = "pool_int64s" required_attr = [ mode, min_gram_length, max_gram_length, max_skip_count, ngram_counts, ngram_indexes, pool_int64s, ] for i in required_attr: assert i in params, f"Missing attribute: {i}" self.mode = params[mode] self.min_gram_length = params[min_gram_length] self.max_gram_length = params[max_gram_length] self.max_skip_count = params[max_skip_count] self.ngram_counts = params[ngram_counts] self.ngram_indexes = params[ngram_indexes] self.pool_int64s = params[pool_int64s] def make_node_noweights(self) -> NodeProto: return onnx.helper.make_node( "TfIdfVectorizer", inputs=["X"], outputs=["Y"], mode=self.mode, min_gram_length=self.min_gram_length, max_gram_length=self.max_gram_length, max_skip_count=self.max_skip_count, ngram_counts=self.ngram_counts, ngram_indexes=self.ngram_indexes, pool_int64s=self.pool_int64s, ) class TfIdfVectorizer(Base): @staticmethod def export_tf_only_bigrams_skip0() -> None: input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_only_bigrams_skip0", ) @staticmethod def export_tf_batch_onlybigrams_skip0() -> None: input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0", ) @staticmethod def export_tf_onlybigrams_levelempty() -> None: input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([1.0, 1.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 0]).astype(np.int64) ngram_indexes = np.array([0, 1, 2]).astype(np.int64) pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype( # unigrams none np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=0, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_levelempty", ) @staticmethod def export_tf_onlybigrams_skip5() -> None: input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_onlybigrams_skip5", ) @staticmethod def export_tf_batch_onlybigrams_skip5() -> None: input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=2, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5", ) @staticmethod def export_tf_uniandbigrams_skip5() -> None: input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32) output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_uniandbigrams_skip5", ) @staticmethod def export_tf_batch_uniandbigrams_skip5() -> None: input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32) output = np.array( [[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]] ).astype(np.float32) ngram_counts = np.array([0, 4]).astype(np.int64) ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64) pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams np.int64 ) # bigrams helper = TfIdfVectorizerHelper( mode="TF", min_gram_length=1, max_gram_length=2, max_skip_count=5, ngram_counts=ngram_counts, ngram_indexes=ngram_indexes, pool_int64s=pool_int64s, ) node = helper.make_node_noweights() expect( node, inputs=[input], outputs=[output], name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5", ) onnx-onnx-bca0315/onnx/backend/test/case/node/thresholdedrelu.py000066400000000000000000000024221511334557700250260ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class ThresholdedRelu(Base): @staticmethod def export() -> None: alpha = 2.0 node = onnx.helper.make_node( "ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha ) x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32) y = np.clip(x, alpha, np.inf) # expected output [0., 0., 0., 0., 2.2] y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, alpha, np.inf) y[y == alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu") @staticmethod def export_default() -> None: default_alpha = 1.0 node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"]) x = np.random.randn(3, 4, 5).astype(np.float32) y = np.clip(x, default_alpha, np.inf) y[y == default_alpha] = 0 expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default") onnx-onnx-bca0315/onnx/backend/test/case/node/tile.py000066400000000000000000000021171511334557700225670ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Tile(Base): @staticmethod def export_tile() -> None: node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.random.rand(2, 3, 4, 5).astype(np.float32) repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64) z = np.tile(x, repeats) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile") @staticmethod def export_tile_precomputed() -> None: node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"]) x = np.array([[0, 1], [2, 3]], dtype=np.float32) repeats = np.array([2, 2], dtype=np.int64) z = np.array( [[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32 ) expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed") onnx-onnx-bca0315/onnx/backend/test/case/node/topk.py000066400000000000000000000151511511334557700226110ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def topk_sorted_implementation(X, k, axis, largest): ind_axis = np.indices(X.shape)[axis] if largest: ind_axis = -ind_axis sorted_indices = np.lexsort((ind_axis, X), axis=axis) sorted_values = np.sort(X, axis=axis) if largest: sorted_indices = np.flip(sorted_indices, axis=axis) sorted_values = np.flip(sorted_values, axis=axis) topk_sorted_indices = np.take(sorted_indices, np.arange(k), axis=axis) topk_sorted_values = np.take(sorted_values, np.arange(k), axis=axis) return topk_sorted_values, np.array(topk_sorted_indices, dtype=np.int64) class TopK(Base): @staticmethod def export_top_k() -> None: axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k" ) @staticmethod def export_top_k_uint64() -> None: axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.uint64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3 2 1] # [ 7 6 5] # [11 10 9]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_uint64", ) @staticmethod def export_top_k_same_values() -> None: axis = 0 largest = 0 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # (Pdb) print(values_ref) # [0 0 0] # (Pdb) print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values", ) @staticmethod def export_top_k_same_values_largest() -> None: axis = 0 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [0, 0, 0, 0], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [0 0 0] # print(indices_ref) # [0 1 2] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_largest", ) @staticmethod def export_top_k_same_values_2d() -> None: axis = 1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 1, 1]], dtype=np.int64, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[0 0 0] # [1 1 1] # [1 1 2]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [2 3 0]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_same_values_2d", ) @staticmethod def export_top_k_smallest() -> None: axis = 1 largest = 0 sorted_ = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis, largest=largest, sorted=sorted_, ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [11, 10, 9, 8], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 0. 1. 2.] # [ 4. 5. 6.] # [ 8. 9. 10.]] # print(indices_ref) # [[0 1 2] # [0 1 2] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_smallest", ) @staticmethod def export_top_k_negative_axis() -> None: axis = -1 largest = 1 k = 3 node = onnx.helper.make_node( "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis ) X = np.array( [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=np.float32, ) K = np.array([k], dtype=np.int64) values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest) # print(values_ref) # [[ 3. 2. 1.] # [ 7. 6. 5.] # [11. 10. 9.]] # print(indices_ref) # [[3 2 1] # [3 2 1] # [3 2 1]] expect( node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k_negative_axis", ) onnx-onnx-bca0315/onnx/backend/test/case/node/transpose.py000066400000000000000000000025571511334557700236600ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import itertools import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Transpose(Base): @staticmethod def export_default() -> None: shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"] ) transposed = np.transpose(data) expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default") @staticmethod def export_all_permutations() -> None: shape = (2, 3, 4) data = np.random.random_sample(shape).astype(np.float32) permutations = list(itertools.permutations(np.arange(len(shape)))) for i, permutation in enumerate(permutations): node = onnx.helper.make_node( "Transpose", inputs=["data"], outputs=["transposed"], perm=permutation, ) transposed = np.transpose(data, permutation) expect( node, inputs=[data], outputs=[transposed], name=f"test_transpose_all_permutations_{i}", ) onnx-onnx-bca0315/onnx/backend/test/case/node/trilu.py000066400000000000000000000303421511334557700227720ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def triu_reference_implementation(x, k=0): return np.triu(x, k) def tril_reference_implementation(x, k=0): return np.tril(x, k) class Trilu(Base): @staticmethod def export_triu() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [0, 2, 8, 6, 9], # [0, 0, 0, 8, 7], # [0, 0, 0, 2, 4]] y = triu_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_triu") @staticmethod def export_triu_neg() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [0, 4, 0, 8, 7], # [0, 0, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg") @staticmethod def export_triu_out_neg_out() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out") @staticmethod def export_triu_pos() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 3, 7, 9], # [0, 0, 0, 6, 9], # [0, 0, 0, 0, 7], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos") @staticmethod def export_triu_out_pos() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 0, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos") @staticmethod def export_triu_square() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) y = triu_reference_implementation(x) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [0, 5, 4], # [0, 0, 2]], # # [[1, 4, 9], # [0, 6, 3], # [0, 0, 8]]] expect(node, inputs=[x], outputs=[y], name="test_triu_square") @staticmethod def export_triu_square_neg() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[4, 6, 9], # [7, 5, 4], # [8, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [8, 9, 8]]] # expect result: # [[[4, 6, 9], # [7, 5, 4], # [0, 1, 2]], # # [[1, 4, 9], # [9, 6, 3], # [0, 9, 8]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg") @staticmethod def export_triu_one_row() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) k = np.array(1).astype(np.int64) # X: # [[[1, 4, 9, 7, 1]], # # [[9, 2, 8, 8, 4]], # # [[3, 9, 7, 4, 2]]] # expect result: # [[[0, 4, 9, 7, 1]], # # [[0, 2, 8, 8, 4]], # # [[0, 9, 7, 4, 2]]] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row") @staticmethod def export_triu_zero() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], ) x = np.random.randint(10, size=(0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = triu_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero") @staticmethod def export_tril() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 0, 0, 0, 0], # [1, 2, 0, 0, 0], # [9, 4, 1, 0, 0], # [4, 3, 4, 2, 0]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril") @staticmethod def export_tril_neg() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [1, 0, 0, 0, 0], # [9, 4, 0, 0, 0], # [4, 3, 4, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg") @staticmethod def export_tril_out_neg() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(-7).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0], # [0, 0, 0, 0, 0]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg") @staticmethod def export_tril_pos() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(2).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 0, 0], # [1, 2, 8, 6, 0], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos") @staticmethod def export_tril_out_pos() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(4, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] # expect result: # [[4, 7, 3, 7, 9], # [1, 2, 8, 6, 9], # [9, 4, 1, 8, 7], # [4, 3, 4, 2, 4]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos") @staticmethod def export_tril_square() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 5]], # # [[2, 0, 0], # [2, 6, 0], # [2, 6, 5]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_square") @staticmethod def export_tril_square_neg() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64) k = np.array(-1).astype(np.int64) # X: # [[[0, 4, 3], # [2, 0, 9], # [8, 2, 5]], # # [[2, 7, 2], # [2, 6, 0], # [2, 6, 5]]] # expect result: # [[[0, 0, 0], # [2, 0, 0], # [8, 2, 0]], # # [[0, 0, 0], # [2, 0, 0], # [2, 6, 0]]] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg") @staticmethod def export_tril_one_row() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64) # X: # [[[6, 2, 4, 1, 6]], # # [[8, 3, 8, 7, 0]], # # [[2, 2, 9, 5, 9]]] # expect result: # [[[6, 0, 0, 0, 0]], # # [[8, 0, 0, 0, 0]], # # [[2, 0, 0, 0, 0]]] y = tril_reference_implementation(x) expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg") @staticmethod def export_tril_zero() -> None: node = onnx.helper.make_node( "Trilu", inputs=["x", "k"], outputs=["y"], upper=0, ) x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64) k = np.array(6).astype(np.int64) # X: # [] # expect result: # [] y = tril_reference_implementation(x, int(k)) expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero") onnx-onnx-bca0315/onnx/backend/test/case/node/unique.py000066400000000000000000000155611511334557700231470ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect def specify_int64(indices, inverse_indices, counts): return ( np.array(indices, dtype=np.int64), np.array(inverse_indices, dtype=np.int64), np.array(counts, dtype=np.int64), ) class Unique(Base): @staticmethod def export_sorted_without_axis() -> None: node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], ) x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_without_axis", ) @staticmethod def export_not_sorted_without_axis() -> None: node_not_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=0, ) # numpy unique does not retain original order (it sorts the output unique values) # https://github.com/numpy/numpy/issues/8621 # we need to recover unsorted output and indices x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True) # prepare index mapping from sorted to unsorted argsorted_indices = np.argsort(indices) inverse_indices_map = dict( zip(argsorted_indices, np.arange(len(argsorted_indices)), strict=True) ) indices = indices[argsorted_indices] y = np.take(x, indices, axis=0) inverse_indices = np.asarray( [inverse_indices_map[i] for i in inverse_indices], dtype=np.int64 ) counts = counts[argsorted_indices] indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # print(y) # [2.0, 1.0, 3.0, 4.0] # print(indices) # [0 1 3 4] # print(inverse_indices) # [0, 1, 1, 2, 3, 2] # print(counts) # [1, 2, 2, 1] expect( node_not_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_not_sorted_without_axis", ) @staticmethod def export_sorted_with_axis() -> None: node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=0, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[1. 0. 0.] # [2. 3. 4.]] # print(indices) # [0 2] # print(inverse_indices) # [0 0 1] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis", ) @staticmethod def export_sorted_with_axis_3d() -> None: node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=1, ) x = np.array( [ [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]], ], dtype=np.float32, ) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[[0. 1.] # [1. 1.] # [2. 1.]] # [[0. 1.] # [1. 1.] # [2. 1.]]] # print(indices) # [1 0 2] # print(inverse_indices) # [1 0 2 0] # print(counts) # [2 1 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_axis_3d", ) @staticmethod def export_sorted_with_negative_axis() -> None: node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, axis=-1, ) x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32) y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [[0. 1.] # [0. 1.] # [3. 2.]] # print(indices) # [1 0] # print(inverse_indices) # [1 0 0] # print(counts) # [2 1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_sorted_with_negative_axis", ) @staticmethod def export_length_1() -> None: node_sorted = onnx.helper.make_node( "Unique", inputs=["X"], outputs=["Y", "indices", "inverse_indices", "counts"], sorted=1, ) x = np.array([0], dtype=np.int64) y, indices, inverse_indices, counts = np.unique(x, True, True, True) indices, inverse_indices, counts = specify_int64( indices, inverse_indices, counts ) # behavior changed with numpy >= 2.0 inverse_indices = inverse_indices.reshape(-1) # print(y) # [0] # print(indices) # [0] # print(inverse_indices) # [0] # print(counts) # [1] expect( node_sorted, inputs=[x], outputs=[y, indices, inverse_indices, counts], name="test_unique_length_1", ) onnx-onnx-bca0315/onnx/backend/test/case/node/unsqueeze.py000066400000000000000000000053171511334557700236630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Unsqueeze(Base): @staticmethod def export_unsqueeze_one_axis() -> None: x = np.random.randn(3, 4, 5).astype(np.float32) for i in range(x.ndim): axes = np.array([i]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=i) expect( node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_axis_" + str(i), ) @staticmethod def export_unsqueeze_two_axes() -> None: x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([1, 4]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=1) y = np.expand_dims(y, axis=4) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes") @staticmethod def export_unsqueeze_three_axes() -> None: x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([2, 4, 5]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes") @staticmethod def export_unsqueeze_unsorted_axes() -> None: x = np.random.randn(3, 4, 5).astype(np.float32) axes = np.array([5, 4, 2]).astype(np.int64) node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) y = np.expand_dims(x, axis=2) y = np.expand_dims(y, axis=4) y = np.expand_dims(y, axis=5) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes") @staticmethod def export_unsqueeze_negative_axes() -> None: node = onnx.helper.make_node( "Unsqueeze", inputs=["x", "axes"], outputs=["y"], ) x = np.random.randn(1, 3, 1, 5).astype(np.float32) axes = np.array([-2]).astype(np.int64) y = np.expand_dims(x, axis=-2) expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes") onnx-onnx-bca0315/onnx/backend/test/case/node/upsample.py000066400000000000000000000025341511334557700234630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx import helper from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Upsample(Base): @staticmethod def export_nearest() -> None: node = onnx.helper.make_node( "Upsample", inputs=["X", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) output = np.array( [ [ [ [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4], [3, 3, 3, 4, 4, 4], ] ] ], dtype=np.float32, ) expect( node, inputs=[data, scales], outputs=[output], name="test_upsample_nearest", opset_imports=[helper.make_opsetid("", 9)], ) onnx-onnx-bca0315/onnx/backend/test/case/node/where.py000066400000000000000000000024621511334557700227470ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Where(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.float32) y = np.array([[9, 8], [7, 6]], dtype=np.float32) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example") @staticmethod def export_long() -> None: node = onnx.helper.make_node( "Where", inputs=["condition", "x", "y"], outputs=["z"], ) condition = np.array([[1, 0], [1, 1]], dtype=bool) x = np.array([[1, 2], [3, 4]], dtype=np.int64) y = np.array([[9, 8], [7, 6]], dtype=np.int64) z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]] expect( node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example" ) onnx-onnx-bca0315/onnx/backend/test/case/node/xor.py000066400000000000000000000047101511334557700224430ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class Xor(Base): @staticmethod def export() -> None: node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 2d x = (np.random.randn(3, 4) > 0).astype(bool) y = (np.random.randn(3, 4) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor2d") # 3d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(3, 4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor3d") # 4d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor4d") @staticmethod def export_xor_broadcast() -> None: node = onnx.helper.make_node( "Xor", inputs=["x", "y"], outputs=["xor"], ) # 3d vs 1d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v1d") # 3d vs 2d x = (np.random.randn(3, 4, 5) > 0).astype(bool) y = (np.random.randn(4, 5) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v2d") # 4d vs 2d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v2d") # 4d vs 3d x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool) y = (np.random.randn(4, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v3d") # 4d vs 4d x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool) y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool) z = np.logical_xor(x, y) expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d") onnx-onnx-bca0315/onnx/backend/test/case/test_case.py000066400000000000000000000011611511334557700226550ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING if TYPE_CHECKING: from collections.abc import Sequence import numpy as np import onnx @dataclass class TestCase: name: str model_name: str url: str | None model_dir: str | None model: onnx.ModelProto | None data_sets: Sequence[tuple[Sequence[np.ndarray], Sequence[np.ndarray]]] | None kind: str rtol: float atol: float # Tell PyTest this isn't a real test. __test__: bool = False onnx-onnx-bca0315/onnx/backend/test/case/utils.py000066400000000000000000000020201511334557700220360ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import importlib import pkgutil from typing import TYPE_CHECKING import numpy as np from onnx import ONNX_ML if TYPE_CHECKING: from types import ModuleType all_numeric_dtypes = [ np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, np.float16, np.float32, np.float64, ] def import_recursive(package: ModuleType) -> None: """Takes a package and imports all modules underneath it.""" pkg_dir = package.__path__ module_location = package.__name__ for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir): module_name = f"{module_location}.{name}" # Module/package if not ONNX_ML and module_name.startswith( "onnx.backend.test.case.node.ai_onnx_ml" ): continue module = importlib.import_module(module_name) if ispkg: import_recursive(module) onnx-onnx-bca0315/onnx/backend/test/cmd_tools.py000066400000000000000000000170721511334557700217630ustar00rootroot00000000000000# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import argparse import json import os import shutil import warnings import onnx.backend.test.case.model as model_test import onnx.backend.test.case.node as node_test from onnx import ONNX_ML, TensorProto, numpy_helper TOP_DIR = os.path.realpath(os.path.dirname(__file__)) DATA_DIR = os.path.join(TOP_DIR, "data") def generate_data(args: argparse.Namespace) -> None: def prepare_dir(path: str) -> None: if os.path.exists(path): shutil.rmtree(path) os.makedirs(path) # Clean the output directory before generating data for node testcases # It is used to check new generated data is correct in CIs node_root = os.path.join(args.output, "node") original_dir_number = len( [name for name in os.listdir(node_root) if os.path.isfile(name)] ) if args.clean and os.path.exists(node_root): for sub_dir in os.listdir(node_root): if ONNX_ML or not sub_dir.startswith("test_ai_onnx_ml_"): shutil.rmtree(os.path.join(node_root, sub_dir)) cases = model_test.collect_testcases() # If op_type is specified, only include those testcases including the given operator # Otherwise, include all of the testcases if args.diff: cases += node_test.collect_diff_testcases() else: cases += node_test.collect_testcases(args.op_type) node_number = 0 for case in cases: output_dir = os.path.join(args.output, case.kind, case.name) prepare_dir(output_dir) if case.kind == "node": node_number += 1 if case.kind == "real": with open(os.path.join(output_dir, "data.json"), "w") as fi: json.dump( { "url": case.url, "model_name": case.model_name, "rtol": case.rtol, "atol": case.atol, }, fi, sort_keys=True, ) else: assert case.model with open(os.path.join(output_dir, "model.onnx"), "wb") as f: f.write(case.model.SerializeToString()) assert case.data_sets for i, (inputs, outputs) in enumerate(case.data_sets): data_set_dir = os.path.join(output_dir, f"test_data_set_{i}") prepare_dir(data_set_dir) for j, input in enumerate(inputs): with open(os.path.join(data_set_dir, f"input_{j}.pb"), "wb") as f: if case.model.graph.input[j].type.HasField("map_type"): f.write( numpy_helper.from_dict( input, case.model.graph.input[j].name ).SerializeToString() ) elif case.model.graph.input[j].type.HasField("sequence_type"): f.write( numpy_helper.from_list( input, case.model.graph.input[j].name ).SerializeToString() ) elif case.model.graph.input[j].type.HasField("optional_type"): f.write( numpy_helper.from_optional( input, case.model.graph.input[j].name ).SerializeToString() ) else: assert case.model.graph.input[j].type.HasField( "tensor_type" ) if isinstance(input, TensorProto): f.write(input.SerializeToString()) else: f.write( numpy_helper.from_array( input, case.model.graph.input[j].name ).SerializeToString() ) for j, output in enumerate(outputs): with open(os.path.join(data_set_dir, f"output_{j}.pb"), "wb") as f: if case.model.graph.output[j].type.HasField("map_type"): f.write( numpy_helper.from_dict( output, case.model.graph.output[j].name ).SerializeToString() ) elif case.model.graph.output[j].type.HasField("sequence_type"): f.write( numpy_helper.from_list( output, case.model.graph.output[j].name ).SerializeToString() ) elif case.model.graph.output[j].type.HasField("optional_type"): f.write( numpy_helper.from_optional( output, case.model.graph.output[j].name ).SerializeToString() ) else: assert case.model.graph.output[j].type.HasField( "tensor_type" ) if isinstance(output, TensorProto): f.write(output.SerializeToString()) else: f.write( numpy_helper.from_array( output, case.model.graph.output[j].name ).SerializeToString() ) if not args.clean and node_number != original_dir_number: warnings.warn( "There are some models under 'onnx/backend/test/data/node' which cannot not" " be generated by the script from 'onnx/backend/test/case/node'. Please add" " '--clean' option for 'python onnx/backend/test/cmd_tools.py generate-data'" " to cleanup the existing directories and regenerate them.", Warning, stacklevel=2, ) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser("backend-test-tools") subparsers = parser.add_subparsers() subparser = subparsers.add_parser( "generate-data", help="convert testcases to test data." ) subparser.add_argument( "-c", "--clean", default=False, action="store_true", help="Clean the output directory before generating data for node testcases.", ) subparser.add_argument( "-o", "--output", default=DATA_DIR, help="output directory (default: %(default)s)", ) subparser.add_argument( "-t", "--op_type", default=None, help="op_type for test case generation. (generates test data for the specified op_type only.)", ) subparser.add_argument( "-d", "--diff", default=False, action="store_true", help="only generates test data for those changed files (compared to the main branch).", ) subparser.set_defaults(func=generate_data) return parser.parse_args() def main() -> None: args = parse_args() args.func(args) if __name__ == "__main__": main() onnx-onnx-bca0315/onnx/backend/test/data/000077500000000000000000000000001511334557700203305ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/light/000077500000000000000000000000001511334557700214375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/light/README.md000066400000000000000000000007221511334557700227170ustar00rootroot00000000000000 # Light models The models in this folder were created by replacing all float initializers by nodes `ConstantOfShape` with function `replace_initializer_by_constant_of_shape`. The models are lighter and can be added to the repository for unit testing. 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conv3_10/x2/bn_mean_0__SHAPE  Z+ conv3_10/x2/bn_scale_0__SHAPE  Z) conv3_10/x2/bn_var_0__SHAPE  Z' conv3_10/x2/bn_w_0__SHAPE  Z$ conv3_10/x2_w_0__SHAPE  Z' conv3_11/x1/bn_b_0__SHAPE  Z* conv3_11/x1/bn_bias_0__SHAPE  Z* conv3_11/x1/bn_mean_0__SHAPE  Z+ conv3_11/x1/bn_scale_0__SHAPE  Z) conv3_11/x1/bn_var_0__SHAPE  Z' conv3_11/x1/bn_w_0__SHAPE  Z$ conv3_11/x1_w_0__SHAPE  Z' conv3_11/x2/bn_b_0__SHAPE  Z* conv3_11/x2/bn_bias_0__SHAPE  Z* conv3_11/x2/bn_mean_0__SHAPE  Z+ conv3_11/x2/bn_scale_0__SHAPE  Z) conv3_11/x2/bn_var_0__SHAPE  Z' conv3_11/x2/bn_w_0__SHAPE  Z$ conv3_11/x2_w_0__SHAPE  Z' conv3_12/x1/bn_b_0__SHAPE  Z* conv3_12/x1/bn_bias_0__SHAPE  Z* conv3_12/x1/bn_mean_0__SHAPE  Z+ conv3_12/x1/bn_scale_0__SHAPE  Z) conv3_12/x1/bn_var_0__SHAPE  Z' conv3_12/x1/bn_w_0__SHAPE  Z$ conv3_12/x1_w_0__SHAPE  Z' conv3_12/x2/bn_b_0__SHAPE  Z* conv3_12/x2/bn_bias_0__SHAPE  Z* conv3_12/x2/bn_mean_0__SHAPE  Z+ conv3_12/x2/bn_scale_0__SHAPE  Z) conv3_12/x2/bn_var_0__SHAPE  Z' conv3_12/x2/bn_w_0__SHAPE  Z$ conv3_12/x2_w_0__SHAPE  Z& conv3_2/x1/bn_b_0__SHAPE  Z) conv3_2/x1/bn_bias_0__SHAPE  Z) conv3_2/x1/bn_mean_0__SHAPE  Z* conv3_2/x1/bn_scale_0__SHAPE  Z( conv3_2/x1/bn_var_0__SHAPE  Z& conv3_2/x1/bn_w_0__SHAPE  Z# conv3_2/x1_w_0__SHAPE  Z& conv3_2/x2/bn_b_0__SHAPE  Z) conv3_2/x2/bn_bias_0__SHAPE  Z) conv3_2/x2/bn_mean_0__SHAPE  Z* conv3_2/x2/bn_scale_0__SHAPE  Z( conv3_2/x2/bn_var_0__SHAPE  Z& conv3_2/x2/bn_w_0__SHAPE  Z# conv3_2/x2_w_0__SHAPE  Z& conv3_3/x1/bn_b_0__SHAPE  Z) conv3_3/x1/bn_bias_0__SHAPE  Z) conv3_3/x1/bn_mean_0__SHAPE  Z* conv3_3/x1/bn_scale_0__SHAPE  Z( conv3_3/x1/bn_var_0__SHAPE  Z& conv3_3/x1/bn_w_0__SHAPE  Z# conv3_3/x1_w_0__SHAPE  Z& conv3_3/x2/bn_b_0__SHAPE  Z) conv3_3/x2/bn_bias_0__SHAPE  Z) conv3_3/x2/bn_mean_0__SHAPE  Z* conv3_3/x2/bn_scale_0__SHAPE  Z( conv3_3/x2/bn_var_0__SHAPE  Z& conv3_3/x2/bn_w_0__SHAPE  Z# conv3_3/x2_w_0__SHAPE  Z& conv3_4/x1/bn_b_0__SHAPE  Z) conv3_4/x1/bn_bias_0__SHAPE  Z) conv3_4/x1/bn_mean_0__SHAPE  Z* conv3_4/x1/bn_scale_0__SHAPE  Z( conv3_4/x1/bn_var_0__SHAPE  Z& conv3_4/x1/bn_w_0__SHAPE  Z# conv3_4/x1_w_0__SHAPE  Z& conv3_4/x2/bn_b_0__SHAPE  Z) conv3_4/x2/bn_bias_0__SHAPE  Z) conv3_4/x2/bn_mean_0__SHAPE  Z* conv3_4/x2/bn_scale_0__SHAPE  Z( conv3_4/x2/bn_var_0__SHAPE  Z& conv3_4/x2/bn_w_0__SHAPE  Z# conv3_4/x2_w_0__SHAPE  Z& conv3_5/x1/bn_b_0__SHAPE  Z) conv3_5/x1/bn_bias_0__SHAPE  Z) conv3_5/x1/bn_mean_0__SHAPE  Z* conv3_5/x1/bn_scale_0__SHAPE  Z( conv3_5/x1/bn_var_0__SHAPE  Z& conv3_5/x1/bn_w_0__SHAPE  Z# conv3_5/x1_w_0__SHAPE  Z& conv3_5/x2/bn_b_0__SHAPE  Z) conv3_5/x2/bn_bias_0__SHAPE  Z) conv3_5/x2/bn_mean_0__SHAPE  Z* conv3_5/x2/bn_scale_0__SHAPE  Z( conv3_5/x2/bn_var_0__SHAPE  Z& conv3_5/x2/bn_w_0__SHAPE  Z# conv3_5/x2_w_0__SHAPE  Z& conv3_6/x1/bn_b_0__SHAPE  Z) conv3_6/x1/bn_bias_0__SHAPE  Z) conv3_6/x1/bn_mean_0__SHAPE  Z* conv3_6/x1/bn_scale_0__SHAPE  Z( conv3_6/x1/bn_var_0__SHAPE  Z& conv3_6/x1/bn_w_0__SHAPE  Z# conv3_6/x1_w_0__SHAPE  Z& conv3_6/x2/bn_b_0__SHAPE  Z) conv3_6/x2/bn_bias_0__SHAPE  Z) conv3_6/x2/bn_mean_0__SHAPE  Z* conv3_6/x2/bn_scale_0__SHAPE  Z( conv3_6/x2/bn_var_0__SHAPE  Z& conv3_6/x2/bn_w_0__SHAPE  Z# conv3_6/x2_w_0__SHAPE  Z& conv3_7/x1/bn_b_0__SHAPE  Z) conv3_7/x1/bn_bias_0__SHAPE  Z) conv3_7/x1/bn_mean_0__SHAPE  Z* conv3_7/x1/bn_scale_0__SHAPE  Z( conv3_7/x1/bn_var_0__SHAPE  Z& conv3_7/x1/bn_w_0__SHAPE  Z# conv3_7/x1_w_0__SHAPE  Z& conv3_7/x2/bn_b_0__SHAPE  Z) conv3_7/x2/bn_bias_0__SHAPE  Z) conv3_7/x2/bn_mean_0__SHAPE  Z* conv3_7/x2/bn_scale_0__SHAPE  Z( conv3_7/x2/bn_var_0__SHAPE  Z& conv3_7/x2/bn_w_0__SHAPE  Z# conv3_7/x2_w_0__SHAPE  Z& conv3_8/x1/bn_b_0__SHAPE  Z) conv3_8/x1/bn_bias_0__SHAPE  Z) conv3_8/x1/bn_mean_0__SHAPE  Z* conv3_8/x1/bn_scale_0__SHAPE  Z( conv3_8/x1/bn_var_0__SHAPE  Z& conv3_8/x1/bn_w_0__SHAPE  Z# conv3_8/x1_w_0__SHAPE  Z& conv3_8/x2/bn_b_0__SHAPE  Z) conv3_8/x2/bn_bias_0__SHAPE  Z) conv3_8/x2/bn_mean_0__SHAPE  Z* conv3_8/x2/bn_scale_0__SHAPE  Z( conv3_8/x2/bn_var_0__SHAPE  Z& conv3_8/x2/bn_w_0__SHAPE  Z# conv3_8/x2_w_0__SHAPE  Z& conv3_9/x1/bn_b_0__SHAPE  Z) conv3_9/x1/bn_bias_0__SHAPE  Z) conv3_9/x1/bn_mean_0__SHAPE  Z* conv3_9/x1/bn_scale_0__SHAPE  Z( conv3_9/x1/bn_var_0__SHAPE  Z& conv3_9/x1/bn_w_0__SHAPE  Z# conv3_9/x1_w_0__SHAPE  Z& conv3_9/x2/bn_b_0__SHAPE  Z) conv3_9/x2/bn_bias_0__SHAPE  Z) conv3_9/x2/bn_mean_0__SHAPE  Z* conv3_9/x2/bn_scale_0__SHAPE  Z( conv3_9/x2/bn_var_0__SHAPE  Z& conv3_9/x2/bn_w_0__SHAPE  Z# conv3_9/x2_w_0__SHAPE  Z% conv3_blk/bn_b_0__SHAPE  Z( conv3_blk/bn_bias_0__SHAPE  Z( conv3_blk/bn_mean_0__SHAPE  Z) conv3_blk/bn_scale_0__SHAPE  Z' conv3_blk/bn_var_0__SHAPE  Z% conv3_blk/bn_w_0__SHAPE  Z" conv3_blk_w_0__SHAPE  Z& conv4_1/x1/bn_b_0__SHAPE  Z) conv4_1/x1/bn_bias_0__SHAPE  Z) conv4_1/x1/bn_mean_0__SHAPE  Z* conv4_1/x1/bn_scale_0__SHAPE  Z( conv4_1/x1/bn_var_0__SHAPE  Z& conv4_1/x1/bn_w_0__SHAPE  Z# conv4_1/x1_w_0__SHAPE  Z& conv4_1/x2/bn_b_0__SHAPE  Z) conv4_1/x2/bn_bias_0__SHAPE  Z) conv4_1/x2/bn_mean_0__SHAPE  Z* conv4_1/x2/bn_scale_0__SHAPE  Z( conv4_1/x2/bn_var_0__SHAPE  Z& conv4_1/x2/bn_w_0__SHAPE  Z# conv4_1/x2_w_0__SHAPE  Z' conv4_10/x1/bn_b_0__SHAPE  Z* conv4_10/x1/bn_bias_0__SHAPE  Z* conv4_10/x1/bn_mean_0__SHAPE  Z+ conv4_10/x1/bn_scale_0__SHAPE  Z) conv4_10/x1/bn_var_0__SHAPE  Z' conv4_10/x1/bn_w_0__SHAPE  Z$ conv4_10/x1_w_0__SHAPE  Z' conv4_10/x2/bn_b_0__SHAPE  Z* conv4_10/x2/bn_bias_0__SHAPE  Z* conv4_10/x2/bn_mean_0__SHAPE  Z+ conv4_10/x2/bn_scale_0__SHAPE  Z) conv4_10/x2/bn_var_0__SHAPE  Z' conv4_10/x2/bn_w_0__SHAPE  Z$ conv4_10/x2_w_0__SHAPE  Z' conv4_11/x1/bn_b_0__SHAPE  Z* conv4_11/x1/bn_bias_0__SHAPE  Z* conv4_11/x1/bn_mean_0__SHAPE  Z+ conv4_11/x1/bn_scale_0__SHAPE  Z) conv4_11/x1/bn_var_0__SHAPE  Z' conv4_11/x1/bn_w_0__SHAPE  Z$ conv4_11/x1_w_0__SHAPE  Z' conv4_11/x2/bn_b_0__SHAPE  Z* conv4_11/x2/bn_bias_0__SHAPE  Z* conv4_11/x2/bn_mean_0__SHAPE  Z+ conv4_11/x2/bn_scale_0__SHAPE  Z) conv4_11/x2/bn_var_0__SHAPE  Z' conv4_11/x2/bn_w_0__SHAPE  Z$ conv4_11/x2_w_0__SHAPE  Z' conv4_12/x1/bn_b_0__SHAPE  Z* conv4_12/x1/bn_bias_0__SHAPE  Z* conv4_12/x1/bn_mean_0__SHAPE  Z+ conv4_12/x1/bn_scale_0__SHAPE  Z) conv4_12/x1/bn_var_0__SHAPE  Z' conv4_12/x1/bn_w_0__SHAPE  Z$ conv4_12/x1_w_0__SHAPE  Z' conv4_12/x2/bn_b_0__SHAPE  Z* conv4_12/x2/bn_bias_0__SHAPE  Z* conv4_12/x2/bn_mean_0__SHAPE  Z+ conv4_12/x2/bn_scale_0__SHAPE  Z) conv4_12/x2/bn_var_0__SHAPE  Z' conv4_12/x2/bn_w_0__SHAPE  Z$ conv4_12/x2_w_0__SHAPE  Z' conv4_13/x1/bn_b_0__SHAPE  Z* conv4_13/x1/bn_bias_0__SHAPE  Z* conv4_13/x1/bn_mean_0__SHAPE  Z+ conv4_13/x1/bn_scale_0__SHAPE  Z) conv4_13/x1/bn_var_0__SHAPE  Z' conv4_13/x1/bn_w_0__SHAPE  Z$ conv4_13/x1_w_0__SHAPE  Z' conv4_13/x2/bn_b_0__SHAPE  Z* conv4_13/x2/bn_bias_0__SHAPE  Z* conv4_13/x2/bn_mean_0__SHAPE  Z+ conv4_13/x2/bn_scale_0__SHAPE  Z) conv4_13/x2/bn_var_0__SHAPE  Z' conv4_13/x2/bn_w_0__SHAPE  Z$ conv4_13/x2_w_0__SHAPE  Z' conv4_14/x1/bn_b_0__SHAPE  Z* conv4_14/x1/bn_bias_0__SHAPE  Z* conv4_14/x1/bn_mean_0__SHAPE  Z+ conv4_14/x1/bn_scale_0__SHAPE  Z) conv4_14/x1/bn_var_0__SHAPE  Z' conv4_14/x1/bn_w_0__SHAPE  Z$ conv4_14/x1_w_0__SHAPE  Z' conv4_14/x2/bn_b_0__SHAPE  Z* conv4_14/x2/bn_bias_0__SHAPE  Z* conv4_14/x2/bn_mean_0__SHAPE  Z+ conv4_14/x2/bn_scale_0__SHAPE  Z) conv4_14/x2/bn_var_0__SHAPE  Z' conv4_14/x2/bn_w_0__SHAPE  Z$ conv4_14/x2_w_0__SHAPE  Z' conv4_15/x1/bn_b_0__SHAPE  Z* conv4_15/x1/bn_bias_0__SHAPE  Z* conv4_15/x1/bn_mean_0__SHAPE  Z+ conv4_15/x1/bn_scale_0__SHAPE  Z) conv4_15/x1/bn_var_0__SHAPE  Z' conv4_15/x1/bn_w_0__SHAPE  Z$ conv4_15/x1_w_0__SHAPE  Z' conv4_15/x2/bn_b_0__SHAPE  Z* conv4_15/x2/bn_bias_0__SHAPE  Z* conv4_15/x2/bn_mean_0__SHAPE  Z+ conv4_15/x2/bn_scale_0__SHAPE  Z) conv4_15/x2/bn_var_0__SHAPE  Z' conv4_15/x2/bn_w_0__SHAPE  Z$ conv4_15/x2_w_0__SHAPE  Z' conv4_16/x1/bn_b_0__SHAPE  Z* conv4_16/x1/bn_bias_0__SHAPE  Z* conv4_16/x1/bn_mean_0__SHAPE  Z+ conv4_16/x1/bn_scale_0__SHAPE  Z) conv4_16/x1/bn_var_0__SHAPE  Z' conv4_16/x1/bn_w_0__SHAPE  Z$ conv4_16/x1_w_0__SHAPE  Z' conv4_16/x2/bn_b_0__SHAPE  Z* conv4_16/x2/bn_bias_0__SHAPE  Z* conv4_16/x2/bn_mean_0__SHAPE  Z+ conv4_16/x2/bn_scale_0__SHAPE  Z) conv4_16/x2/bn_var_0__SHAPE  Z' conv4_16/x2/bn_w_0__SHAPE  Z$ conv4_16/x2_w_0__SHAPE  Z' conv4_17/x1/bn_b_0__SHAPE  Z* conv4_17/x1/bn_bias_0__SHAPE  Z* conv4_17/x1/bn_mean_0__SHAPE  Z+ conv4_17/x1/bn_scale_0__SHAPE  Z) conv4_17/x1/bn_var_0__SHAPE  Z' conv4_17/x1/bn_w_0__SHAPE  Z$ conv4_17/x1_w_0__SHAPE  Z' conv4_17/x2/bn_b_0__SHAPE  Z* conv4_17/x2/bn_bias_0__SHAPE  Z* conv4_17/x2/bn_mean_0__SHAPE  Z+ conv4_17/x2/bn_scale_0__SHAPE  Z) conv4_17/x2/bn_var_0__SHAPE  Z' conv4_17/x2/bn_w_0__SHAPE  Z$ conv4_17/x2_w_0__SHAPE  Z' conv4_18/x1/bn_b_0__SHAPE  Z* conv4_18/x1/bn_bias_0__SHAPE  Z* conv4_18/x1/bn_mean_0__SHAPE  Z+ conv4_18/x1/bn_scale_0__SHAPE  Z) conv4_18/x1/bn_var_0__SHAPE  Z' conv4_18/x1/bn_w_0__SHAPE  Z$ conv4_18/x1_w_0__SHAPE  Z' conv4_18/x2/bn_b_0__SHAPE  Z* conv4_18/x2/bn_bias_0__SHAPE  Z* conv4_18/x2/bn_mean_0__SHAPE  Z+ conv4_18/x2/bn_scale_0__SHAPE  Z) conv4_18/x2/bn_var_0__SHAPE  Z' conv4_18/x2/bn_w_0__SHAPE  Z$ conv4_18/x2_w_0__SHAPE  Z' conv4_19/x1/bn_b_0__SHAPE  Z* conv4_19/x1/bn_bias_0__SHAPE  Z* conv4_19/x1/bn_mean_0__SHAPE  Z+ conv4_19/x1/bn_scale_0__SHAPE  Z) conv4_19/x1/bn_var_0__SHAPE  Z' conv4_19/x1/bn_w_0__SHAPE  Z$ conv4_19/x1_w_0__SHAPE  Z' conv4_19/x2/bn_b_0__SHAPE  Z* conv4_19/x2/bn_bias_0__SHAPE  Z* conv4_19/x2/bn_mean_0__SHAPE  Z+ conv4_19/x2/bn_scale_0__SHAPE  Z) conv4_19/x2/bn_var_0__SHAPE  Z' conv4_19/x2/bn_w_0__SHAPE  Z$ conv4_19/x2_w_0__SHAPE  Z& conv4_2/x1/bn_b_0__SHAPE  Z) conv4_2/x1/bn_bias_0__SHAPE  Z) conv4_2/x1/bn_mean_0__SHAPE  Z* conv4_2/x1/bn_scale_0__SHAPE  Z( conv4_2/x1/bn_var_0__SHAPE  Z& conv4_2/x1/bn_w_0__SHAPE  Z# conv4_2/x1_w_0__SHAPE  Z& conv4_2/x2/bn_b_0__SHAPE  Z) conv4_2/x2/bn_bias_0__SHAPE  Z) conv4_2/x2/bn_mean_0__SHAPE  Z* conv4_2/x2/bn_scale_0__SHAPE  Z( conv4_2/x2/bn_var_0__SHAPE  Z& conv4_2/x2/bn_w_0__SHAPE  Z# conv4_2/x2_w_0__SHAPE  Z' conv4_20/x1/bn_b_0__SHAPE  Z* conv4_20/x1/bn_bias_0__SHAPE  Z* conv4_20/x1/bn_mean_0__SHAPE  Z+ conv4_20/x1/bn_scale_0__SHAPE  Z) conv4_20/x1/bn_var_0__SHAPE  Z' conv4_20/x1/bn_w_0__SHAPE  Z$ conv4_20/x1_w_0__SHAPE  Z' conv4_20/x2/bn_b_0__SHAPE  Z* conv4_20/x2/bn_bias_0__SHAPE  Z* conv4_20/x2/bn_mean_0__SHAPE  Z+ conv4_20/x2/bn_scale_0__SHAPE  Z) conv4_20/x2/bn_var_0__SHAPE  Z' conv4_20/x2/bn_w_0__SHAPE  Z$ conv4_20/x2_w_0__SHAPE  Z' conv4_21/x1/bn_b_0__SHAPE  Z* conv4_21/x1/bn_bias_0__SHAPE  Z* conv4_21/x1/bn_mean_0__SHAPE  Z+ conv4_21/x1/bn_scale_0__SHAPE  Z) conv4_21/x1/bn_var_0__SHAPE  Z' conv4_21/x1/bn_w_0__SHAPE  Z$ conv4_21/x1_w_0__SHAPE  Z' conv4_21/x2/bn_b_0__SHAPE  Z* conv4_21/x2/bn_bias_0__SHAPE  Z* conv4_21/x2/bn_mean_0__SHAPE  Z+ conv4_21/x2/bn_scale_0__SHAPE  Z) conv4_21/x2/bn_var_0__SHAPE  Z' conv4_21/x2/bn_w_0__SHAPE  Z$ conv4_21/x2_w_0__SHAPE  Z' conv4_22/x1/bn_b_0__SHAPE  Z* conv4_22/x1/bn_bias_0__SHAPE  Z* conv4_22/x1/bn_mean_0__SHAPE  Z+ conv4_22/x1/bn_scale_0__SHAPE  Z) conv4_22/x1/bn_var_0__SHAPE  Z' conv4_22/x1/bn_w_0__SHAPE  Z$ conv4_22/x1_w_0__SHAPE  Z' conv4_22/x2/bn_b_0__SHAPE  Z* conv4_22/x2/bn_bias_0__SHAPE  Z* conv4_22/x2/bn_mean_0__SHAPE  Z+ conv4_22/x2/bn_scale_0__SHAPE  Z) conv4_22/x2/bn_var_0__SHAPE  Z' conv4_22/x2/bn_w_0__SHAPE  Z$ conv4_22/x2_w_0__SHAPE  Z' conv4_23/x1/bn_b_0__SHAPE  Z* conv4_23/x1/bn_bias_0__SHAPE  Z* conv4_23/x1/bn_mean_0__SHAPE  Z+ conv4_23/x1/bn_scale_0__SHAPE  Z) conv4_23/x1/bn_var_0__SHAPE  Z' conv4_23/x1/bn_w_0__SHAPE  Z$ conv4_23/x1_w_0__SHAPE  Z' conv4_23/x2/bn_b_0__SHAPE  Z* conv4_23/x2/bn_bias_0__SHAPE  Z* conv4_23/x2/bn_mean_0__SHAPE  Z+ conv4_23/x2/bn_scale_0__SHAPE  Z) conv4_23/x2/bn_var_0__SHAPE  Z' conv4_23/x2/bn_w_0__SHAPE  Z$ conv4_23/x2_w_0__SHAPE  Z' conv4_24/x1/bn_b_0__SHAPE  Z* conv4_24/x1/bn_bias_0__SHAPE  Z* conv4_24/x1/bn_mean_0__SHAPE  Z+ conv4_24/x1/bn_scale_0__SHAPE  Z) conv4_24/x1/bn_var_0__SHAPE  Z' conv4_24/x1/bn_w_0__SHAPE  Z$ conv4_24/x1_w_0__SHAPE  Z' conv4_24/x2/bn_b_0__SHAPE  Z* conv4_24/x2/bn_bias_0__SHAPE  Z* conv4_24/x2/bn_mean_0__SHAPE  Z+ conv4_24/x2/bn_scale_0__SHAPE  Z) conv4_24/x2/bn_var_0__SHAPE  Z' conv4_24/x2/bn_w_0__SHAPE  Z$ conv4_24/x2_w_0__SHAPE  Z& conv4_3/x1/bn_b_0__SHAPE  Z) conv4_3/x1/bn_bias_0__SHAPE  Z) conv4_3/x1/bn_mean_0__SHAPE  Z* conv4_3/x1/bn_scale_0__SHAPE  Z( conv4_3/x1/bn_var_0__SHAPE  Z& conv4_3/x1/bn_w_0__SHAPE  Z# conv4_3/x1_w_0__SHAPE  Z& conv4_3/x2/bn_b_0__SHAPE  Z) conv4_3/x2/bn_bias_0__SHAPE  Z) conv4_3/x2/bn_mean_0__SHAPE  Z* conv4_3/x2/bn_scale_0__SHAPE  Z( conv4_3/x2/bn_var_0__SHAPE  Z& conv4_3/x2/bn_w_0__SHAPE  Z# conv4_3/x2_w_0__SHAPE  Z& conv4_4/x1/bn_b_0__SHAPE  Z) conv4_4/x1/bn_bias_0__SHAPE  Z) conv4_4/x1/bn_mean_0__SHAPE  Z* conv4_4/x1/bn_scale_0__SHAPE  Z( conv4_4/x1/bn_var_0__SHAPE  Z& conv4_4/x1/bn_w_0__SHAPE  Z# conv4_4/x1_w_0__SHAPE  Z& conv4_4/x2/bn_b_0__SHAPE  Z) conv4_4/x2/bn_bias_0__SHAPE  Z) conv4_4/x2/bn_mean_0__SHAPE  Z* conv4_4/x2/bn_scale_0__SHAPE  Z( conv4_4/x2/bn_var_0__SHAPE  Z& conv4_4/x2/bn_w_0__SHAPE  Z# conv4_4/x2_w_0__SHAPE  Z& conv4_5/x1/bn_b_0__SHAPE  Z) conv4_5/x1/bn_bias_0__SHAPE  Z) conv4_5/x1/bn_mean_0__SHAPE  Z* conv4_5/x1/bn_scale_0__SHAPE  Z( conv4_5/x1/bn_var_0__SHAPE  Z& conv4_5/x1/bn_w_0__SHAPE  Z# conv4_5/x1_w_0__SHAPE  Z& conv4_5/x2/bn_b_0__SHAPE  Z) conv4_5/x2/bn_bias_0__SHAPE  Z) conv4_5/x2/bn_mean_0__SHAPE  Z* conv4_5/x2/bn_scale_0__SHAPE  Z( conv4_5/x2/bn_var_0__SHAPE  Z& conv4_5/x2/bn_w_0__SHAPE  Z# conv4_5/x2_w_0__SHAPE  Z& conv4_6/x1/bn_b_0__SHAPE  Z) conv4_6/x1/bn_bias_0__SHAPE  Z) conv4_6/x1/bn_mean_0__SHAPE  Z* conv4_6/x1/bn_scale_0__SHAPE  Z( conv4_6/x1/bn_var_0__SHAPE  Z& conv4_6/x1/bn_w_0__SHAPE  Z# conv4_6/x1_w_0__SHAPE  Z& conv4_6/x2/bn_b_0__SHAPE  Z) conv4_6/x2/bn_bias_0__SHAPE  Z) conv4_6/x2/bn_mean_0__SHAPE  Z* conv4_6/x2/bn_scale_0__SHAPE  Z( conv4_6/x2/bn_var_0__SHAPE  Z& conv4_6/x2/bn_w_0__SHAPE  Z# conv4_6/x2_w_0__SHAPE  Z& conv4_7/x1/bn_b_0__SHAPE  Z) conv4_7/x1/bn_bias_0__SHAPE  Z) conv4_7/x1/bn_mean_0__SHAPE  Z* conv4_7/x1/bn_scale_0__SHAPE  Z( conv4_7/x1/bn_var_0__SHAPE  Z& conv4_7/x1/bn_w_0__SHAPE  Z# conv4_7/x1_w_0__SHAPE  Z& conv4_7/x2/bn_b_0__SHAPE  Z) conv4_7/x2/bn_bias_0__SHAPE  Z) conv4_7/x2/bn_mean_0__SHAPE  Z* conv4_7/x2/bn_scale_0__SHAPE  Z( conv4_7/x2/bn_var_0__SHAPE  Z& conv4_7/x2/bn_w_0__SHAPE  Z# conv4_7/x2_w_0__SHAPE  Z& conv4_8/x1/bn_b_0__SHAPE  Z) conv4_8/x1/bn_bias_0__SHAPE  Z) conv4_8/x1/bn_mean_0__SHAPE  Z* conv4_8/x1/bn_scale_0__SHAPE  Z( conv4_8/x1/bn_var_0__SHAPE  Z& conv4_8/x1/bn_w_0__SHAPE  Z# conv4_8/x1_w_0__SHAPE  Z& conv4_8/x2/bn_b_0__SHAPE  Z) conv4_8/x2/bn_bias_0__SHAPE  Z) conv4_8/x2/bn_mean_0__SHAPE  Z* conv4_8/x2/bn_scale_0__SHAPE  Z( conv4_8/x2/bn_var_0__SHAPE  Z& conv4_8/x2/bn_w_0__SHAPE  Z# conv4_8/x2_w_0__SHAPE  Z& conv4_9/x1/bn_b_0__SHAPE  Z) conv4_9/x1/bn_bias_0__SHAPE  Z) conv4_9/x1/bn_mean_0__SHAPE  Z* conv4_9/x1/bn_scale_0__SHAPE  Z( conv4_9/x1/bn_var_0__SHAPE  Z& conv4_9/x1/bn_w_0__SHAPE  Z# conv4_9/x1_w_0__SHAPE  Z& conv4_9/x2/bn_b_0__SHAPE  Z) conv4_9/x2/bn_bias_0__SHAPE  Z) conv4_9/x2/bn_mean_0__SHAPE  Z* conv4_9/x2/bn_scale_0__SHAPE  Z( conv4_9/x2/bn_var_0__SHAPE  Z& conv4_9/x2/bn_w_0__SHAPE  Z# conv4_9/x2_w_0__SHAPE  Z% conv4_blk/bn_b_0__SHAPE  Z( conv4_blk/bn_bias_0__SHAPE  Z( conv4_blk/bn_mean_0__SHAPE  Z) conv4_blk/bn_scale_0__SHAPE  Z' conv4_blk/bn_var_0__SHAPE  Z% conv4_blk/bn_w_0__SHAPE  Z" conv4_blk_w_0__SHAPE  Z& conv5_1/x1/bn_b_0__SHAPE  Z) conv5_1/x1/bn_bias_0__SHAPE  Z) conv5_1/x1/bn_mean_0__SHAPE  Z* conv5_1/x1/bn_scale_0__SHAPE  Z( conv5_1/x1/bn_var_0__SHAPE  Z& conv5_1/x1/bn_w_0__SHAPE  Z# conv5_1/x1_w_0__SHAPE  Z& conv5_1/x2/bn_b_0__SHAPE  Z) conv5_1/x2/bn_bias_0__SHAPE  Z) conv5_1/x2/bn_mean_0__SHAPE  Z* conv5_1/x2/bn_scale_0__SHAPE  Z( conv5_1/x2/bn_var_0__SHAPE  Z& conv5_1/x2/bn_w_0__SHAPE  Z# conv5_1/x2_w_0__SHAPE  Z' conv5_10/x1/bn_b_0__SHAPE  Z* conv5_10/x1/bn_bias_0__SHAPE  Z* conv5_10/x1/bn_mean_0__SHAPE  Z+ conv5_10/x1/bn_scale_0__SHAPE  Z) conv5_10/x1/bn_var_0__SHAPE  Z' conv5_10/x1/bn_w_0__SHAPE  Z$ conv5_10/x1_w_0__SHAPE  Z' conv5_10/x2/bn_b_0__SHAPE  Z* conv5_10/x2/bn_bias_0__SHAPE  Z* conv5_10/x2/bn_mean_0__SHAPE  Z+ conv5_10/x2/bn_scale_0__SHAPE  Z) conv5_10/x2/bn_var_0__SHAPE  Z' conv5_10/x2/bn_w_0__SHAPE  Z$ conv5_10/x2_w_0__SHAPE  Z' conv5_11/x1/bn_b_0__SHAPE  Z* conv5_11/x1/bn_bias_0__SHAPE  Z* conv5_11/x1/bn_mean_0__SHAPE  Z+ conv5_11/x1/bn_scale_0__SHAPE  Z) conv5_11/x1/bn_var_0__SHAPE  Z' conv5_11/x1/bn_w_0__SHAPE  Z$ conv5_11/x1_w_0__SHAPE  Z' conv5_11/x2/bn_b_0__SHAPE  Z* conv5_11/x2/bn_bias_0__SHAPE  Z* conv5_11/x2/bn_mean_0__SHAPE  Z+ conv5_11/x2/bn_scale_0__SHAPE  Z) conv5_11/x2/bn_var_0__SHAPE  Z' conv5_11/x2/bn_w_0__SHAPE  Z$ conv5_11/x2_w_0__SHAPE  Z' conv5_12/x1/bn_b_0__SHAPE  Z* conv5_12/x1/bn_bias_0__SHAPE  Z* conv5_12/x1/bn_mean_0__SHAPE  Z+ conv5_12/x1/bn_scale_0__SHAPE  Z) conv5_12/x1/bn_var_0__SHAPE  Z' conv5_12/x1/bn_w_0__SHAPE  Z$ conv5_12/x1_w_0__SHAPE  Z' conv5_12/x2/bn_b_0__SHAPE  Z* conv5_12/x2/bn_bias_0__SHAPE  Z* conv5_12/x2/bn_mean_0__SHAPE  Z+ conv5_12/x2/bn_scale_0__SHAPE  Z) conv5_12/x2/bn_var_0__SHAPE  Z' conv5_12/x2/bn_w_0__SHAPE  Z$ conv5_12/x2_w_0__SHAPE  Z' conv5_13/x1/bn_b_0__SHAPE  Z* conv5_13/x1/bn_bias_0__SHAPE  Z* conv5_13/x1/bn_mean_0__SHAPE  Z+ conv5_13/x1/bn_scale_0__SHAPE  Z) conv5_13/x1/bn_var_0__SHAPE  Z' conv5_13/x1/bn_w_0__SHAPE  Z$ conv5_13/x1_w_0__SHAPE  Z' conv5_13/x2/bn_b_0__SHAPE  Z* conv5_13/x2/bn_bias_0__SHAPE  Z* conv5_13/x2/bn_mean_0__SHAPE  Z+ conv5_13/x2/bn_scale_0__SHAPE  Z) conv5_13/x2/bn_var_0__SHAPE  Z' conv5_13/x2/bn_w_0__SHAPE  Z$ conv5_13/x2_w_0__SHAPE  Z' conv5_14/x1/bn_b_0__SHAPE  Z* conv5_14/x1/bn_bias_0__SHAPE  Z* conv5_14/x1/bn_mean_0__SHAPE  Z+ conv5_14/x1/bn_scale_0__SHAPE  Z) conv5_14/x1/bn_var_0__SHAPE  Z' conv5_14/x1/bn_w_0__SHAPE  Z$ conv5_14/x1_w_0__SHAPE  Z' conv5_14/x2/bn_b_0__SHAPE  Z* conv5_14/x2/bn_bias_0__SHAPE  Z* conv5_14/x2/bn_mean_0__SHAPE  Z+ conv5_14/x2/bn_scale_0__SHAPE  Z) conv5_14/x2/bn_var_0__SHAPE  Z' conv5_14/x2/bn_w_0__SHAPE  Z$ conv5_14/x2_w_0__SHAPE  Z' conv5_15/x1/bn_b_0__SHAPE  Z* conv5_15/x1/bn_bias_0__SHAPE  Z* conv5_15/x1/bn_mean_0__SHAPE  Z+ conv5_15/x1/bn_scale_0__SHAPE  Z) conv5_15/x1/bn_var_0__SHAPE  Z' conv5_15/x1/bn_w_0__SHAPE  Z$ conv5_15/x1_w_0__SHAPE  Z' conv5_15/x2/bn_b_0__SHAPE  Z* conv5_15/x2/bn_bias_0__SHAPE  Z* conv5_15/x2/bn_mean_0__SHAPE  Z+ conv5_15/x2/bn_scale_0__SHAPE  Z) conv5_15/x2/bn_var_0__SHAPE  Z' conv5_15/x2/bn_w_0__SHAPE  Z$ conv5_15/x2_w_0__SHAPE  Z' conv5_16/x1/bn_b_0__SHAPE  Z* conv5_16/x1/bn_bias_0__SHAPE  Z* conv5_16/x1/bn_mean_0__SHAPE  Z+ conv5_16/x1/bn_scale_0__SHAPE  Z) conv5_16/x1/bn_var_0__SHAPE  Z' conv5_16/x1/bn_w_0__SHAPE  Z$ conv5_16/x1_w_0__SHAPE  Z' conv5_16/x2/bn_b_0__SHAPE  Z* conv5_16/x2/bn_bias_0__SHAPE  Z* conv5_16/x2/bn_mean_0__SHAPE  Z+ conv5_16/x2/bn_scale_0__SHAPE  Z) conv5_16/x2/bn_var_0__SHAPE  Z' conv5_16/x2/bn_w_0__SHAPE  Z$ conv5_16/x2_w_0__SHAPE  Z& conv5_2/x1/bn_b_0__SHAPE  Z) conv5_2/x1/bn_bias_0__SHAPE  Z) conv5_2/x1/bn_mean_0__SHAPE  Z* conv5_2/x1/bn_scale_0__SHAPE  Z( conv5_2/x1/bn_var_0__SHAPE  Z& conv5_2/x1/bn_w_0__SHAPE  Z# conv5_2/x1_w_0__SHAPE  Z& conv5_2/x2/bn_b_0__SHAPE  Z) conv5_2/x2/bn_bias_0__SHAPE  Z) conv5_2/x2/bn_mean_0__SHAPE  Z* conv5_2/x2/bn_scale_0__SHAPE  Z( conv5_2/x2/bn_var_0__SHAPE  Z& conv5_2/x2/bn_w_0__SHAPE  Z# conv5_2/x2_w_0__SHAPE  Z& conv5_3/x1/bn_b_0__SHAPE  Z) conv5_3/x1/bn_bias_0__SHAPE  Z) conv5_3/x1/bn_mean_0__SHAPE  Z* conv5_3/x1/bn_scale_0__SHAPE  Z( conv5_3/x1/bn_var_0__SHAPE  Z& conv5_3/x1/bn_w_0__SHAPE  Z# conv5_3/x1_w_0__SHAPE  Z& conv5_3/x2/bn_b_0__SHAPE  Z) conv5_3/x2/bn_bias_0__SHAPE  Z) conv5_3/x2/bn_mean_0__SHAPE  Z* conv5_3/x2/bn_scale_0__SHAPE  Z( conv5_3/x2/bn_var_0__SHAPE  Z& conv5_3/x2/bn_w_0__SHAPE  Z# conv5_3/x2_w_0__SHAPE  Z& conv5_4/x1/bn_b_0__SHAPE  Z) conv5_4/x1/bn_bias_0__SHAPE  Z) conv5_4/x1/bn_mean_0__SHAPE  Z* conv5_4/x1/bn_scale_0__SHAPE  Z( conv5_4/x1/bn_var_0__SHAPE  Z& conv5_4/x1/bn_w_0__SHAPE  Z# conv5_4/x1_w_0__SHAPE  Z& conv5_4/x2/bn_b_0__SHAPE  Z) conv5_4/x2/bn_bias_0__SHAPE  Z) conv5_4/x2/bn_mean_0__SHAPE  Z* conv5_4/x2/bn_scale_0__SHAPE  Z( conv5_4/x2/bn_var_0__SHAPE  Z& conv5_4/x2/bn_w_0__SHAPE  Z# conv5_4/x2_w_0__SHAPE  Z& conv5_5/x1/bn_b_0__SHAPE  Z) conv5_5/x1/bn_bias_0__SHAPE  Z) conv5_5/x1/bn_mean_0__SHAPE  Z* conv5_5/x1/bn_scale_0__SHAPE  Z( conv5_5/x1/bn_var_0__SHAPE  Z& conv5_5/x1/bn_w_0__SHAPE  Z# conv5_5/x1_w_0__SHAPE  Z& conv5_5/x2/bn_b_0__SHAPE  Z) conv5_5/x2/bn_bias_0__SHAPE  Z) conv5_5/x2/bn_mean_0__SHAPE  Z* conv5_5/x2/bn_scale_0__SHAPE  Z( conv5_5/x2/bn_var_0__SHAPE  Z& conv5_5/x2/bn_w_0__SHAPE  Z# conv5_5/x2_w_0__SHAPE  Z& conv5_6/x1/bn_b_0__SHAPE  Z) conv5_6/x1/bn_bias_0__SHAPE  Z) conv5_6/x1/bn_mean_0__SHAPE  Z* conv5_6/x1/bn_scale_0__SHAPE  Z( conv5_6/x1/bn_var_0__SHAPE  Z& conv5_6/x1/bn_w_0__SHAPE  Z# conv5_6/x1_w_0__SHAPE  Z& conv5_6/x2/bn_b_0__SHAPE  Z) conv5_6/x2/bn_bias_0__SHAPE  Z) conv5_6/x2/bn_mean_0__SHAPE  Z* conv5_6/x2/bn_scale_0__SHAPE  Z( conv5_6/x2/bn_var_0__SHAPE  Z& conv5_6/x2/bn_w_0__SHAPE  Z# conv5_6/x2_w_0__SHAPE  Z& conv5_7/x1/bn_b_0__SHAPE  Z) conv5_7/x1/bn_bias_0__SHAPE  Z) conv5_7/x1/bn_mean_0__SHAPE  Z* conv5_7/x1/bn_scale_0__SHAPE  Z( conv5_7/x1/bn_var_0__SHAPE  Z& conv5_7/x1/bn_w_0__SHAPE  Z# conv5_7/x1_w_0__SHAPE  Z& conv5_7/x2/bn_b_0__SHAPE  Z) conv5_7/x2/bn_bias_0__SHAPE  Z) conv5_7/x2/bn_mean_0__SHAPE  Z* conv5_7/x2/bn_scale_0__SHAPE  Z( conv5_7/x2/bn_var_0__SHAPE  Z& conv5_7/x2/bn_w_0__SHAPE  Z# conv5_7/x2_w_0__SHAPE  Z& conv5_8/x1/bn_b_0__SHAPE  Z) conv5_8/x1/bn_bias_0__SHAPE  Z) conv5_8/x1/bn_mean_0__SHAPE  Z* conv5_8/x1/bn_scale_0__SHAPE  Z( conv5_8/x1/bn_var_0__SHAPE  Z& conv5_8/x1/bn_w_0__SHAPE  Z# conv5_8/x1_w_0__SHAPE  Z& conv5_8/x2/bn_b_0__SHAPE  Z) conv5_8/x2/bn_bias_0__SHAPE  Z) conv5_8/x2/bn_mean_0__SHAPE  Z* conv5_8/x2/bn_scale_0__SHAPE  Z( conv5_8/x2/bn_var_0__SHAPE  Z& conv5_8/x2/bn_w_0__SHAPE  Z# conv5_8/x2_w_0__SHAPE  Z& conv5_9/x1/bn_b_0__SHAPE  Z) conv5_9/x1/bn_bias_0__SHAPE  Z) conv5_9/x1/bn_mean_0__SHAPE  Z* conv5_9/x1/bn_scale_0__SHAPE  Z( conv5_9/x1/bn_var_0__SHAPE  Z& conv5_9/x1/bn_w_0__SHAPE  Z# conv5_9/x1_w_0__SHAPE  Z& conv5_9/x2/bn_b_0__SHAPE  Z) conv5_9/x2/bn_bias_0__SHAPE  Z) conv5_9/x2/bn_mean_0__SHAPE  Z* conv5_9/x2/bn_scale_0__SHAPE  Z( conv5_9/x2/bn_var_0__SHAPE  Z& conv5_9/x2/bn_w_0__SHAPE  Z# conv5_9/x2_w_0__SHAPE  Z% conv5_blk/bn_b_0__SHAPE  Z( conv5_blk/bn_bias_0__SHAPE  Z( conv5_blk/bn_mean_0__SHAPE  Z) conv5_blk/bn_scale_0__SHAPE  Z' conv5_blk/bn_var_0__SHAPE  Z% conv5_blk/bn_w_0__SHAPE  Z fc6_b_0__SHAPE  Z fc6_w_0__SHAPE  b fc6_1   č  B  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(inception_3c/double3x3a/bn/sc_w_0__SHAPE  Z6 (inception_3c/double3x3a/bn_bias_0__SHAPE  Z6 (inception_3c/double3x3a/bn_mean_0__SHAPE  Z7 )inception_3c/double3x3a/bn_scale_0__SHAPE  Z5 'inception_3c/double3x3a/bn_var_0__SHAPE  Z0 "inception_3c/double3x3a_w_0__SHAPE  Z6 (inception_3c/double3x3b/bn/sc_b_0__SHAPE  Z6 (inception_3c/double3x3b/bn/sc_w_0__SHAPE  Z6 (inception_3c/double3x3b/bn_bias_0__SHAPE  Z6 (inception_3c/double3x3b/bn_mean_0__SHAPE  Z7 )inception_3c/double3x3b/bn_scale_0__SHAPE  Z5 'inception_3c/double3x3b/bn_var_0__SHAPE  Z0 "inception_3c/double3x3b_w_0__SHAPE  Z/ !inception_4a/1x1/bn/sc_b_0__SHAPE  Z/ !inception_4a/1x1/bn/sc_w_0__SHAPE  Z/ !inception_4a/1x1/bn_bias_0__SHAPE  Z/ !inception_4a/1x1/bn_mean_0__SHAPE  Z0 "inception_4a/1x1/bn_scale_0__SHAPE  Z. inception_4a/1x1/bn_var_0__SHAPE  Z) inception_4a/1x1_w_0__SHAPE  Z/ !inception_4a/3x3/bn/sc_b_0__SHAPE  Z/ !inception_4a/3x3/bn/sc_w_0__SHAPE  Z/ !inception_4a/3x3/bn_bias_0__SHAPE  Z/ !inception_4a/3x3/bn_mean_0__SHAPE  Z0 "inception_4a/3x3/bn_scale_0__SHAPE  Z. inception_4a/3x3/bn_var_0__SHAPE  Z0 "inception_4a/3x3_reduce_w_0__SHAPE  Z) inception_4a/3x3_w_0__SHAPE  Z< .inception_4a/double3x3_reduce/bn/sc_b_0__SHAPE  Z< .inception_4a/double3x3_reduce/bn/sc_w_0__SHAPE  Z< .inception_4a/double3x3_reduce/bn_bias_0__SHAPE  Z< .inception_4a/double3x3_reduce/bn_mean_0__SHAPE  Z= /inception_4a/double3x3_reduce/bn_scale_0__SHAPE  Z; -inception_4a/double3x3_reduce/bn_var_0__SHAPE  Z6 (inception_4a/double3x3_reduce_w_0__SHAPE  Z6 (inception_4a/double3x3a/bn/sc_b_0__SHAPE  Z6 (inception_4a/double3x3a/bn/sc_w_0__SHAPE  Z6 (inception_4a/double3x3a/bn_bias_0__SHAPE  Z6 (inception_4a/double3x3a/bn_mean_0__SHAPE  Z7 )inception_4a/double3x3a/bn_scale_0__SHAPE  Z5 'inception_4a/double3x3a/bn_var_0__SHAPE  Z0 "inception_4a/double3x3a_w_0__SHAPE  Z6 (inception_4a/double3x3b/bn/sc_b_0__SHAPE  Z6 (inception_4a/double3x3b/bn/sc_w_0__SHAPE  Z6 (inception_4a/double3x3b/bn_bias_0__SHAPE  Z6 (inception_4a/double3x3b/bn_mean_0__SHAPE  Z7 )inception_4a/double3x3b/bn_scale_0__SHAPE  Z5 'inception_4a/double3x3b/bn_var_0__SHAPE  Z0 "inception_4a/double3x3b_w_0__SHAPE  Z5 'inception_4a/pool_proj/bn/sc_b_0__SHAPE  Z5 'inception_4a/pool_proj/bn/sc_w_0__SHAPE  Z5 'inception_4a/pool_proj/bn_bias_0__SHAPE  Z5 'inception_4a/pool_proj/bn_mean_0__SHAPE  Z6 (inception_4a/pool_proj/bn_scale_0__SHAPE  Z4 &inception_4a/pool_proj/bn_var_0__SHAPE  Z/ !inception_4a/pool_proj_w_0__SHAPE  Z/ !inception_4b/1x1/bn/sc_b_0__SHAPE  Z/ !inception_4b/1x1/bn/sc_w_0__SHAPE  Z/ !inception_4b/1x1/bn_bias_0__SHAPE  Z/ !inception_4b/1x1/bn_mean_0__SHAPE  Z0 "inception_4b/1x1/bn_scale_0__SHAPE  Z. inception_4b/1x1/bn_var_0__SHAPE  Z) inception_4b/1x1_w_0__SHAPE  Z/ !inception_4b/3x3/bn/sc_b_0__SHAPE  Z/ !inception_4b/3x3/bn/sc_w_0__SHAPE  Z/ !inception_4b/3x3/bn_bias_0__SHAPE  Z/ !inception_4b/3x3/bn_mean_0__SHAPE  Z0 "inception_4b/3x3/bn_scale_0__SHAPE  Z. inception_4b/3x3/bn_var_0__SHAPE  Z6 (inception_4b/3x3_reduce/bn/sc_b_0__SHAPE  Z6 (inception_4b/3x3_reduce/bn/sc_w_0__SHAPE  Z6 (inception_4b/3x3_reduce/bn_bias_0__SHAPE  Z6 (inception_4b/3x3_reduce/bn_mean_0__SHAPE  Z7 )inception_4b/3x3_reduce/bn_scale_0__SHAPE  Z5 'inception_4b/3x3_reduce/bn_var_0__SHAPE  Z0 "inception_4b/3x3_reduce_w_0__SHAPE  Z) inception_4b/3x3_w_0__SHAPE  Z< .inception_4b/double3x3_reduce/bn/sc_b_0__SHAPE  Z< .inception_4b/double3x3_reduce/bn/sc_w_0__SHAPE  Z< .inception_4b/double3x3_reduce/bn_bias_0__SHAPE  Z< .inception_4b/double3x3_reduce/bn_mean_0__SHAPE  Z= /inception_4b/double3x3_reduce/bn_scale_0__SHAPE  Z; -inception_4b/double3x3_reduce/bn_var_0__SHAPE  Z6 (inception_4b/double3x3_reduce_w_0__SHAPE  Z6 (inception_4b/double3x3a/bn/sc_b_0__SHAPE  Z6 (inception_4b/double3x3a/bn/sc_w_0__SHAPE  Z6 (inception_4b/double3x3a/bn_bias_0__SHAPE  Z6 (inception_4b/double3x3a/bn_mean_0__SHAPE  Z7 )inception_4b/double3x3a/bn_scale_0__SHAPE  Z5 'inception_4b/double3x3a/bn_var_0__SHAPE  Z0 "inception_4b/double3x3a_w_0__SHAPE  Z6 (inception_4b/double3x3b/bn/sc_b_0__SHAPE  Z6 (inception_4b/double3x3b/bn/sc_w_0__SHAPE  Z6 (inception_4b/double3x3b/bn_bias_0__SHAPE  Z6 (inception_4b/double3x3b/bn_mean_0__SHAPE  Z7 )inception_4b/double3x3b/bn_scale_0__SHAPE  Z5 'inception_4b/double3x3b/bn_var_0__SHAPE  Z0 "inception_4b/double3x3b_w_0__SHAPE  Z5 'inception_4b/pool_proj/bn/sc_b_0__SHAPE  Z5 'inception_4b/pool_proj/bn/sc_w_0__SHAPE  Z5 'inception_4b/pool_proj/bn_bias_0__SHAPE  Z5 'inception_4b/pool_proj/bn_mean_0__SHAPE  Z6 (inception_4b/pool_proj/bn_scale_0__SHAPE  Z4 &inception_4b/pool_proj/bn_var_0__SHAPE  Z/ !inception_4b/pool_proj_w_0__SHAPE  Z/ !inception_4c/1x1/bn/sc_b_0__SHAPE  Z/ !inception_4c/1x1/bn/sc_w_0__SHAPE  Z/ !inception_4c/1x1/bn_bias_0__SHAPE  Z/ !inception_4c/1x1/bn_mean_0__SHAPE  Z0 "inception_4c/1x1/bn_scale_0__SHAPE  Z. inception_4c/1x1/bn_var_0__SHAPE  Z) inception_4c/1x1_w_0__SHAPE  Z/ !inception_4c/3x3/bn/sc_b_0__SHAPE  Z/ !inception_4c/3x3/bn/sc_w_0__SHAPE  Z/ !inception_4c/3x3/bn_bias_0__SHAPE  Z/ !inception_4c/3x3/bn_mean_0__SHAPE  Z0 "inception_4c/3x3/bn_scale_0__SHAPE  Z. inception_4c/3x3/bn_var_0__SHAPE  Z6 (inception_4c/3x3_reduce/bn/sc_b_0__SHAPE  Z6 (inception_4c/3x3_reduce/bn/sc_w_0__SHAPE  Z6 (inception_4c/3x3_reduce/bn_bias_0__SHAPE  Z6 (inception_4c/3x3_reduce/bn_mean_0__SHAPE  Z7 )inception_4c/3x3_reduce/bn_scale_0__SHAPE  Z5 'inception_4c/3x3_reduce/bn_var_0__SHAPE  Z0 "inception_4c/3x3_reduce_w_0__SHAPE  Z) inception_4c/3x3_w_0__SHAPE  Z< .inception_4c/double3x3_reduce/bn/sc_b_0__SHAPE  Z< .inception_4c/double3x3_reduce/bn/sc_w_0__SHAPE  Z< .inception_4c/double3x3_reduce/bn_bias_0__SHAPE  Z< .inception_4c/double3x3_reduce/bn_mean_0__SHAPE  Z= /inception_4c/double3x3_reduce/bn_scale_0__SHAPE  Z; -inception_4c/double3x3_reduce/bn_var_0__SHAPE  Z6 (inception_4c/double3x3_reduce_w_0__SHAPE  Z6 (inception_4c/double3x3a/bn/sc_b_0__SHAPE  Z6 (inception_4c/double3x3a/bn/sc_w_0__SHAPE  Z6 (inception_4c/double3x3a/bn_bias_0__SHAPE  Z6 (inception_4c/double3x3a/bn_mean_0__SHAPE  Z7 )inception_4c/double3x3a/bn_scale_0__SHAPE  Z5 'inception_4c/double3x3a/bn_var_0__SHAPE  Z0 "inception_4c/double3x3a_w_0__SHAPE  Z6 (inception_4c/double3x3b/bn/sc_b_0__SHAPE  Z6 (inception_4c/double3x3b/bn/sc_w_0__SHAPE  Z6 (inception_4c/double3x3b/bn_bias_0__SHAPE  Z6 (inception_4c/double3x3b/bn_mean_0__SHAPE  Z7 )inception_4c/double3x3b/bn_scale_0__SHAPE  Z5 'inception_4c/double3x3b/bn_var_0__SHAPE  Z0 "inception_4c/double3x3b_w_0__SHAPE  Z5 'inception_4c/pool_proj/bn/sc_b_0__SHAPE  Z5 'inception_4c/pool_proj/bn/sc_w_0__SHAPE  Z5 'inception_4c/pool_proj/bn_bias_0__SHAPE  Z5 'inception_4c/pool_proj/bn_mean_0__SHAPE  Z6 (inception_4c/pool_proj/bn_scale_0__SHAPE  Z4 &inception_4c/pool_proj/bn_var_0__SHAPE  Z/ !inception_4c/pool_proj_w_0__SHAPE  Z/ !inception_4d/1x1/bn/sc_b_0__SHAPE  Z/ !inception_4d/1x1/bn/sc_w_0__SHAPE  Z/ !inception_4d/1x1/bn_bias_0__SHAPE  Z/ !inception_4d/1x1/bn_mean_0__SHAPE 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-inception_4d/double3x3_reduce/bn_var_0__SHAPE  Z6 (inception_4d/double3x3_reduce_w_0__SHAPE  Z6 (inception_4d/double3x3a/bn/sc_b_0__SHAPE  Z6 (inception_4d/double3x3a/bn/sc_w_0__SHAPE  Z6 (inception_4d/double3x3a/bn_bias_0__SHAPE  Z6 (inception_4d/double3x3a/bn_mean_0__SHAPE  Z7 )inception_4d/double3x3a/bn_scale_0__SHAPE  Z5 'inception_4d/double3x3a/bn_var_0__SHAPE  Z0 "inception_4d/double3x3a_w_0__SHAPE  Z6 (inception_4d/double3x3b/bn/sc_b_0__SHAPE  Z6 (inception_4d/double3x3b/bn/sc_w_0__SHAPE  Z6 (inception_4d/double3x3b/bn_bias_0__SHAPE  Z6 (inception_4d/double3x3b/bn_mean_0__SHAPE  Z7 )inception_4d/double3x3b/bn_scale_0__SHAPE  Z5 'inception_4d/double3x3b/bn_var_0__SHAPE  Z0 "inception_4d/double3x3b_w_0__SHAPE  Z5 'inception_4d/pool_proj/bn/sc_b_0__SHAPE  Z5 'inception_4d/pool_proj/bn/sc_w_0__SHAPE  Z5 'inception_4d/pool_proj/bn_bias_0__SHAPE  Z5 'inception_4d/pool_proj/bn_mean_0__SHAPE  Z6 (inception_4d/pool_proj/bn_scale_0__SHAPE  Z4 &inception_4d/pool_proj/bn_var_0__SHAPE  Z/ 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 Z* gpu_0/gconv1_5_bn_b_0__SHAPE  Z, gpu_0/gconv1_5_bn_riv_0__SHAPE  Z+ gpu_0/gconv1_5_bn_rm_0__SHAPE  Z* gpu_0/gconv1_5_bn_s_0__SHAPE  Z' gpu_0/gconv1_5_w_0__SHAPE  Z* gpu_0/gconv1_6_bn_b_0__SHAPE  Z, gpu_0/gconv1_6_bn_riv_0__SHAPE  Z+ gpu_0/gconv1_6_bn_rm_0__SHAPE  Z* gpu_0/gconv1_6_bn_s_0__SHAPE  Z' gpu_0/gconv1_6_w_0__SHAPE  Z* gpu_0/gconv1_7_bn_b_0__SHAPE  Z, gpu_0/gconv1_7_bn_riv_0__SHAPE  Z+ gpu_0/gconv1_7_bn_rm_0__SHAPE  Z* gpu_0/gconv1_7_bn_s_0__SHAPE  Z' gpu_0/gconv1_7_w_0__SHAPE  Z* gpu_0/gconv1_8_bn_b_0__SHAPE  Z, gpu_0/gconv1_8_bn_riv_0__SHAPE  Z+ gpu_0/gconv1_8_bn_rm_0__SHAPE  Z* gpu_0/gconv1_8_bn_s_0__SHAPE  Z' gpu_0/gconv1_8_w_0__SHAPE  Z* gpu_0/gconv1_9_bn_b_0__SHAPE  Z, gpu_0/gconv1_9_bn_riv_0__SHAPE  Z+ gpu_0/gconv1_9_bn_rm_0__SHAPE  Z* gpu_0/gconv1_9_bn_s_0__SHAPE  Z' gpu_0/gconv1_9_w_0__SHAPE  Z* gpu_0/gconv3_0_bn_b_0__SHAPE  Z, gpu_0/gconv3_0_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_0_bn_rm_0__SHAPE  Z* gpu_0/gconv3_0_bn_s_0__SHAPE  Z' gpu_0/gconv3_0_w_0__SHAPE  Z+ gpu_0/gconv3_10_bn_b_0__SHAPE  Z- gpu_0/gconv3_10_bn_riv_0__SHAPE  Z, gpu_0/gconv3_10_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_10_bn_s_0__SHAPE  Z( gpu_0/gconv3_10_w_0__SHAPE  Z+ gpu_0/gconv3_11_bn_b_0__SHAPE  Z- gpu_0/gconv3_11_bn_riv_0__SHAPE  Z, gpu_0/gconv3_11_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_11_bn_s_0__SHAPE  Z( gpu_0/gconv3_11_w_0__SHAPE  Z+ gpu_0/gconv3_12_bn_b_0__SHAPE  Z- gpu_0/gconv3_12_bn_riv_0__SHAPE  Z, gpu_0/gconv3_12_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_12_bn_s_0__SHAPE  Z( gpu_0/gconv3_12_w_0__SHAPE  Z+ gpu_0/gconv3_13_bn_b_0__SHAPE  Z- gpu_0/gconv3_13_bn_riv_0__SHAPE  Z, gpu_0/gconv3_13_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_13_bn_s_0__SHAPE  Z( gpu_0/gconv3_13_w_0__SHAPE  Z+ gpu_0/gconv3_14_bn_b_0__SHAPE  Z- gpu_0/gconv3_14_bn_riv_0__SHAPE  Z, gpu_0/gconv3_14_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_14_bn_s_0__SHAPE  Z( gpu_0/gconv3_14_w_0__SHAPE  Z+ gpu_0/gconv3_15_bn_b_0__SHAPE  Z- gpu_0/gconv3_15_bn_riv_0__SHAPE  Z, gpu_0/gconv3_15_bn_rm_0__SHAPE  Z+ gpu_0/gconv3_15_bn_s_0__SHAPE  Z( gpu_0/gconv3_15_w_0__SHAPE  Z* gpu_0/gconv3_1_bn_b_0__SHAPE  Z, gpu_0/gconv3_1_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_1_bn_rm_0__SHAPE  Z* gpu_0/gconv3_1_bn_s_0__SHAPE  Z' gpu_0/gconv3_1_w_0__SHAPE  Z* gpu_0/gconv3_2_bn_b_0__SHAPE  Z, gpu_0/gconv3_2_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_2_bn_rm_0__SHAPE  Z* gpu_0/gconv3_2_bn_s_0__SHAPE  Z' gpu_0/gconv3_2_w_0__SHAPE  Z* gpu_0/gconv3_3_bn_b_0__SHAPE  Z, gpu_0/gconv3_3_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_3_bn_rm_0__SHAPE  Z* gpu_0/gconv3_3_bn_s_0__SHAPE  Z' gpu_0/gconv3_3_w_0__SHAPE  Z* gpu_0/gconv3_4_bn_b_0__SHAPE  Z, gpu_0/gconv3_4_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_4_bn_rm_0__SHAPE  Z* gpu_0/gconv3_4_bn_s_0__SHAPE  Z' gpu_0/gconv3_4_w_0__SHAPE  Z* gpu_0/gconv3_5_bn_b_0__SHAPE  Z, gpu_0/gconv3_5_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_5_bn_rm_0__SHAPE  Z* gpu_0/gconv3_5_bn_s_0__SHAPE  Z' gpu_0/gconv3_5_w_0__SHAPE  Z* gpu_0/gconv3_6_bn_b_0__SHAPE  Z, gpu_0/gconv3_6_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_6_bn_rm_0__SHAPE  Z* gpu_0/gconv3_6_bn_s_0__SHAPE  Z' gpu_0/gconv3_6_w_0__SHAPE  Z* gpu_0/gconv3_7_bn_b_0__SHAPE  Z, gpu_0/gconv3_7_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_7_bn_rm_0__SHAPE  Z* gpu_0/gconv3_7_bn_s_0__SHAPE  Z' gpu_0/gconv3_7_w_0__SHAPE  Z* gpu_0/gconv3_8_bn_b_0__SHAPE  Z, gpu_0/gconv3_8_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_8_bn_rm_0__SHAPE  Z* gpu_0/gconv3_8_bn_s_0__SHAPE  Z' gpu_0/gconv3_8_w_0__SHAPE  Z* gpu_0/gconv3_9_bn_b_0__SHAPE  Z, gpu_0/gconv3_9_bn_riv_0__SHAPE  Z+ gpu_0/gconv3_9_bn_rm_0__SHAPE  Z* gpu_0/gconv3_9_bn_s_0__SHAPE  Z' gpu_0/gconv3_9_w_0__SHAPE  Z# gpu_0/pred_b_0__SHAPE  Z# gpu_0/pred_w_0__SHAPE  b" gpu_0/softmax_1   čB  onnx-onnx-bca0315/onnx/backend/test/data/light/light_shufflenet_output_0.pb000066400000000000000000000076521511334557700271650ustar00rootroot00000000000000čJ oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:onnx-onnx-bca0315/onnx/backend/test/data/light/light_squeezenet.onnx000066400000000000000000000364021511334557700257270ustar00rootroot00000000000000 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conv10_w_0__SHAPE  Z conv1_w_0__SHAPE  Z( fire2/expand1x1_w_0__SHAPE  Z( fire2/expand3x3_w_0__SHAPE  Z) fire2/squeeze1x1_w_0__SHAPE  Z( fire3/expand1x1_w_0__SHAPE  Z( fire3/expand3x3_w_0__SHAPE  Z) fire3/squeeze1x1_w_0__SHAPE  Z( fire4/expand1x1_b_0__SHAPE  Z( fire4/expand1x1_w_0__SHAPE  Z( fire4/expand3x3_b_0__SHAPE  Z( fire4/expand3x3_w_0__SHAPE  Z) fire4/squeeze1x1_w_0__SHAPE  Z( fire5/expand1x1_b_0__SHAPE  Z( fire5/expand1x1_w_0__SHAPE  Z( fire5/expand3x3_b_0__SHAPE  Z( fire5/expand3x3_w_0__SHAPE  Z) fire5/squeeze1x1_w_0__SHAPE  Z( fire6/expand1x1_b_0__SHAPE  Z( fire6/expand1x1_w_0__SHAPE  Z( fire6/expand3x3_b_0__SHAPE  Z( fire6/expand3x3_w_0__SHAPE  Z) fire6/squeeze1x1_w_0__SHAPE  Z( fire7/expand1x1_b_0__SHAPE  Z( fire7/expand1x1_w_0__SHAPE  Z( fire7/expand3x3_b_0__SHAPE  Z( fire7/expand3x3_w_0__SHAPE  Z) fire7/squeeze1x1_w_0__SHAPE  Z( fire8/expand1x1_b_0__SHAPE  Z( fire8/expand1x1_w_0__SHAPE  Z( fire8/expand3x3_b_0__SHAPE  Z( fire8/expand3x3_w_0__SHAPE  Z) fire8/squeeze1x1_w_0__SHAPE  Z( fire9/expand1x1_b_0__SHAPE  Z( fire9/expand1x1_w_0__SHAPE  Z( fire9/expand3x3_b_0__SHAPE  Z( fire9/expand3x3_w_0__SHAPE  Z) fire9/squeeze1x1_w_0__SHAPE  b' softmaxout_1   č  B  onnx-onnx-bca0315/onnx/backend/test/data/light/light_squeezenet_output_0.pb000066400000000000000000000076561511334557700272160ustar00rootroot00000000000000čJ oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:oƒ:onnx-onnx-bca0315/onnx/backend/test/data/light/light_vgg19.onnx000066400000000000000000000221371511334557700244740ustar00rootroot00000000000000 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' " )%""#"$onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/000077500000000000000000000000001511334557700241735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/model.onnx000066400000000000000000000002071511334557700261760ustar00rootroot00000000000000 backend-test:o  x ysum"Addtest_add_uint8Z x    Z y    b sum    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/test_data_set_0/000077500000000000000000000000001511334557700272355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700311270ustar00rootroot00000000000000BxJ<          onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/test_data_set_0/input_1.pb000066400000000000000000000001111511334557700311300ustar00rootroot00000000000000ByJ<        onnx-onnx-bca0315/onnx/backend/test/data/node/test_add_uint8/test_data_set_0/output_0.pb000066400000000000000000000001131511334557700313320ustar00rootroot00000000000000BsumJ<*+% #),# "  #"# # onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d/000077500000000000000000000000001511334557700251365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d/model.onnx000066400000000000000000000002661511334557700271460ustar00rootroot00000000000000  backend-test: 5 theta sizegrid" AffineGrid* align_corners test_affine_grid_2dZ theta    Z size  b grid     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d/test_data_set_0/000077500000000000000000000000001511334557700302005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d/test_data_set_0/input_0.pb000066400000000000000000000001011511334557700320710ustar00rootroot00000000000000BthetaJ0ˆb‹?[oRĀ @Įm@ÛwĀę5`@ĻļLĀqw@’!Ā V‡@íÚėŋ—ķ’@ž‘–ŋc&ģ?~xƒĀ‘œé?eĖ[Ā_ @ͧ0ĀvD#@6ƒŒ:@<Ŋ´ŋŖēQ@č<ŋĸĪ@˙iž'¤!@Āø =Ŧx3@ ˜>1ME@‰;?ļ!W@ˆqR?:öh@Ã͊?+ž@̰Ü=°r@(˜Ŋ>5G%@"?ē7@8e?>đH@7”?ÃÄZ@Ōĩ?hŲæ?;%ã>9A@œČ4?ž@›ūw?Bę(@Mš?Įž:@L5ŋ?L“L@KĐā?yvĘ?%G?ƒî?’b…?Fä@‘ũĻ?˸@‘˜Č?P,@3ę?Õa>@Hį@‹Ž?×Ŏ?•ŧŅ?Ö`°?žeõ?ÕûŅ?T‡ @Ֆķ?Ų[@ę˜ @^00@jf@onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners/000077500000000000000000000000001511334557700300435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners/model.onnx000066400000000000000000000003041511334557700320440ustar00rootroot00000000000000  backend-test:Ģ 5 theta sizegrid" AffineGrid* align_corners !test_affine_grid_2d_align_cornersZ theta    Z size  b grid     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners/test_data_set_0/000077500000000000000000000000001511334557700331055ustar00rootroot00000000000000input_0.pb000066400000000000000000000001011511334557700347170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners/test_data_set_0BthetaJ0ˆb‹?[oRĀ @Įm@Ė“c@;?8ųx@!"†?€ø?` ÜŊŗĨ@–ŒT> '@WĘ?‹p<@‰qV?÷ÕQ@]Œ“?d;g@ößģ?ãÕ?<īŸ>ģĪ˙?О ?JM@Fq?ļ˛*@™ö ?"@@2JÉ?Ž}U@˝ņ?9‰ą?Hs;?TÜ?= †?u@Ö`Ž?áô@n´Ö?MZ.@˙?ēŋC@Đ­@ Ž?yw“?gظ?Ëģ?@Ŗã?Ģä? 7@"9@xœ@îb@ä2@ēŒ.@onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners_expanded/000077500000000000000000000000001511334557700317135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners_expanded/model.onnx000066400000000000000000000661511511334557700337300ustar00rootroot00000000000000  backend-test:ĪØ bBAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one"Constant* value_int : bBAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two"Constant* value_int : cCAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero"Constant* value_int : cCAffineGrid_test_affine_grid_2d_align_corners_expanded_function_four"Constant* value_int : fEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1d"Constant* value_ints@ : gFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero_1d"Constant* value_ints@ : qHAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one"Constant* value_int˙˙˙˙˙˙˙˙˙ : Š HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one thetaJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_f"CastLike: Ÿ CAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero thetaEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero_f"CastLike:  BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one thetaDAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_f"CastLike:  BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two thetaDAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_f"CastLike: uUAffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_align_corners"Constant* value_int : ‡ UAffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_align_corners CAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero`AffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_align_corners_equal_zero"Equal: X sizeHAffineGrid_test_affine_grid_2d_align_corners_expanded_function_size_ndim"Size: č HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_size_ndim CAffineGrid_test_affine_grid_2d_align_corners_expanded_function_fourNAffineGrid_test_affine_grid_2d_align_corners_expanded_function_condition_is_2d"Equal: – NAffineGrid_test_affine_grid_2d_align_corners_expanded_function_condition_is_2d@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_N@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_C@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_D@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_H@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_W"If*ã then_branch2Đ ŋ sizeEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_thenEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_C_thenEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_thenEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_then"Split* num_outputs : š EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1dEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_D_then"Identity:g1bG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_thenbG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_C_thenbG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_D_thenbG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_thenbG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_then * else_branch2ú † sizeEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_elseEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_C_elseEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_D_elseEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_elseEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_else"Split* num_outputs :g2bG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_elsebG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_C_elsebG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_D_elsebG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_elsebG EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_else : Ŧ @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_N @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_C @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_D @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_H @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_WIAffineGrid_test_affine_grid_2d_align_corners_expanded_function_size_NCDHW"Concat* axis : —, NAffineGrid_test_affine_grid_2d_align_corners_expanded_function_condition_is_2dGAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_3d"If*Ž) then_branch2›) vKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_6"Constant* value_ints@@@@@@ : jGAffineGrid_test_affine_grid_2d_align_corners_expanded_function_shape_23"Constant* value_ints@@ : ī KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_6 GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_shape_23LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_23"Reshape: ė @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_N GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_shape_23HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_shape_N23"Concat* axis : ņ LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_23 HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_shape_N23MAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_N23"Expand: ž theta MAffineGrid_test_affine_grid_2d_align_corners_expanded_function_gather_idx_N23GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_thetaN23"GatherElements* axis : ų GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_thetaN23AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r1AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r2"Split* axis * num_outputs : ’ AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r1BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r1_"Squeeze: ’ AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r2BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r2_"Squeeze: š BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r1_BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r11BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r12AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_t1"Split* axis * num_outputs : š BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r2_BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r21BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r22AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_t2"Split* axis * num_outputs : — BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r21HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r11_shape"Shape: Ŧ HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r11_shapeMAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d_"ConstantOfShape: ° MAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d_ thetaLAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d"CastLike: č LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_fKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_one_1d"Add: ķ BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r11 BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r12 LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_t1AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R1"Concat* axis : ķ BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r21 BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_r22 LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_t2AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R2"Concat* axis : ‘ LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1d KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_one_1d LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_float_zero_1dAAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R3"Concat* axis : Û AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R1 EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1dBAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R1_" Unsqueeze: Û AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R2 EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1dBAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R2_" Unsqueeze: Û AAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R3 EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1dBAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R3_" Unsqueeze: Ž BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R1_ BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R2_ BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_R3_IAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_then"Concat* axis :g3bK IAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_then *Ä else_branch2ą ^ thetaIAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_else"Identity:g4bK IAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_else : fEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_1d"Constant* value_ints@ : hGAffineGrid_test_affine_grid_2d_align_corners_expanded_function_three_1d"Constant* value_ints@ : gFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_five_1d"Constant* value_ints@ : ¸ IAffineGrid_test_affine_grid_2d_align_corners_expanded_function_size_NCDHW EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_1d 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@AffineGrid_test_affine_grid_2d_align_corners_expanded_function_H EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero_fFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_float"CastLike: Ũ @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_W EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero_fFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_float"CastLike: ¯1 `AffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_align_corners_equal_zeroFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_dEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_dFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_hEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_hFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_wEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_w"If*Ÿ then_branch2Œ á DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_f FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_D_floatJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_d_then"Div: á DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_f FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_H_floatJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_h_then"Div: á DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_f FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_W_floatJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_w_then"Div: å JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_d_then DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_fJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_d_half"Div: ė JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_f JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_d_halfKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_d_then"Add: å JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_h_then DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_fJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_h_half"Div: ė JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_f JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_h_halfKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_h_then"Add: å JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_w_then DAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_fJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_w_half"Div: ė JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_f 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JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_fKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_d_else"Identity: Ĩ JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_fKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_h_else"Identity: Ĩ JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_one_fKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_w_else"Identity:h2bM KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_d_elsebL JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_d_elsebM KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_h_elsebL JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_h_elsebM KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_w_elsebL JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_w_else : Ĩ CAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_W BAffineGrid_test_affine_grid_2d_align_corners_expanded_function_oneOAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_steps_int"Range: ÷ OAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_steps_int EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_wQAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_steps_float"CastLike: î QAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_steps_float EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_step_wKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_steps"Mul: å FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_w KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_stepsGAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_0"Add: 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FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_start_d KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d_stepsGAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d_0"Add: ļ JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_D_H_WJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_H_W_D" Transpose* perm@@@ : å JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_H_W_D GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d_0GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d_1"Add: Ž GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d_1EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_d" Transpose* perm@@@ : ļ JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_D_H_WJAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_D_W_H" Transpose* perm@@@ : å JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_D_W_H GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_h_0GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_h_1"Add: Ž GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_h_1EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_h" Transpose* perm@@@ : ã GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_0 JAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zeros_D_H_WEAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w"Add: ė EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_minus_oneLAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_w_usqzed" Unsqueeze: ė EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_h 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PAffineGrid_test_affine_grid_2d_align_corners_expanded_function_ones_D_H_W_usqzedLAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid"Concat* axis˙˙˙˙˙˙˙˙˙ : SAffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_shape_DHW_4"Constant* value_ints@˙˙˙˙˙˙˙˙˙@ : ‚ LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid SAffineGrid_test_affine_grid_2d_align_corners_expanded_function_constant_shape_DHW_4RAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_DHW_4"Reshape: ļ RAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_DHW_4SAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_4_DHW_" Transpose: ū SAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_4_DHW_ GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_3dRAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_4_DHW"CastLike: ô GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_3d RAffineGrid_test_affine_grid_2d_align_corners_expanded_function_original_grid_4_DHWKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_N_3_DHW"MatMul: ¸ KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_N_3_DHWKAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_N_DHW_3" Transpose* perm@@@ : ģ @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_N @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_D @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_H @AffineGrid_test_affine_grid_2d_align_corners_expanded_function_W GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_three_1dHAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_D_H_W_3"Concat* axis˙˙˙˙˙˙˙˙˙ : đ KAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_N_DHW_3 HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_N_D_H_W_3LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_3d_else_"Reshape: ë LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_3d_else_ GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_theta_3dFAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_3d"CastLike: ° NAffineGrid_test_affine_grid_2d_align_corners_expanded_function_condition_is_2dgrid"If*Ę then_branch2ˇ č FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_3d EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_one_1dLAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_squeezed"Squeeze: ų LAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_squeezed FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_zero_1d EAffineGrid_test_affine_grid_2d_align_corners_expanded_function_two_1d GAffineGrid_test_affine_grid_2d_align_corners_expanded_function_three_1dHAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_then"Slice:g1bJ HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_then *„ else_branch2ņ ž FAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_3dHAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_else"Identity:g2bJ HAffineGrid_test_affine_grid_2d_align_corners_expanded_function_grid_else :*test_affine_grid_2d_align_corners_expandedZ theta    Z size  b grid     B test_data_set_0/000077500000000000000000000000001511334557700346765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners_expandedinput_0.pb000066400000000000000000000001011511334557700365670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_align_corners_expanded/test_data_set_0BthetaJ0ˆb‹?[oRĀ @Įm@Ė“c@;?8ųx@!"†?€ø?` ÜŊŗĨ@–ŒT> '@WĘ?‹p<@‰qV?÷ÕQ@]Œ“?d;g@ößģ?ãÕ?<īŸ>ģĪ˙?О ?JM@Fq?ļ˛*@™ö ?"@@2JÉ?Ž}U@˝ņ?9‰ą?Hs;?TÜ?= †?u@Ö`Ž?áô@n´Ö?MZ.@˙?ēŋC@Đ­@ Ž?yw“?gظ?Ëģ?@Ŗã?Ģä? 7@"9@xœ@îb@ä2@ēŒ.@onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_expanded/000077500000000000000000000000001511334557700270065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_expanded/model.onnx000066400000000000000000000546771511334557700310350ustar00rootroot00000000000000  backend-test:Ĩŗ T4AffineGrid_test_affine_grid_2d_expanded_function_one"Constant* value_int : T4AffineGrid_test_affine_grid_2d_expanded_function_two"Constant* value_int : U5AffineGrid_test_affine_grid_2d_expanded_function_zero"Constant* value_int : U5AffineGrid_test_affine_grid_2d_expanded_function_four"Constant* value_int : X7AffineGrid_test_affine_grid_2d_expanded_function_one_1d"Constant* value_ints@ : Y8AffineGrid_test_affine_grid_2d_expanded_function_zero_1d"Constant* value_ints@ : c:AffineGrid_test_affine_grid_2d_expanded_function_minus_one"Constant* value_int˙˙˙˙˙˙˙˙˙ :  :AffineGrid_test_affine_grid_2d_expanded_function_minus_one thetaAffineGrid_test_affine_grid_2d_expanded_function_gather_idx_23"Reshape:  2AffineGrid_test_affine_grid_2d_expanded_function_N 9AffineGrid_test_affine_grid_2d_expanded_function_shape_23:AffineGrid_test_affine_grid_2d_expanded_function_shape_N23"Concat* axis : Į >AffineGrid_test_affine_grid_2d_expanded_function_gather_idx_23 :AffineGrid_test_affine_grid_2d_expanded_function_shape_N23?AffineGrid_test_affine_grid_2d_expanded_function_gather_idx_N23"Expand: ĸ theta ?AffineGrid_test_affine_grid_2d_expanded_function_gather_idx_N239AffineGrid_test_affine_grid_2d_expanded_function_thetaN23"GatherElements* axis : Ī 9AffineGrid_test_affine_grid_2d_expanded_function_thetaN233AffineGrid_test_affine_grid_2d_expanded_function_r13AffineGrid_test_affine_grid_2d_expanded_function_r2"Split* axis * num_outputs : v 3AffineGrid_test_affine_grid_2d_expanded_function_r14AffineGrid_test_affine_grid_2d_expanded_function_r1_"Squeeze: v 3AffineGrid_test_affine_grid_2d_expanded_function_r24AffineGrid_test_affine_grid_2d_expanded_function_r2_"Squeeze:  4AffineGrid_test_affine_grid_2d_expanded_function_r1_4AffineGrid_test_affine_grid_2d_expanded_function_r114AffineGrid_test_affine_grid_2d_expanded_function_r123AffineGrid_test_affine_grid_2d_expanded_function_t1"Split* axis * num_outputs :  4AffineGrid_test_affine_grid_2d_expanded_function_r2_4AffineGrid_test_affine_grid_2d_expanded_function_r214AffineGrid_test_affine_grid_2d_expanded_function_r223AffineGrid_test_affine_grid_2d_expanded_function_t2"Split* axis * num_outputs : { 4AffineGrid_test_affine_grid_2d_expanded_function_r21:AffineGrid_test_affine_grid_2d_expanded_function_r11_shape"Shape:  :AffineGrid_test_affine_grid_2d_expanded_function_r11_shape?AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d_"ConstantOfShape: ” ?AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d_ theta>AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d"CastLike: ž >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d 6AffineGrid_test_affine_grid_2d_expanded_function_one_f=AffineGrid_test_affine_grid_2d_expanded_function_float_one_1d"Add: ­ 4AffineGrid_test_affine_grid_2d_expanded_function_r11 4AffineGrid_test_affine_grid_2d_expanded_function_r12 >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d 3AffineGrid_test_affine_grid_2d_expanded_function_t13AffineGrid_test_affine_grid_2d_expanded_function_R1"Concat* axis : ­ 4AffineGrid_test_affine_grid_2d_expanded_function_r21 4AffineGrid_test_affine_grid_2d_expanded_function_r22 >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d 3AffineGrid_test_affine_grid_2d_expanded_function_t23AffineGrid_test_affine_grid_2d_expanded_function_R2"Concat* axis : Ë >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d =AffineGrid_test_affine_grid_2d_expanded_function_float_one_1d >AffineGrid_test_affine_grid_2d_expanded_function_float_zero_1d3AffineGrid_test_affine_grid_2d_expanded_function_R3"Concat* axis : ą 3AffineGrid_test_affine_grid_2d_expanded_function_R1 7AffineGrid_test_affine_grid_2d_expanded_function_one_1d4AffineGrid_test_affine_grid_2d_expanded_function_R1_" Unsqueeze: ą 3AffineGrid_test_affine_grid_2d_expanded_function_R2 7AffineGrid_test_affine_grid_2d_expanded_function_one_1d4AffineGrid_test_affine_grid_2d_expanded_function_R2_" Unsqueeze: ą 3AffineGrid_test_affine_grid_2d_expanded_function_R3 7AffineGrid_test_affine_grid_2d_expanded_function_one_1d4AffineGrid_test_affine_grid_2d_expanded_function_R3_" Unsqueeze: ö 4AffineGrid_test_affine_grid_2d_expanded_function_R1_ 4AffineGrid_test_affine_grid_2d_expanded_function_R2_ 4AffineGrid_test_affine_grid_2d_expanded_function_R3_;AffineGrid_test_affine_grid_2d_expanded_function_theta_then"Concat* axis :g3b= ;AffineGrid_test_affine_grid_2d_expanded_function_theta_then *¨ else_branch2• P theta;AffineGrid_test_affine_grid_2d_expanded_function_theta_else"Identity:g4b= ;AffineGrid_test_affine_grid_2d_expanded_function_theta_else : X7AffineGrid_test_affine_grid_2d_expanded_function_two_1d"Constant* value_ints@ : Z9AffineGrid_test_affine_grid_2d_expanded_function_three_1d"Constant* value_ints@ : Y8AffineGrid_test_affine_grid_2d_expanded_function_five_1d"Constant* value_ints@ : € ;AffineGrid_test_affine_grid_2d_expanded_function_size_NCDHW 7AffineGrid_test_affine_grid_2d_expanded_function_two_1d 8AffineGrid_test_affine_grid_2d_expanded_function_five_1dEAffineGrid_test_affine_grid_2d_expanded_function_constant_D_H_W_shape"Slice: ™ EAffineGrid_test_affine_grid_2d_expanded_function_constant_D_H_W_shape=AffineGrid_test_affine_grid_2d_expanded_function_zeros_D_H_W_"ConstantOfShape:  =AffineGrid_test_affine_grid_2d_expanded_function_zeros_D_H_W_ theta AffineGrid_test_affine_grid_2d_expanded_function_grid_w_usqzed" Unsqueeze:  7AffineGrid_test_affine_grid_2d_expanded_function_grid_h :AffineGrid_test_affine_grid_2d_expanded_function_minus_one>AffineGrid_test_affine_grid_2d_expanded_function_grid_h_usqzed" Unsqueeze:  7AffineGrid_test_affine_grid_2d_expanded_function_grid_d :AffineGrid_test_affine_grid_2d_expanded_function_minus_one>AffineGrid_test_affine_grid_2d_expanded_function_grid_d_usqzed" Unsqueeze: Ę ;AffineGrid_test_affine_grid_2d_expanded_function_ones_D_H_W :AffineGrid_test_affine_grid_2d_expanded_function_minus_oneBAffineGrid_test_affine_grid_2d_expanded_function_ones_D_H_W_usqzed" Unsqueeze: ä >AffineGrid_test_affine_grid_2d_expanded_function_grid_w_usqzed >AffineGrid_test_affine_grid_2d_expanded_function_grid_h_usqzed >AffineGrid_test_affine_grid_2d_expanded_function_grid_d_usqzed BAffineGrid_test_affine_grid_2d_expanded_function_ones_D_H_W_usqzed>AffineGrid_test_affine_grid_2d_expanded_function_original_grid"Concat* axis˙˙˙˙˙˙˙˙˙ : qEAffineGrid_test_affine_grid_2d_expanded_function_constant_shape_DHW_4"Constant* value_ints@˙˙˙˙˙˙˙˙˙@ : Ø >AffineGrid_test_affine_grid_2d_expanded_function_original_grid EAffineGrid_test_affine_grid_2d_expanded_function_constant_shape_DHW_4DAffineGrid_test_affine_grid_2d_expanded_function_original_grid_DHW_4"Reshape: š DAffineGrid_test_affine_grid_2d_expanded_function_original_grid_DHW_4EAffineGrid_test_affine_grid_2d_expanded_function_original_grid_4_DHW_" Transpose: Ô EAffineGrid_test_affine_grid_2d_expanded_function_original_grid_4_DHW_ 9AffineGrid_test_affine_grid_2d_expanded_function_theta_3dDAffineGrid_test_affine_grid_2d_expanded_function_original_grid_4_DHW"CastLike: Ę 9AffineGrid_test_affine_grid_2d_expanded_function_theta_3d DAffineGrid_test_affine_grid_2d_expanded_function_original_grid_4_DHW=AffineGrid_test_affine_grid_2d_expanded_function_grid_N_3_DHW"MatMul: œ =AffineGrid_test_affine_grid_2d_expanded_function_grid_N_3_DHW=AffineGrid_test_affine_grid_2d_expanded_function_grid_N_DHW_3" Transpose* perm@@@ : į 2AffineGrid_test_affine_grid_2d_expanded_function_N 2AffineGrid_test_affine_grid_2d_expanded_function_D 2AffineGrid_test_affine_grid_2d_expanded_function_H 2AffineGrid_test_affine_grid_2d_expanded_function_W 9AffineGrid_test_affine_grid_2d_expanded_function_three_1d:AffineGrid_test_affine_grid_2d_expanded_function_N_D_H_W_3"Concat* axis˙˙˙˙˙˙˙˙˙ : Æ =AffineGrid_test_affine_grid_2d_expanded_function_grid_N_DHW_3 :AffineGrid_test_affine_grid_2d_expanded_function_N_D_H_W_3>AffineGrid_test_affine_grid_2d_expanded_function_grid_3d_else_"Reshape: Á >AffineGrid_test_affine_grid_2d_expanded_function_grid_3d_else_ 9AffineGrid_test_affine_grid_2d_expanded_function_theta_3d8AffineGrid_test_affine_grid_2d_expanded_function_grid_3d"CastLike: ú @AffineGrid_test_affine_grid_2d_expanded_function_condition_is_2dgrid"If*Ė then_branch2š ž 8AffineGrid_test_affine_grid_2d_expanded_function_grid_3d 7AffineGrid_test_affine_grid_2d_expanded_function_one_1d>AffineGrid_test_affine_grid_2d_expanded_function_grid_squeezed"Squeeze: ŗ >AffineGrid_test_affine_grid_2d_expanded_function_grid_squeezed 8AffineGrid_test_affine_grid_2d_expanded_function_zero_1d 7AffineGrid_test_affine_grid_2d_expanded_function_two_1d 9AffineGrid_test_affine_grid_2d_expanded_function_three_1d:AffineGrid_test_affine_grid_2d_expanded_function_grid_then"Slice:g1b< :AffineGrid_test_affine_grid_2d_expanded_function_grid_then *Ú else_branch2Į ‚ 8AffineGrid_test_affine_grid_2d_expanded_function_grid_3d:AffineGrid_test_affine_grid_2d_expanded_function_grid_else"Identity:g2b< :AffineGrid_test_affine_grid_2d_expanded_function_grid_else :test_affine_grid_2d_expandedZ theta    Z size  b grid     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_expanded/test_data_set_0/000077500000000000000000000000001511334557700320505ustar00rootroot00000000000000input_0.pb000066400000000000000000000001011511334557700336620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_2d_expanded/test_data_set_0BthetaJ0ˆb‹?[oRĀ @Įm@ÛwĀę5`@ĻļLĀqw@’!Ā V‡@íÚėŋ—ķ’@ž‘–ŋc&ģ?~xƒĀ‘œé?eĖ[Ā_ @ͧ0ĀvD#@6ƒŒ:@<Ŋ´ŋŖēQ@č<ŋĸĪ@˙iž'¤!@Āø =Ŧx3@ ˜>1ME@‰;?ļ!W@ˆqR?:öh@Ã͊?+ž@̰Ü=°r@(˜Ŋ>5G%@"?ē7@8e?>đH@7”?ÃÄZ@Ōĩ?hŲæ?;%ã>9A@œČ4?ž@›ūw?Bę(@Mš?Įž:@L5ŋ?L“L@KĐā?yvĘ?%G?ƒî?’b…?Fä@‘ũĻ?˸@‘˜Č?P,@3ę?Õa>@Hį@‹Ž?×Ŏ?•ŧŅ?Ö`°?žeõ?ÕûŅ?T‡ @Ֆķ?Ų[@ę˜ @^00@jf@onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/000077500000000000000000000000001511334557700251375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/model.onnx000066400000000000000000000002721511334557700271440ustar00rootroot00000000000000  backend-test:Ą 5 theta sizegrid" AffineGrid* align_corners test_affine_grid_3dZ theta    Z size  b" grid      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/test_data_set_0/000077500000000000000000000000001511334557700302015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/test_data_set_0/input_0.pb000066400000000000000000000001611511334557700321000ustar00rootroot00000000000000BthetaJ`yˇ+@QYKŋû‡_> @ L?[oR@ļÃŋ33SĀ'}>׹Ü?HīŸ=Íˌŋsļžî€?q#ŗ? @ß4>%ņv=ÍlŋÍˌ?Íˌŋffæžffæ>ÍĖ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/test_data_set_0/input_1.pb000066400000000000000000000000641511334557700321030ustar00rootroot00000000000000BsizeJ(onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d/test_data_set_0/output_0.pb000066400000000000000000000055251511334557700323120ustar00rootroot00000000000000BgridJĀ2O@ˇŖÁƒœ/Ā­*„@ŒģĀ^V*ĀAÉ @a]´Ā8%ĀÖgŊ@6ē­ĀĘĀjÚ@ §ĀėƒĀū¤ö@ās ĀĮ=ĀwÂ:@q—ĀōxŸ˙s@FęĀ˜eüŋdž–@GŠĀMŲņŋø<ŗ@đŖƒĀMįŋŒÛĪ@‹zĀļĀÜŋ zė@4ģlĀk4Ōŋģl&@XîZĀÁĒŽŋäŠ_@¨MĀv¤ŋ†sŒ@Ŧa@Ā*’™ŋŠ@V3ĀߏŋްÅ@Õ%Ā“y„ŋCOâ@ĒŽÚsŋ@ÍÁĀ<Į,ŋ(TK@îöōŋĨŽŋ¨H‚@BjØŋ–ŋ=įž@–ŨŊŋīúÚžŅ…ģ@ęPŖŋÂɰže$Ø@>Ĉŋ”˜†žŠ‚û? 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Ū´?…@#ŗ¨?"ŪŊ?R¸Î?1–?:ŪÆ?gf–?ąå@‡š?š™I@¸Ë@ŸšĻ?¤p-@ŋą@ˇš¯?ŽG@Œ/û?Īš¸?q=ę?šûč?įšÁ?…ëą?¨ĮÖ?˙šĘ?43s?ė 9@L•Ą?43;@ķī/@c•Ē?> @úÕ&@{•ŗ?Há@ŧ@“•ŧ?¤pÍ? ĸ@Ģ•Å?š•?ˆ @ÕÎ?š™9?(.Y@qĨ?ÍĖ,@/P@(qŽ?×Ŗ@6úF@@qˇ?Ãõč?=ā=@XqĀ?ØŖ°?DÆ4@pqÉ?ØŖp?KŦ+@ˆqŌ??ĩS@$P?š™y@ŧ9 @TP ?¤p]@Ã@„P2?ŽGA@” ō?´PD?¸%@ĸ×ß?äPV?Ãõ@°ŖÍ?Qh?š™Ų?đw4@­?33k@÷]+@Ũ(?> O@ūC"@ :?Há2@*@=L?R¸@ @m^?šõ?ö@p?ÍĖŧ?,œT@7ŋ?ÍĖ\@3‚K@fŋ/?×Ŗ@@:hB@–ŋA?áz$@AN9@ÆŋS?ėQ@H40@öŋe?ėQØ?O'@&Āw? ?gĀt@Āv%?gfN@nĻk@đv7?q=2@uŒb@ wI?{@}rY@Ow[? ×ķ?„XP@wm?…ģ?‹>G@¯w?33ƒ?QrŠ@I.-?@@Uå…@y.?? ×#@XX@Š.Q?Ž@¸–y@Ų.c?> ×?ŋ|p@/u?R¸ž?Æbg@œ—ƒ?ÍĖL?0 P@1;vŊff†@7đF@6'<×Ŗp@>Ö=@få¤=ázT@Eŧ4@ss>ėQ8@Lĸ+@2tb>ö(@Sˆ"@y:•>@k.p@=…õŧgf~@rg@`@%=q=b@yú]@¯Ąâ={F@āT@—Q9>…ë)@ˆÆK@+Š€>Â @ŦB@‹Š¤>33ã?S)ˆ@ô59p@Wœƒ@y\= ×S@ĩ~@ü.>Ž7@ŧu@ŧ/X>…@Ãęk@>>R¸ū?ĘĐb@ž´>ffÆ?q;˜@ ]ø<š™a@uŽ“@ÂÎ=¤pE@x!@! />ŽG)@|”Š@ā w>¸ @†@P‡Ÿ>…ëá?ƒz@°‡Ã>š™Š?M¨@§w=43S@’ĀŖ@†ę>> 7@–3Ÿ@EëM>Há@šĻš@öŠ>¤pũ?–@böŽ>šÅ?ĄŒ‘@ÂöŌ>Íˌ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expanded/000077500000000000000000000000001511334557700317145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expanded/model.onnx000066400000000000000000000661551511334557700337350ustar00rootroot00000000000000  backend-test:ĶØ bBAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one"Constant* value_int : bBAffineGrid_test_affine_grid_3d_align_corners_expanded_function_two"Constant* value_int : cCAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero"Constant* value_int : cCAffineGrid_test_affine_grid_3d_align_corners_expanded_function_four"Constant* value_int : fEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_1d"Constant* value_ints@ : gFAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero_1d"Constant* value_ints@ : qHAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_one"Constant* value_int˙˙˙˙˙˙˙˙˙ : Š HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_one thetaJAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_one_f"CastLike: Ÿ CAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero thetaEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero_f"CastLike:  BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one thetaDAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_f"CastLike:  BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_two thetaDAffineGrid_test_affine_grid_3d_align_corners_expanded_function_two_f"CastLike: uUAffineGrid_test_affine_grid_3d_align_corners_expanded_function_constant_align_corners"Constant* value_int : ‡ UAffineGrid_test_affine_grid_3d_align_corners_expanded_function_constant_align_corners CAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero`AffineGrid_test_affine_grid_3d_align_corners_expanded_function_constant_align_corners_equal_zero"Equal: X sizeHAffineGrid_test_affine_grid_3d_align_corners_expanded_function_size_ndim"Size: č HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_size_ndim CAffineGrid_test_affine_grid_3d_align_corners_expanded_function_fourNAffineGrid_test_affine_grid_3d_align_corners_expanded_function_condition_is_2d"Equal: – NAffineGrid_test_affine_grid_3d_align_corners_expanded_function_condition_is_2d@AffineGrid_test_affine_grid_3d_align_corners_expanded_function_N@AffineGrid_test_affine_grid_3d_align_corners_expanded_function_C@AffineGrid_test_affine_grid_3d_align_corners_expanded_function_D@AffineGrid_test_affine_grid_3d_align_corners_expanded_function_H@AffineGrid_test_affine_grid_3d_align_corners_expanded_function_W"If*ã then_branch2Đ ŋ sizeEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_thenEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_C_thenEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_H_thenEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_W_then"Split* num_outputs : š EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_1dEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_D_then"Identity:g1bG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_thenbG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_C_thenbG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_D_thenbG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_H_thenbG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_W_then * else_branch2ú † sizeEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_elseEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_C_elseEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_D_elseEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_H_elseEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_W_else"Split* num_outputs :g2bG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_elsebG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_C_elsebG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_D_elsebG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_H_elsebG EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_W_else : Ŧ @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_N @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_C @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_D 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AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r2BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r2_"Squeeze: š BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r1_BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r11BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r12AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_t1"Split* axis * num_outputs : š BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r2_BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r21BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r22AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_t2"Split* axis * num_outputs : — BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r21HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r11_shape"Shape: Ŧ HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r11_shapeMAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d_"ConstantOfShape: ° MAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d_ thetaLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d"CastLike: č LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d DAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_fKAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_one_1d"Add: ķ BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r11 BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r12 LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_t1AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_R1"Concat* axis : ķ BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r21 BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_r22 LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_t2AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_R2"Concat* axis : ‘ LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1d KAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_one_1d LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_float_zero_1dAAffineGrid_test_affine_grid_3d_align_corners_expanded_function_R3"Concat* axis : Û AAffineGrid_test_affine_grid_3d_align_corners_expanded_function_R1 EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_1dBAffineGrid_test_affine_grid_3d_align_corners_expanded_function_R1_" Unsqueeze: Û 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î QAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_steps_float EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_step_hKAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_steps"Mul: å FAffineGrid_test_affine_grid_3d_align_corners_expanded_function_start_h KAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_stepsGAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_0"Add: Ĩ CAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_D BAffineGrid_test_affine_grid_3d_align_corners_expanded_function_oneOAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_steps_int"Range: ÷ OAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_steps_int 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GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_0GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_1"Add: Ž GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_1EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d" Transpose* perm@@@ : ļ JAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zeros_D_H_WJAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zeros_D_W_H" Transpose* perm@@@ : å JAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zeros_D_W_H GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_0GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_1"Add: Ž GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_1EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h" Transpose* perm@@@ : ã GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_w_0 JAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zeros_D_H_WEAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_w"Add: ė EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_w HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_oneLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_w_usqzed" Unsqueeze: ė EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_oneLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_usqzed" Unsqueeze: ė EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_oneLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_usqzed" Unsqueeze: ô IAffineGrid_test_affine_grid_3d_align_corners_expanded_function_ones_D_H_W HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_minus_onePAffineGrid_test_affine_grid_3d_align_corners_expanded_function_ones_D_H_W_usqzed" Unsqueeze: Ē LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_w_usqzed LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_h_usqzed LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_d_usqzed PAffineGrid_test_affine_grid_3d_align_corners_expanded_function_ones_D_H_W_usqzedLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid"Concat* axis˙˙˙˙˙˙˙˙˙ : SAffineGrid_test_affine_grid_3d_align_corners_expanded_function_constant_shape_DHW_4"Constant* value_ints@˙˙˙˙˙˙˙˙˙@ : ‚ LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid SAffineGrid_test_affine_grid_3d_align_corners_expanded_function_constant_shape_DHW_4RAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_DHW_4"Reshape: ļ RAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_DHW_4SAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_4_DHW_" Transpose: ū SAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_4_DHW_ GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_theta_3dRAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_4_DHW"CastLike: ô GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_theta_3d RAffineGrid_test_affine_grid_3d_align_corners_expanded_function_original_grid_4_DHWKAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_N_3_DHW"MatMul: ¸ KAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_N_3_DHWKAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_N_DHW_3" Transpose* perm@@@ : ģ @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_N @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_D @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_H @AffineGrid_test_affine_grid_3d_align_corners_expanded_function_W GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_three_1dHAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_D_H_W_3"Concat* axis˙˙˙˙˙˙˙˙˙ : đ KAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_N_DHW_3 HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_N_D_H_W_3LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_3d_else_"Reshape: ë LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_3d_else_ GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_theta_3dFAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_3d"CastLike: ° NAffineGrid_test_affine_grid_3d_align_corners_expanded_function_condition_is_2dgrid"If*Ę then_branch2ˇ č FAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_3d EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_one_1dLAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_squeezed"Squeeze: ų LAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_squeezed FAffineGrid_test_affine_grid_3d_align_corners_expanded_function_zero_1d EAffineGrid_test_affine_grid_3d_align_corners_expanded_function_two_1d GAffineGrid_test_affine_grid_3d_align_corners_expanded_function_three_1dHAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_then"Slice:g1bJ HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_then *„ else_branch2ņ ž FAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_3dHAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_else"Identity:g2bJ HAffineGrid_test_affine_grid_3d_align_corners_expanded_function_grid_else :*test_affine_grid_3d_align_corners_expandedZ theta    Z size  b" grid      B test_data_set_0/000077500000000000000000000000001511334557700346775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expandedinput_0.pb000066400000000000000000000001611511334557700365760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expanded/test_data_set_0BthetaJ`yˇ+@QYKŋû‡_> @ L?[oR@ļÃŋ33SĀ'}>׹Ü?HīŸ=Íˌŋsļžî€?q#ŗ? @ß4>%ņv=ÍlŋÍˌ?Íˌŋffæžffæ>ÍĖ @input_1.pb000066400000000000000000000000641511334557700366010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expanded/test_data_set_0BsizeJ(output_0.pb000066400000000000000000000055251511334557700370100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_align_corners_expanded/test_data_set_0BgridJĀ\&9@QÂÕĀ=‘IĀ&Ö}@QËÍĀ=CĀøBĄ@PÔÅĀãč<ĀŨšÃ@PŨŊĀļ”6ĀÂōå@Pæĩˆ@0ĀS%AOī­Ā[ė)Ā1ģ@{&ĄĀČdĀûjd@z/™Āš 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7@–3Ÿ@EëM>Há@šĻš@öŠ>¤pũ?–@böŽ>šÅ?ĄŒ‘@ÂöŌ>Íˌ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/000077500000000000000000000000001511334557700270075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/model.onnx000066400000000000000000000547031511334557700310240ustar00rootroot00000000000000  backend-test:Šŗ T4AffineGrid_test_affine_grid_3d_expanded_function_one"Constant* value_int : T4AffineGrid_test_affine_grid_3d_expanded_function_two"Constant* value_int : U5AffineGrid_test_affine_grid_3d_expanded_function_zero"Constant* value_int : U5AffineGrid_test_affine_grid_3d_expanded_function_four"Constant* value_int : X7AffineGrid_test_affine_grid_3d_expanded_function_one_1d"Constant* value_ints@ : Y8AffineGrid_test_affine_grid_3d_expanded_function_zero_1d"Constant* value_ints@ : c:AffineGrid_test_affine_grid_3d_expanded_function_minus_one"Constant* value_int˙˙˙˙˙˙˙˙˙ :  :AffineGrid_test_affine_grid_3d_expanded_function_minus_one thetaAffineGrid_test_affine_grid_3d_expanded_function_gather_idx_23"Reshape:  2AffineGrid_test_affine_grid_3d_expanded_function_N 9AffineGrid_test_affine_grid_3d_expanded_function_shape_23:AffineGrid_test_affine_grid_3d_expanded_function_shape_N23"Concat* axis : Į >AffineGrid_test_affine_grid_3d_expanded_function_gather_idx_23 :AffineGrid_test_affine_grid_3d_expanded_function_shape_N23?AffineGrid_test_affine_grid_3d_expanded_function_gather_idx_N23"Expand: ĸ theta ?AffineGrid_test_affine_grid_3d_expanded_function_gather_idx_N239AffineGrid_test_affine_grid_3d_expanded_function_thetaN23"GatherElements* axis : Ī 9AffineGrid_test_affine_grid_3d_expanded_function_thetaN233AffineGrid_test_affine_grid_3d_expanded_function_r13AffineGrid_test_affine_grid_3d_expanded_function_r2"Split* axis * num_outputs : v 3AffineGrid_test_affine_grid_3d_expanded_function_r14AffineGrid_test_affine_grid_3d_expanded_function_r1_"Squeeze: v 3AffineGrid_test_affine_grid_3d_expanded_function_r24AffineGrid_test_affine_grid_3d_expanded_function_r2_"Squeeze:  4AffineGrid_test_affine_grid_3d_expanded_function_r1_4AffineGrid_test_affine_grid_3d_expanded_function_r114AffineGrid_test_affine_grid_3d_expanded_function_r123AffineGrid_test_affine_grid_3d_expanded_function_t1"Split* axis * num_outputs :  4AffineGrid_test_affine_grid_3d_expanded_function_r2_4AffineGrid_test_affine_grid_3d_expanded_function_r214AffineGrid_test_affine_grid_3d_expanded_function_r223AffineGrid_test_affine_grid_3d_expanded_function_t2"Split* axis * num_outputs : { 4AffineGrid_test_affine_grid_3d_expanded_function_r21:AffineGrid_test_affine_grid_3d_expanded_function_r11_shape"Shape:  :AffineGrid_test_affine_grid_3d_expanded_function_r11_shape?AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d_"ConstantOfShape: ” ?AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d_ theta>AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d"CastLike: ž >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d 6AffineGrid_test_affine_grid_3d_expanded_function_one_f=AffineGrid_test_affine_grid_3d_expanded_function_float_one_1d"Add: ­ 4AffineGrid_test_affine_grid_3d_expanded_function_r11 4AffineGrid_test_affine_grid_3d_expanded_function_r12 >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d 3AffineGrid_test_affine_grid_3d_expanded_function_t13AffineGrid_test_affine_grid_3d_expanded_function_R1"Concat* axis : ­ 4AffineGrid_test_affine_grid_3d_expanded_function_r21 4AffineGrid_test_affine_grid_3d_expanded_function_r22 >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d 3AffineGrid_test_affine_grid_3d_expanded_function_t23AffineGrid_test_affine_grid_3d_expanded_function_R2"Concat* axis : Ë >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d =AffineGrid_test_affine_grid_3d_expanded_function_float_one_1d >AffineGrid_test_affine_grid_3d_expanded_function_float_zero_1d3AffineGrid_test_affine_grid_3d_expanded_function_R3"Concat* axis : ą 3AffineGrid_test_affine_grid_3d_expanded_function_R1 7AffineGrid_test_affine_grid_3d_expanded_function_one_1d4AffineGrid_test_affine_grid_3d_expanded_function_R1_" Unsqueeze: ą 3AffineGrid_test_affine_grid_3d_expanded_function_R2 7AffineGrid_test_affine_grid_3d_expanded_function_one_1d4AffineGrid_test_affine_grid_3d_expanded_function_R2_" Unsqueeze: ą 3AffineGrid_test_affine_grid_3d_expanded_function_R3 7AffineGrid_test_affine_grid_3d_expanded_function_one_1d4AffineGrid_test_affine_grid_3d_expanded_function_R3_" Unsqueeze: ö 4AffineGrid_test_affine_grid_3d_expanded_function_R1_ 4AffineGrid_test_affine_grid_3d_expanded_function_R2_ 4AffineGrid_test_affine_grid_3d_expanded_function_R3_;AffineGrid_test_affine_grid_3d_expanded_function_theta_then"Concat* axis :g3b= ;AffineGrid_test_affine_grid_3d_expanded_function_theta_then *¨ else_branch2• P theta;AffineGrid_test_affine_grid_3d_expanded_function_theta_else"Identity:g4b= ;AffineGrid_test_affine_grid_3d_expanded_function_theta_else : X7AffineGrid_test_affine_grid_3d_expanded_function_two_1d"Constant* value_ints@ : Z9AffineGrid_test_affine_grid_3d_expanded_function_three_1d"Constant* value_ints@ : Y8AffineGrid_test_affine_grid_3d_expanded_function_five_1d"Constant* value_ints@ : € ;AffineGrid_test_affine_grid_3d_expanded_function_size_NCDHW 7AffineGrid_test_affine_grid_3d_expanded_function_two_1d 8AffineGrid_test_affine_grid_3d_expanded_function_five_1dEAffineGrid_test_affine_grid_3d_expanded_function_constant_D_H_W_shape"Slice: ™ EAffineGrid_test_affine_grid_3d_expanded_function_constant_D_H_W_shape=AffineGrid_test_affine_grid_3d_expanded_function_zeros_D_H_W_"ConstantOfShape:  =AffineGrid_test_affine_grid_3d_expanded_function_zeros_D_H_W_ theta AffineGrid_test_affine_grid_3d_expanded_function_grid_w_usqzed" Unsqueeze:  7AffineGrid_test_affine_grid_3d_expanded_function_grid_h :AffineGrid_test_affine_grid_3d_expanded_function_minus_one>AffineGrid_test_affine_grid_3d_expanded_function_grid_h_usqzed" Unsqueeze:  7AffineGrid_test_affine_grid_3d_expanded_function_grid_d :AffineGrid_test_affine_grid_3d_expanded_function_minus_one>AffineGrid_test_affine_grid_3d_expanded_function_grid_d_usqzed" Unsqueeze: Ę ;AffineGrid_test_affine_grid_3d_expanded_function_ones_D_H_W :AffineGrid_test_affine_grid_3d_expanded_function_minus_oneBAffineGrid_test_affine_grid_3d_expanded_function_ones_D_H_W_usqzed" Unsqueeze: ä >AffineGrid_test_affine_grid_3d_expanded_function_grid_w_usqzed >AffineGrid_test_affine_grid_3d_expanded_function_grid_h_usqzed >AffineGrid_test_affine_grid_3d_expanded_function_grid_d_usqzed BAffineGrid_test_affine_grid_3d_expanded_function_ones_D_H_W_usqzed>AffineGrid_test_affine_grid_3d_expanded_function_original_grid"Concat* axis˙˙˙˙˙˙˙˙˙ : qEAffineGrid_test_affine_grid_3d_expanded_function_constant_shape_DHW_4"Constant* value_ints@˙˙˙˙˙˙˙˙˙@ : Ø >AffineGrid_test_affine_grid_3d_expanded_function_original_grid EAffineGrid_test_affine_grid_3d_expanded_function_constant_shape_DHW_4DAffineGrid_test_affine_grid_3d_expanded_function_original_grid_DHW_4"Reshape: š DAffineGrid_test_affine_grid_3d_expanded_function_original_grid_DHW_4EAffineGrid_test_affine_grid_3d_expanded_function_original_grid_4_DHW_" Transpose: Ô EAffineGrid_test_affine_grid_3d_expanded_function_original_grid_4_DHW_ 9AffineGrid_test_affine_grid_3d_expanded_function_theta_3dDAffineGrid_test_affine_grid_3d_expanded_function_original_grid_4_DHW"CastLike: Ę 9AffineGrid_test_affine_grid_3d_expanded_function_theta_3d DAffineGrid_test_affine_grid_3d_expanded_function_original_grid_4_DHW=AffineGrid_test_affine_grid_3d_expanded_function_grid_N_3_DHW"MatMul: œ =AffineGrid_test_affine_grid_3d_expanded_function_grid_N_3_DHW=AffineGrid_test_affine_grid_3d_expanded_function_grid_N_DHW_3" Transpose* perm@@@ : į 2AffineGrid_test_affine_grid_3d_expanded_function_N 2AffineGrid_test_affine_grid_3d_expanded_function_D 2AffineGrid_test_affine_grid_3d_expanded_function_H 2AffineGrid_test_affine_grid_3d_expanded_function_W 9AffineGrid_test_affine_grid_3d_expanded_function_three_1d:AffineGrid_test_affine_grid_3d_expanded_function_N_D_H_W_3"Concat* axis˙˙˙˙˙˙˙˙˙ : Æ =AffineGrid_test_affine_grid_3d_expanded_function_grid_N_DHW_3 :AffineGrid_test_affine_grid_3d_expanded_function_N_D_H_W_3>AffineGrid_test_affine_grid_3d_expanded_function_grid_3d_else_"Reshape: Á >AffineGrid_test_affine_grid_3d_expanded_function_grid_3d_else_ 9AffineGrid_test_affine_grid_3d_expanded_function_theta_3d8AffineGrid_test_affine_grid_3d_expanded_function_grid_3d"CastLike: ú @AffineGrid_test_affine_grid_3d_expanded_function_condition_is_2dgrid"If*Ė then_branch2š ž 8AffineGrid_test_affine_grid_3d_expanded_function_grid_3d 7AffineGrid_test_affine_grid_3d_expanded_function_one_1d>AffineGrid_test_affine_grid_3d_expanded_function_grid_squeezed"Squeeze: ŗ >AffineGrid_test_affine_grid_3d_expanded_function_grid_squeezed 8AffineGrid_test_affine_grid_3d_expanded_function_zero_1d 7AffineGrid_test_affine_grid_3d_expanded_function_two_1d 9AffineGrid_test_affine_grid_3d_expanded_function_three_1d:AffineGrid_test_affine_grid_3d_expanded_function_grid_then"Slice:g1b< :AffineGrid_test_affine_grid_3d_expanded_function_grid_then *Ú else_branch2Į ‚ 8AffineGrid_test_affine_grid_3d_expanded_function_grid_3d:AffineGrid_test_affine_grid_3d_expanded_function_grid_else"Identity:g2b< :AffineGrid_test_affine_grid_3d_expanded_function_grid_else :test_affine_grid_3d_expandedZ theta    Z size  b" grid      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/test_data_set_0/000077500000000000000000000000001511334557700320515ustar00rootroot00000000000000input_0.pb000066400000000000000000000001611511334557700336710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/test_data_set_0BthetaJ`yˇ+@QYKŋû‡_> @ L?[oR@ļÃŋ33SĀ'}>׹Ü?HīŸ=Íˌŋsļžî€?q#ŗ? @ß4>%ņv=ÍlŋÍˌ?Íˌŋffæžffæ>ÍĖ @input_1.pb000066400000000000000000000000641511334557700336740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/test_data_set_0BsizeJ(output_0.pb000066400000000000000000000055251511334557700341030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_affine_grid_3d_expanded/test_data_set_0BgridJĀ2O@ˇŖÁƒœ/Ā­*„@ŒģĀ^V*ĀAÉ @a]´Ā8%ĀÖgŊ@6ē­ĀĘĀjÚ@ §ĀėƒĀū¤ö@ās ĀĮ=ĀwÂ:@q—ĀōxŸ˙s@FęĀ˜eüŋdž–@GŠĀMŲņŋø<ŗ@đŖƒĀMįŋŒÛĪ@‹zĀļĀÜŋ 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>îŋ›>ÛUš=nmŲ>ŦfÜ=ĩv?ly|>output_0.pb000066400000000000000000000014161511334557700340370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_attn_mask/test_data_set_0BYJ€JA]>x‡?Ō-õ>6¸Œ>ឝ>˙ķ>ŧĖĄ>6˛õ>*:4?´;÷>œ3?ė ?ą{Ŋ>í<?z?ë‰.?Bq%?…‚Ö>ˆøĖ>ž$?Ķž”>$üÕ>ū!?2Č ?EW>=B ?ąŋø>P> >‘Ą>˚ū>"cĩ>B˛î>#?.?ĮãĐ>)k?Ū ?šį>VĪ?úģ?(?…M&?ˆSų>ĩiØ>R.,?‚›>Ū æ>Í ?å)?h;j>Ћ?™aî>‘nŽ>ŗ-ĩ>†ã>Ž>ËÄÛ>Ķņ)?ūpâ>úÚ?„P.?čöž>ú+?JÜ ?gŪ1?’î:?IĶū>Jĸ ?é?N'•>ĶÖ>x4?­?€'K>•ō?ĻŖß>!Žē>¯ôŠ>—ø÷>iõą>ŠXË>ģô,?îÛ>ŠÖų>šÉ?ífš>ā?„?҇/?Z°-?Úáė>éęá>x2?Bɞ>ô$å>9"?‡!?ŧ§:?Zŗ>īŊ?Q3?¤ ú>RąÁ>ūŋ>l ŗ>ėž?\@¨>W!Ü>Ų';?¸xw>Ĩ1æ>0ëÎ>×à ?%.?Cü>›b?–-Š>ãŖ ?ŸĶ?â?Š'ņ>NL1?æMĀ>‰č?A;?¤ ?æâ>á[°>Üyā>’?¨ē>Ü>@´.?^Ŗ‚>Š`ˇ>ŽĪ>Á ?~€2?˙ä?ũ-?™Ģ‡>ÕO?&™#?™× ?¸é>1"?Ir>cv?áÔ6?’?îŌ>‘o­>`ęĩ>Ētî>ŨčŽ>ÎĄĨ>a$?yT>ī˛ ?öæÂ> ~ ?â8?ēĶ>yy ?šÂc>›ú ?N?Ž$?ōfô>1Ø/?ũÅ>āí?Rļíģ>H%æ>`ŧ?Ú/­>ŗŅ> v'?;…>qüŅ>x Õ>ps?ŧĄ)?0ií>æ†?Æöq>;ú>’/(?Œ[õ>ĀĄØ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_attn_mask_expanded/000077500000000000000000000000001511334557700306205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_attn_mask_expanded/model.onnx000066400000000000000000000326221511334557700326310ustar00rootroot00000000000000  backend-test:ųj i QAAttention_test_attention_3d_attn_mask_expanded_function_BatchSize"Shape* start * end : y Q?Attention_test_attention_3d_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : z K@Attention_test_attention_3d_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : hEAttention_test_attention_3d_attn_mask_expanded_function_QNumHeadsAttr"Constant* value*: : iFAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeadsAttr"Constant* value*: : j>Attention_test_attention_3d_attn_mask_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : î AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize ?Attention_test_attention_3d_attn_mask_expanded_function_QSeqLen EAttention_test_attention_3d_attn_mask_expanded_function_QNumHeadsAttr >Attention_test_attention_3d_attn_mask_expanded_function_NegOneJAttention_test_attention_3d_attn_mask_expanded_function_QIntermediateShape"Concat* axis : ņ AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize @Attention_test_attention_3d_attn_mask_expanded_function_KVSeqLen FAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeadsAttr >Attention_test_attention_3d_attn_mask_expanded_function_NegOneKAttention_test_attention_3d_attn_mask_expanded_function_KVIntermediateShape"Concat* axis : Ą Q JAttention_test_attention_3d_attn_mask_expanded_function_QIntermediateShapeEAttention_test_attention_3d_attn_mask_expanded_function_QIntermediate"Reshape: ĸ K KAttention_test_attention_3d_attn_mask_expanded_function_KVIntermediateShapeEAttention_test_attention_3d_attn_mask_expanded_function_KIntermediate"Reshape: ĸ V KAttention_test_attention_3d_attn_mask_expanded_function_KVIntermediateShapeEAttention_test_attention_3d_attn_mask_expanded_function_VIntermediate"Reshape: Ē EAttention_test_attention_3d_attn_mask_expanded_function_QIntermediateAAttention_test_attention_3d_attn_mask_expanded_function_QReshaped" Transpose* perm@@@@ : Ē EAttention_test_attention_3d_attn_mask_expanded_function_KIntermediateAAttention_test_attention_3d_attn_mask_expanded_function_KReshaped" Transpose* perm@@@@ : Ē EAttention_test_attention_3d_attn_mask_expanded_function_VIntermediateAAttention_test_attention_3d_attn_mask_expanded_function_VReshaped" Transpose* perm@@@@ : Š AAttention_test_attention_3d_attn_mask_expanded_function_QReshapedAAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads"Shape* start * end : Ē AAttention_test_attention_3d_attn_mask_expanded_function_KReshapedBAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeads"Shape* start * end : Ē AAttention_test_attention_3d_attn_mask_expanded_function_QReshapedBAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSize"Shape* start * end : œ BAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSizeCAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSizeF"Cast* to : Š AAttention_test_attention_3d_attn_mask_expanded_function_VReshapedAAttention_test_attention_3d_attn_mask_expanded_function_VHeadSize"Shape* start * end : “ CAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSizeFDAttention_test_attention_3d_attn_mask_expanded_function_SqrtHeadSize"Sqrt: `=Attention_test_attention_3d_attn_mask_expanded_function_One1D"Constant* value*: : d>Attention_test_attention_3d_attn_mask_expanded_function_One1DF"Constant* value* "€? : a>Attention_test_attention_3d_attn_mask_expanded_function_Zero1D"Constant* value*: : Ö >Attention_test_attention_3d_attn_mask_expanded_function_One1DF DAttention_test_attention_3d_attn_mask_expanded_function_SqrtHeadSizeGAttention_test_attention_3d_attn_mask_expanded_function_CalculatedScale"Div: b>Attention_test_attention_3d_attn_mask_expanded_function_ScaleF"Constant* value*"€? : š GAttention_test_attention_3d_attn_mask_expanded_function_CalculatedScaleCAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactor"Identity: – CAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorGAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: ĸ GAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorSqrtDAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorF"Cast* to : “ AAttention_test_attention_3d_attn_mask_expanded_function_KReshapedBAttention_test_attention_3d_attn_mask_expanded_function_PresentKey"Identity: gDAttention_test_attention_3d_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: : • AAttention_test_attention_3d_attn_mask_expanded_function_VReshapedDAttention_test_attention_3d_attn_mask_expanded_function_PresentValue"Identity: ž BAttention_test_attention_3d_attn_mask_expanded_function_PresentKeyCAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ä ?Attention_test_attention_3d_attn_mask_expanded_function_QSeqLen CAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLenEAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasShape"Concat* axis : iCAttention_test_attention_3d_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : hBAttention_test_attention_3d_attn_mask_expanded_function_ScalarZero"Constant* value* " : ^ attn_maskEAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasShort"Identity: • EAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasShort@Attention_test_attention_3d_attn_mask_expanded_function_AttnBias"Identity: › @Attention_test_attention_3d_attn_mask_expanded_function_AttnBiasKAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Ŗ KAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasCausalOrNotAAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasT"Cast* to : Ķ AAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeadsAAttention_test_attention_3d_attn_mask_expanded_function_NGQACond1"Equal: Œ AAttention_test_attention_3d_attn_mask_expanded_function_NGQACond1@Attention_test_attention_3d_attn_mask_expanded_function_GQACond1"Not: Ķ AAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeadsCAttention_test_attention_3d_attn_mask_expanded_function_DivNumHeads"Div: ž CAttention_test_attention_3d_attn_mask_expanded_function_DivNumHeadsDAttention_test_attention_3d_attn_mask_expanded_function_IDivNumHeads"Cast* to : Ų AAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeadsIAttention_test_attention_3d_attn_mask_expanded_function_RemainderNumHeads"Mod: Ö IAttention_test_attention_3d_attn_mask_expanded_function_RemainderNumHeads >Attention_test_attention_3d_attn_mask_expanded_function_Zero1D@Attention_test_attention_3d_attn_mask_expanded_function_GQACond2"Equal: Ė @Attention_test_attention_3d_attn_mask_expanded_function_GQACond1 @Attention_test_attention_3d_attn_mask_expanded_function_GQACond2?Attention_test_attention_3d_attn_mask_expanded_function_GQACond"And: – ?Attention_test_attention_3d_attn_mask_expanded_function_GQACond DAttention_test_attention_3d_attn_mask_expanded_function_IDivNumHeads =Attention_test_attention_3d_attn_mask_expanded_function_One1DEAttention_test_attention_3d_attn_mask_expanded_function_InterleaveDim"Where: `=Attention_test_attention_3d_attn_mask_expanded_function_Two1D"Constant* value*: : Õ BAttention_test_attention_3d_attn_mask_expanded_function_PresentKey =Attention_test_attention_3d_attn_mask_expanded_function_Two1DCAttention_test_attention_3d_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: × DAttention_test_attention_3d_attn_mask_expanded_function_PresentValue =Attention_test_attention_3d_attn_mask_expanded_function_Two1DCAttention_test_attention_3d_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: ´ AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize BAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeads EAttention_test_attention_3d_attn_mask_expanded_function_InterleaveDim CAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLen BAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSizeDAttention_test_attention_3d_attn_mask_expanded_function_KExpandShape"Concat* axis : Ø CAttention_test_attention_3d_attn_mask_expanded_function_KUnsqueezed DAttention_test_attention_3d_attn_mask_expanded_function_KExpandShapeAAttention_test_attention_3d_attn_mask_expanded_function_KExpanded"Expand: ŗ AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize BAttention_test_attention_3d_attn_mask_expanded_function_KVNumHeads EAttention_test_attention_3d_attn_mask_expanded_function_InterleaveDim CAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLen AAttention_test_attention_3d_attn_mask_expanded_function_VHeadSizeDAttention_test_attention_3d_attn_mask_expanded_function_VExpandShape"Concat* axis : Ø CAttention_test_attention_3d_attn_mask_expanded_function_VUnsqueezed DAttention_test_attention_3d_attn_mask_expanded_function_VExpandShapeAAttention_test_attention_3d_attn_mask_expanded_function_VExpanded"Expand: ī AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize AAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads CAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLen BAttention_test_attention_3d_attn_mask_expanded_function_QKHeadSizeGAttention_test_attention_3d_attn_mask_expanded_function_KAttentionShape"Concat* axis : î AAttention_test_attention_3d_attn_mask_expanded_function_BatchSize AAttention_test_attention_3d_attn_mask_expanded_function_QNumHeads CAttention_test_attention_3d_attn_mask_expanded_function_NewKVSeqLen AAttention_test_attention_3d_attn_mask_expanded_function_VHeadSizeGAttention_test_attention_3d_attn_mask_expanded_function_VAttentionShape"Concat* axis : ā AAttention_test_attention_3d_attn_mask_expanded_function_KExpanded GAttention_test_attention_3d_attn_mask_expanded_function_KAttentionShapeGAttention_test_attention_3d_attn_mask_expanded_function_KAttentionInput"Reshape: ā AAttention_test_attention_3d_attn_mask_expanded_function_VExpanded GAttention_test_attention_3d_attn_mask_expanded_function_VAttentionShapeGAttention_test_attention_3d_attn_mask_expanded_function_VAttentionInput"Reshape: ­ GAttention_test_attention_3d_attn_mask_expanded_function_KAttentionInputBAttention_test_attention_3d_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : Ņ AAttention_test_attention_3d_attn_mask_expanded_function_QReshaped DAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorF?Attention_test_attention_3d_attn_mask_expanded_function_QScaled"Mul: Ō BAttention_test_attention_3d_attn_mask_expanded_function_KTranspose DAttention_test_attention_3d_attn_mask_expanded_function_ScaleFactorF?Attention_test_attention_3d_attn_mask_expanded_function_KScaled"Mul: Ō ?Attention_test_attention_3d_attn_mask_expanded_function_QScaled ?Attention_test_attention_3d_attn_mask_expanded_function_KScaledDAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeight"MatMul:  DAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeightBAttention_test_attention_3d_attn_mask_expanded_function_QKAttnCast"Cast* to : Ü BAttention_test_attention_3d_attn_mask_expanded_function_QKAttnCast AAttention_test_attention_3d_attn_mask_expanded_function_AttnBiasTLAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeightWithBias"Add: § LAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeightWithBiasKAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: Ĩ KAttention_test_attention_3d_attn_mask_expanded_function_QKAttnWeightSoftcapCAttention_test_attention_3d_attn_mask_expanded_function_SoftmaxCast"Cast* to : › CAttention_test_attention_3d_attn_mask_expanded_function_SoftmaxCastIAttention_test_attention_3d_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: ĸ IAttention_test_attention_3d_attn_mask_expanded_function_AttnWeightSoftmaxBAttention_test_attention_3d_attn_mask_expanded_function_SoftmaxOut"Cast* to : Ü BAttention_test_attention_3d_attn_mask_expanded_function_SoftmaxOut GAttention_test_attention_3d_attn_mask_expanded_function_VAttentionInputCAttention_test_attention_3d_attn_mask_expanded_function_YPreReshape"MatMul: Š CAttention_test_attention_3d_attn_mask_expanded_function_YPreReshapeBAttention_test_attention_3d_attn_mask_expanded_function_YTranspose" Transpose* perm@@@@ : š >Attention_test_attention_3d_attn_mask_expanded_function_Zero1D >Attention_test_attention_3d_attn_mask_expanded_function_Zero1D >Attention_test_attention_3d_attn_mask_expanded_function_NegOneAAttention_test_attention_3d_attn_mask_expanded_function_YNewShape"Concat* axis : • BAttention_test_attention_3d_attn_mask_expanded_function_YTranspose AAttention_test_attention_3d_attn_mask_expanded_function_YNewShapeY"Reshape:$test_attention_3d_attn_mask_expandedZ Q    Z K    Z V    Z attn_mask   b Y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_attn_mask_expanded/test_data_set_0/000077500000000000000000000000001511334557700336625ustar00rootroot00000000000000input_0.pb000066400000000000000000000014161511334557700355060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_attn_mask_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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Ē>M{?Z-_?O˛?˙l?ī— ?É]l?'tT?žáw?ãvk?2˜=o÷2>Š<Į> ŋs?d™>ĄQ$>Ũäb?Īä>‰jh?u$>˙>)?@já>Ĩœ=iK2?V}>ãC"='ˆu=}-z=0ah?i=?jįe?[.,?›h?eā›>tz?Øpš>æøđ>ZŠÁ>GÂz?ĻŲ2> î§>U+.?˙r=˛t?Žô>rh‘>œ"t>ˇ?aŧ>úŧé>ÖÉŦ>Frx?Y¤>'AÆ= ҝ>ŠM?Ęŋ(?=eË>¯Đ?R+´>Ž8?Ÿ8#?L$P?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>TŨ>ŒĄ?ÎčE? pu?¸Eđ=ũ$Û=<ö?hŌ>?b Y?ą’o?ŌÁ{?ΞĖ>KģÂ>([>ŨW/?!(?"°\?3/Į=Üū>ÉÁ?ģZw>˙->} \?QÂo=9õđ>`:í=›ę>ĐŪz? đØ>Šl[?$Cđ=áŠ>ėŊÎ>-´Ė>Ęß+?å~°>mš6?ÁĄ#?Ú^Ė>ĒŨ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī> Ī>ŋ_¤>bZõ<˛ŧę0?_îē>ų#C>Ÿ§œj6d>÷í¤=~ˇŽ=Āĩb>,ÔĖ=Iŗ‡>_y‡=Ø[†=ę4[?Û&>YG?/F?„Žé>ģ >îbL>°Ũ>Y:?×é˛> H?ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?V`B?˜Ĩē>­E?ļĀ>šÕē>F•…>Ōīũ>‚†.?‹˙>Á=?eđ=|Ž#>¸?=ہx?ķũ|;ĢŨ6>Öä?ĨĻ=øÃa?98?Uew?gô?ƒÎ™>Ŧ ?#Jn?ŸP?\Έ>5`?"lž>†Qĩ:$Ą}>īĸ>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_causal/test_data_set_0/output_0.pb000066400000000000000000000014161511334557700334050ustar00rootroot00000000000000BYJ€-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?|a>6•&?… ? kU>ˆwM>ē?°‚f> Ų?ÃéW?Ą°?@ëí>„'(?%Ō?Ęž>˛?Š‚?ü>ÆC´>Ē3!>ëT?āĨ<>úęž>œü?ÆvR?Jj0>§!?üDŧ>柄>kÃŦ>á!á>Ff>ķTá>hAA?åž$?‹g?ėA?ąOĩ>ų=ņ>Ą ?Uč?Āl&?z‹u>ŽŽ­>ëQ*?ÆÍ–>Æ8¸>ϧ?'ų?ęšr>))?ņ÷Ü>¸üd> w >æuÜ>ocŦ>5Ņ?éy3?‡ãû>.?‘›M?ÚáŊ>ɟ?57?×Ŋ2?÷ß1??ŅØ>Äö>s˙?ī_…>­ŧ>A-3?2?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>TŨ>ŒĄ?ÎčE? pu?¸Eđ=ũ$Û=<ö?hŌ>?b Y?ą’o?ŌÁ{?ΞĖ>KģÂ>([>ŨW/?!(?;Ni?\ûā>Ļę?Ũ´!?Aq>ŨĨ#>õø? >äaį>Ĩŋ >žx?d8x?tjŒ>#€û>– ˛>ŠĻ?Oŗ?Íq?JM?Õ:ŧ>ĩ5?•–Ū>ŖŸ?û~?ŸVK?bŠ>uæ.?‰Â"?°Qå>ŗˇ›>/‚Į>֊n>Ė’ß>˜¸ >~Õ>sxd?,¯b>°?m#í>Ÿ0 ?oœ6?ž¤â>„R?[Հ>z?9iÚ>CŌ?Ĩdđ>0r-?^S…>Ž '?~>/?ņŲ>M֍>Šē˜>KP>ŋ÷Č>2ŗ‡>̞Ī>Äs9?^PI>Ąm?NčÂ>uį ?võ7? –æ>´Ģ)?>z>W ?—†č>ZSô>Û ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_causal_expanded/000077500000000000000000000000001511334557700301075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_causal_expanded/model.onnx000066400000000000000000000366621511334557700321300ustar00rootroot00000000000000  backend-test:™{ f Q>Attention_test_attention_3d_causal_expanded_function_BatchSize"Shape* start * end : v QAttention_test_attention_3d_causal_expanded_function_BatchSize Attention_test_attention_3d_causal_expanded_function_BatchSize =Attention_test_attention_3d_causal_expanded_function_KVSeqLen CAttention_test_attention_3d_causal_expanded_function_KVNumHeadsAttr ;Attention_test_attention_3d_causal_expanded_function_NegOneHAttention_test_attention_3d_causal_expanded_function_KVIntermediateShape"Concat* axis : › Q GAttention_test_attention_3d_causal_expanded_function_QIntermediateShapeBAttention_test_attention_3d_causal_expanded_function_QIntermediate"Reshape: œ K HAttention_test_attention_3d_causal_expanded_function_KVIntermediateShapeBAttention_test_attention_3d_causal_expanded_function_KIntermediate"Reshape: œ V HAttention_test_attention_3d_causal_expanded_function_KVIntermediateShapeBAttention_test_attention_3d_causal_expanded_function_VIntermediate"Reshape: ¤ BAttention_test_attention_3d_causal_expanded_function_QIntermediate>Attention_test_attention_3d_causal_expanded_function_QReshaped" Transpose* perm@@@@ : ¤ BAttention_test_attention_3d_causal_expanded_function_KIntermediate>Attention_test_attention_3d_causal_expanded_function_KReshaped" Transpose* perm@@@@ : ¤ BAttention_test_attention_3d_causal_expanded_function_VIntermediate>Attention_test_attention_3d_causal_expanded_function_VReshaped" Transpose* perm@@@@ : Ŗ >Attention_test_attention_3d_causal_expanded_function_QReshaped>Attention_test_attention_3d_causal_expanded_function_QNumHeads"Shape* start * end : ¤ >Attention_test_attention_3d_causal_expanded_function_KReshaped?Attention_test_attention_3d_causal_expanded_function_KVNumHeads"Shape* start * end : ¤ >Attention_test_attention_3d_causal_expanded_function_QReshaped?Attention_test_attention_3d_causal_expanded_function_QKHeadSize"Shape* start * end : – ?Attention_test_attention_3d_causal_expanded_function_QKHeadSize@Attention_test_attention_3d_causal_expanded_function_QKHeadSizeF"Cast* to : Ŗ >Attention_test_attention_3d_causal_expanded_function_VReshaped>Attention_test_attention_3d_causal_expanded_function_VHeadSize"Shape* start * end :  @Attention_test_attention_3d_causal_expanded_function_QKHeadSizeFAAttention_test_attention_3d_causal_expanded_function_SqrtHeadSize"Sqrt: ]:Attention_test_attention_3d_causal_expanded_function_One1D"Constant* value*: : a;Attention_test_attention_3d_causal_expanded_function_One1DF"Constant* value* "€? : ^;Attention_test_attention_3d_causal_expanded_function_Zero1D"Constant* value*: : Í ;Attention_test_attention_3d_causal_expanded_function_One1DF AAttention_test_attention_3d_causal_expanded_function_SqrtHeadSizeDAttention_test_attention_3d_causal_expanded_function_CalculatedScale"Div: _;Attention_test_attention_3d_causal_expanded_function_ScaleF"Constant* value*"€? : ” DAttention_test_attention_3d_causal_expanded_function_CalculatedScale@Attention_test_attention_3d_causal_expanded_function_ScaleFactor"Identity:  @Attention_test_attention_3d_causal_expanded_function_ScaleFactorDAttention_test_attention_3d_causal_expanded_function_ScaleFactorSqrt"Sqrt: œ DAttention_test_attention_3d_causal_expanded_function_ScaleFactorSqrtAAttention_test_attention_3d_causal_expanded_function_ScaleFactorF"Cast* to :  >Attention_test_attention_3d_causal_expanded_function_KReshaped?Attention_test_attention_3d_causal_expanded_function_PresentKey"Identity: dAAttention_test_attention_3d_causal_expanded_function_PastKVSeqLen"Constant* value*: :  >Attention_test_attention_3d_causal_expanded_function_VReshapedAAttention_test_attention_3d_causal_expanded_function_PresentValue"Identity: ¸ ?Attention_test_attention_3d_causal_expanded_function_PresentKey@Attention_test_attention_3d_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Û Attention_test_attention_3d_causal_expanded_function_ZeroNoDim"Squeeze: ŋ 8Attention_test_attention_3d_causal_expanded_function_One 9Attention_test_attention_3d_causal_expanded_function_Zero=Attention_test_attention_3d_causal_expanded_function_OneNoDim"Squeeze: Ķ BAttention_test_attention_3d_causal_expanded_function_AttnBiasShape >Attention_test_attention_3d_causal_expanded_function_ZeroNoDimCAttention_test_attention_3d_causal_expanded_function_SequenceLength"Gather: × BAttention_test_attention_3d_causal_expanded_function_AttnBiasShape =Attention_test_attention_3d_causal_expanded_function_OneNoDimHAttention_test_attention_3d_causal_expanded_function_TotalSequenceLength"Gather: Œ >Attention_test_attention_3d_causal_expanded_function_ZeroNoDim CAttention_test_attention_3d_causal_expanded_function_SequenceLength =Attention_test_attention_3d_causal_expanded_function_OneNoDim=Attention_test_attention_3d_causal_expanded_function_RangeRow"Range: Į =Attention_test_attention_3d_causal_expanded_function_RangeRow 8Attention_test_attention_3d_causal_expanded_function_One?Attention_test_attention_3d_causal_expanded_function_RangeRow2D" Unsqueeze: ‘ >Attention_test_attention_3d_causal_expanded_function_ZeroNoDim HAttention_test_attention_3d_causal_expanded_function_TotalSequenceLength =Attention_test_attention_3d_causal_expanded_function_OneNoDim=Attention_test_attention_3d_causal_expanded_function_RangeCol"Range: Č =Attention_test_attention_3d_causal_expanded_function_RangeCol 9Attention_test_attention_3d_causal_expanded_function_Zero?Attention_test_attention_3d_causal_expanded_function_RangeCol2D" Unsqueeze: Đ ?Attention_test_attention_3d_causal_expanded_function_RangeRow2D AAttention_test_attention_3d_causal_expanded_function_PastKVSeqLenCAttention_test_attention_3d_causal_expanded_function_RangeRow2DPast"Add: Đ CAttention_test_attention_3d_causal_expanded_function_RangeRow2DPast ?Attention_test_attention_3d_causal_expanded_function_RangeCol2D@Attention_test_attention_3d_causal_expanded_function_BoolMaskTri"Less: Œ @Attention_test_attention_3d_causal_expanded_function_BoolMaskTri @Attention_test_attention_3d_causal_expanded_function_FloatNegInf ?Attention_test_attention_3d_causal_expanded_function_ScalarZeroAttention_test_attention_3d_causal_expanded_function_AttnBiasT"Cast* to : Ę >Attention_test_attention_3d_causal_expanded_function_QNumHeads ?Attention_test_attention_3d_causal_expanded_function_KVNumHeads>Attention_test_attention_3d_causal_expanded_function_NGQACond1"Equal: † >Attention_test_attention_3d_causal_expanded_function_NGQACond1=Attention_test_attention_3d_causal_expanded_function_GQACond1"Not: Ę >Attention_test_attention_3d_causal_expanded_function_QNumHeads ?Attention_test_attention_3d_causal_expanded_function_KVNumHeads@Attention_test_attention_3d_causal_expanded_function_DivNumHeads"Div: ˜ @Attention_test_attention_3d_causal_expanded_function_DivNumHeadsAAttention_test_attention_3d_causal_expanded_function_IDivNumHeads"Cast* to : Đ >Attention_test_attention_3d_causal_expanded_function_QNumHeads ?Attention_test_attention_3d_causal_expanded_function_KVNumHeadsFAttention_test_attention_3d_causal_expanded_function_RemainderNumHeads"Mod: Í FAttention_test_attention_3d_causal_expanded_function_RemainderNumHeads ;Attention_test_attention_3d_causal_expanded_function_Zero1D=Attention_test_attention_3d_causal_expanded_function_GQACond2"Equal: à =Attention_test_attention_3d_causal_expanded_function_GQACond1 =Attention_test_attention_3d_causal_expanded_function_GQACond2Attention_test_attention_3d_causal_expanded_function_BatchSize ?Attention_test_attention_3d_causal_expanded_function_KVNumHeads BAttention_test_attention_3d_causal_expanded_function_InterleaveDim @Attention_test_attention_3d_causal_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_causal_expanded_function_QKHeadSizeAAttention_test_attention_3d_causal_expanded_function_KExpandShape"Concat* axis : Ī @Attention_test_attention_3d_causal_expanded_function_KUnsqueezed AAttention_test_attention_3d_causal_expanded_function_KExpandShape>Attention_test_attention_3d_causal_expanded_function_KExpanded"Expand: Ą >Attention_test_attention_3d_causal_expanded_function_BatchSize ?Attention_test_attention_3d_causal_expanded_function_KVNumHeads BAttention_test_attention_3d_causal_expanded_function_InterleaveDim @Attention_test_attention_3d_causal_expanded_function_NewKVSeqLen >Attention_test_attention_3d_causal_expanded_function_VHeadSizeAAttention_test_attention_3d_causal_expanded_function_VExpandShape"Concat* axis : Ī @Attention_test_attention_3d_causal_expanded_function_VUnsqueezed AAttention_test_attention_3d_causal_expanded_function_VExpandShape>Attention_test_attention_3d_causal_expanded_function_VExpanded"Expand: ā >Attention_test_attention_3d_causal_expanded_function_BatchSize >Attention_test_attention_3d_causal_expanded_function_QNumHeads @Attention_test_attention_3d_causal_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_causal_expanded_function_QKHeadSizeDAttention_test_attention_3d_causal_expanded_function_KAttentionShape"Concat* axis : ß >Attention_test_attention_3d_causal_expanded_function_BatchSize >Attention_test_attention_3d_causal_expanded_function_QNumHeads @Attention_test_attention_3d_causal_expanded_function_NewKVSeqLen >Attention_test_attention_3d_causal_expanded_function_VHeadSizeDAttention_test_attention_3d_causal_expanded_function_VAttentionShape"Concat* axis : × >Attention_test_attention_3d_causal_expanded_function_KExpanded DAttention_test_attention_3d_causal_expanded_function_KAttentionShapeDAttention_test_attention_3d_causal_expanded_function_KAttentionInput"Reshape: × >Attention_test_attention_3d_causal_expanded_function_VExpanded DAttention_test_attention_3d_causal_expanded_function_VAttentionShapeDAttention_test_attention_3d_causal_expanded_function_VAttentionInput"Reshape: § DAttention_test_attention_3d_causal_expanded_function_KAttentionInput?Attention_test_attention_3d_causal_expanded_function_KTranspose" Transpose* perm@@@@ : Č >Attention_test_attention_3d_causal_expanded_function_QReshaped AAttention_test_attention_3d_causal_expanded_function_ScaleFactorFAttention_test_attention_3d_causal_expanded_function_AttnBiasTIAttention_test_attention_3d_causal_expanded_function_QKAttnWeightWithBias"Add: Ą IAttention_test_attention_3d_causal_expanded_function_QKAttnWeightWithBiasHAttention_test_attention_3d_causal_expanded_function_QKAttnWeightSoftcap"Identity: Ÿ HAttention_test_attention_3d_causal_expanded_function_QKAttnWeightSoftcap@Attention_test_attention_3d_causal_expanded_function_SoftmaxCast"Cast* to : • @Attention_test_attention_3d_causal_expanded_function_SoftmaxCastFAttention_test_attention_3d_causal_expanded_function_AttnWeightSoftmax"Softmax: œ FAttention_test_attention_3d_causal_expanded_function_AttnWeightSoftmax?Attention_test_attention_3d_causal_expanded_function_SoftmaxOut"Cast* to : Ķ ?Attention_test_attention_3d_causal_expanded_function_SoftmaxOut DAttention_test_attention_3d_causal_expanded_function_VAttentionInput@Attention_test_attention_3d_causal_expanded_function_YPreReshape"MatMul: Ŗ @Attention_test_attention_3d_causal_expanded_function_YPreReshape?Attention_test_attention_3d_causal_expanded_function_YTranspose" Transpose* perm@@@@ : Ž ;Attention_test_attention_3d_causal_expanded_function_Zero1D ;Attention_test_attention_3d_causal_expanded_function_Zero1D ;Attention_test_attention_3d_causal_expanded_function_NegOne>Attention_test_attention_3d_causal_expanded_function_YNewShape"Concat* axis :  ?Attention_test_attention_3d_causal_expanded_function_YTranspose >Attention_test_attention_3d_causal_expanded_function_YNewShapeY"Reshape:!test_attention_3d_causal_expandedZ Q    Z K    Z V    b Y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_causal_expanded/test_data_set_0/000077500000000000000000000000001511334557700331515ustar00rootroot00000000000000input_0.pb000066400000000000000000000014161511334557700347750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_causal_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?input_3.pb000066400000000000000000000001631511334557700372100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask/test_data_set_0B attn_maskJ`iÖ>)1>O‘Û=#=Q?Ė?ō>XŨa?Ö¸;?ĸĮŅ>Ö<ŋ>ā?o™c?Jž°÷>¯>;Ρ>įæo?Ŋ]l?ūΐ>ä­>output_0.pb000066400000000000000000000017161511334557700374130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask/test_data_set_0BYJĀŨD >Âhƒ>δÔ>‡ā>Ž+5?W@ ? ę>K ?;ņ?O\1?:sņ>dŨ?Ŋ#? †ž>œâĶ>Ōģ€>’ZČ>ĪŪņ>0 ĩ>;Ž.?…¨ī>Û?5Æ?^_?ãs ?w<"?'ŧ?ņjĘ>!Ûå>74G?͜u>ē•>žęį>ŧ%Ú>:Ÿ&?k ?6c>Š4?ĩû?3%?cŠø>jũ>€(?ũĪĻ>šúŅ>Zn>Ú0Đ>Dīų>U|Ŋ>2Ų.?yUŲ>_K?ēœ? H?0Ä ?Ļž.?G?=eâ>aĢæ>Ø1?Ūtk>/eŽ>LĐũ>ފ÷>,ä=?įAí>Īߨ>{ķ?„|?>ú#?ŧ?Ūmâ>!  ?ēÅ>$Ģá>Ŧ†l>¸lŊ>Ēšû>ëSĀ>Āc,?Ągō>Ę?÷ '?Ž:?ģčũ>Į1/?|í ?ß3 ?h?ã9R?ûՕ>ŗ7Ĩ>ˇˆæ>‡ré>ë•B?}æŌ>_o­>oî?ŊĖ?Ur!?üƒũ>Cđâ>)?ÉTŽ>¤Ü÷> Üg>6ŪĻ>ˆŽÜ>:¯ļ>c‡Đ?¯'?…Å?ĸåū>Ė#5?ÜĒ ?-?r?ŒR?†jā>™ ?tãí>Š+?ņ†>ã_ā>pa¸>•Ō—>s÷?ërë>ö(?ëļŠ>Ëâ>á>Â_?ĖēD?\ė˙>Ģ&ė>ā–ņ>ßûķ>Ô[Ō>P“Ŋ>lĘä>Jåč>†k%? 3Õ>´õ;?P.Ŋ>y;?ן+?láč>æh?Ãâ>ßR*?¤{Ž>*TŨ>•ĸÆ>û¤>œ?ã|č>ãŪ?ԋ>JĻį>Qw >B†#? ĸú#Ü>y€ž>ÃEī>V#¯>ŅŪ>‹ūŅ>Ŋwé>ŋ?"įÚ>‰%>?Ä7Ô>É*?f…"?$Œ ?R¤?ļƒĶ>\q-?‚NĨ>$?0ãË>ųÃ>†?­ø>čõ ?‘š>Œ>Â>)9>ŧ?ˆM-?ŗ*ō>Č"Ö>ĀWË>‘¯ü>ŋĻĨ>ęéū>-@Ë>ۜČ>÷ ?€§>ē6?t÷é>Ҥ'?ŋđ)?K ?E?ž‹â>,H+?>ĝ> ?´Ų>)¸Ŋ>…?Nã>ķf ?W°œ>RŒŌ>Cy4>æÖ?˙6?å ú>{ÆŌ>aČ>q)û>n-§>€?/`ļ>ū˙ŋ>Æ˙??@°>OX7?sÅī>=3?R)?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask_expanded/000077500000000000000000000000001511334557700341715ustar00rootroot00000000000000model.onnx000066400000000000000000000404011511334557700361150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask_expanded  backend-test:ၠz QRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize"Shape* start * end : Š QPAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‹ KQAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : yVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeadsAttr"Constant* value*: : zWAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsAttr"Constant* value*: : {OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : à RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QSeqLen VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeadsAttr OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NegOne[Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QIntermediateShape"Concat* axis : Æ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize QAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVSeqLen WAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsAttr OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NegOne\Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVIntermediateShape"Concat* axis : à Q [Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QIntermediateShapeVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QIntermediate"Reshape: Ä K \Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVIntermediateShapeVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KIntermediate"Reshape: Ä V \Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVIntermediateShapeVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VIntermediate"Reshape: Ė VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QIntermediateRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QReshaped" Transpose* perm@@@@ : Ė VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KIntermediateRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KReshaped" Transpose* perm@@@@ : Ė VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VIntermediateRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VReshaped" Transpose* perm@@@@ : Ë RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QReshapedRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads"Shape* start * end : Ė RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KReshapedSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads"Shape* start * end : Ė RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QReshapedSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSize"Shape* start * end : ž SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeF"Cast* to : Ë RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VReshapedRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VHeadSize"Shape* start * end : ĩ TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeFUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SqrtHeadSize"Sqrt: qNAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_One1D"Constant* value*: : uOAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_One1DF"Constant* value* "€? : rOAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Zero1D"Constant* value*: : ‰ OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_One1DF UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SqrtHeadSizeXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_CalculatedScale"Div: sOAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleF"Constant* value*"€? : ŧ XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_CalculatedScaleTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactor"Identity: ¸ TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: Ä XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorSqrtUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorF"Cast* to : ĩ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KReshapedSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PresentKey"Identity: xUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: : ˇ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VReshapedUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PresentValue"Identity: ā SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PresentKeyTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : — PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QSeqLen TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLenVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShape"Concat* axis : zTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : ySAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScalarZero"Constant* value* " : o attn_maskVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShort"Identity: ˇ VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShortQAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBias"Identity: Ŋ QAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBias\Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Å \Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasCausalOrNotRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasT"Cast* to : † RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NGQACond1"Equal: Ž RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NGQACond1QAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond1"Not: † RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_DivNumHeads"Div: Ā TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_DivNumHeadsUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_IDivNumHeads"Cast* to : Œ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsZAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_RemainderNumHeads"Mod: ‰ ZAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_RemainderNumHeads OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Zero1DQAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond2"Equal: ˙ QAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond1 QAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond2PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond"And: Ú PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_GQACond UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_IDivNumHeads NAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_One1DVAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim"Where: qNAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Two1D"Constant* value*: : ˆ SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PresentKey NAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Two1DTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: Š UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_PresentValue NAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Two1DTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: š RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KExpandShape"Concat* axis : ‹ TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KUnsqueezed UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KExpandShapeRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KExpanded"Expand: ™ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads VAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VHeadSizeUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VExpandShape"Concat* axis : ‹ TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VUnsqueezed UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VExpandShapeRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VExpanded"Expand: Ä RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KAttentionShape"Concat* axis : à RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_BatchSize RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VHeadSizeXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VAttentionShape"Concat* axis : “ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KExpanded XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KAttentionShapeXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KAttentionInput"Reshape: “ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VExpanded XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VAttentionShapeXAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VAttentionInput"Reshape: Ī XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KAttentionInputSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : „ RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QReshaped UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorFPAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QScaled"Mul: … SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KTranspose UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorFPAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KScaled"Mul: … PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QScaled PAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_KScaledUAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeight"MatMul: ŋ UAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnCast"Cast* to :  SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnCast RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasT]Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightWithBias"Add: É ]Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightWithBias\Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: Į \Attention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSoftcapTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxCast"Cast* to : Ŋ TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxCastZAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: Ä ZAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_AttnWeightSoftmaxSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxOut"Cast* to :  SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxOut XAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_VAttentionInputTAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YPreReshape"MatMul: Ë TAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YPreReshapeSAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YTranspose" Transpose* perm@@@@ : Ū OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Zero1D OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_Zero1D OAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_NegOneRAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YNewShape"Concat* axis : ˇ SAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YTranspose RAttention_test_attention_3d_diff_heads_sizes_attn_mask_expanded_function_YNewShapeY"Reshape:5test_attention_3d_diff_heads_sizes_attn_mask_expandedZ Q    Z K    Z V    Z attn_mask   b Y    B test_data_set_0/000077500000000000000000000000001511334557700371545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask_expandedinput_0.pb000066400000000000000000000014161511334557700410570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_attn_mask_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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ą>…(><?zÔđ>§JÕ>†đŧ>l˙Æ=äúõ>[Ē@>Ķ"?3ʞ>˙ēß>Šû˜>䈉>Ä(?ˆS?€{9?[? ?ŗo?ĩ Ã>-ˆ?†'Ī>=Î?Bö>¸>Â?+ ?A<?ÃĨš>sĩš>5ô1>¯+?]?Ú ?ĸlˇ>}ĩ>uí?˙•›>5É ?-ՙ>l{Á>ūā>ķx>L?Ū?ú.:?ĶČ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_causal_expanded/000077500000000000000000000000001511334557700334605ustar00rootroot00000000000000model.onnx000066400000000000000000000456471511334557700354250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_causal_expanded  backend-test:— w QOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize"Shape* start * end : ‡ QMAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˆ KNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : vSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeadsAttr"Constant* value*: : wTAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeadsAttr"Constant* value*: : xLAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ´ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QSeqLen SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeadsAttr LAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NegOneXAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QIntermediateShape"Concat* axis : ˇ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVSeqLen TAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeadsAttr LAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NegOneYAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVIntermediateShape"Concat* axis : Ŋ Q XAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QIntermediate"Reshape: ž K YAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KIntermediate"Reshape: ž V YAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VIntermediate"Reshape: Æ SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QIntermediateOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QReshaped" Transpose* perm@@@@ : Æ SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KIntermediateOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KReshaped" Transpose* perm@@@@ : Æ SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VIntermediateOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VReshaped" Transpose* perm@@@@ : Å OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QReshapedOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads"Shape* start * end : Æ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KReshapedPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeads"Shape* start * end : Æ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QReshapedPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSize"Shape* start * end : ¸ PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSizeQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSizeF"Cast* to : Å OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VReshapedOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VHeadSize"Shape* start * end : ¯ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSizeFRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SqrtHeadSize"Sqrt: nKAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One1D"Constant* value*: : rLAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One1DF"Constant* value* "€? : oLAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Zero1D"Constant* value*: : € LAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One1DF RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SqrtHeadSizeUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_CalculatedScale"Div: pLAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleF"Constant* value*"€? : ļ UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_CalculatedScaleQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactor"Identity: ˛ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorSqrt"Sqrt: ž UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorSqrtRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorF"Cast* to : ¯ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KReshapedPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PresentKey"Identity: uRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PastKVSeqLen"Constant* value*: : ą OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VReshapedRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PresentValue"Identity: Ú PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PresentKeyQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Ž MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QSeqLen QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLenSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasShape"Concat* axis : wQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : vPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScalarZero"Constant* value* " : ¸ SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasShapeNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBias"ConstantOfShape: mJAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Zero"Constant* value*: : lIAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One"Constant* value*: : ô JAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Zero JAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroNoDim"Squeeze: ō IAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One JAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_OneNoDim"Squeeze: † SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasShape OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroNoDimTAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SequenceLength"Gather: Š SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasShape NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_OneNoDimYAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_TotalSequenceLength"Gather: Đ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroNoDim TAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SequenceLength NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_OneNoDimNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow"Range: ú NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow IAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_OnePAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow2D" Unsqueeze: Õ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroNoDim YAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_TotalSequenceLength NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_OneNoDimNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeCol"Range: û NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeCol JAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ZeroPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeCol2D" Unsqueeze: ƒ PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow2D RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PastKVSeqLenTAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow2DPast"Add: ƒ TAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeRow2DPast PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RangeCol2DQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BoolMaskTri"Less: Đ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BoolMaskTri QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_FloatNegInf PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScalarZeroMAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_MaskTri"Where:  NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBias MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_MaskTriYAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasCausalOrNot"Add: ŋ YAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasCausalOrNotOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasT"Cast* to : ũ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeadsOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NGQACond1"Equal: ¨ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NGQACond1NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond1"Not: ũ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeadsQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_DivNumHeads"Div: ē QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_DivNumHeadsRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_IDivNumHeads"Cast* to : ƒ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeadsWAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RemainderNumHeads"Mod: € WAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_RemainderNumHeads LAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Zero1DNAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond2"Equal: ö NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond1 NAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond2MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond"And: Î MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_GQACond RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_IDivNumHeads KAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_One1DSAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_InterleaveDim"Where: nKAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Two1D"Constant* value*: : ˙ PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PresentKey KAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Two1DQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KUnsqueezed" Unsqueeze:  RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_PresentValue KAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_Two1DQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VUnsqueezed" Unsqueeze: ˆ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeads SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_InterleaveDim QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSizeRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KExpandShape"Concat* axis : ‚ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KUnsqueezed RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KExpandShapeOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KExpanded"Expand: ‡ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KVNumHeads SAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_InterleaveDim QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VHeadSizeRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VExpandShape"Concat* axis : ‚ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VUnsqueezed RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VExpandShapeOAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VExpanded"Expand: ĩ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKHeadSizeUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KAttentionShape"Concat* axis : ´ OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_BatchSize OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VHeadSizeUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VAttentionShape"Concat* axis : Š OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KExpanded UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KAttentionShapeUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KAttentionInput"Reshape: Š OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VExpanded UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VAttentionShapeUAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VAttentionInput"Reshape: É UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KAttentionInputPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KTranspose" Transpose* perm@@@@ : û OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QReshaped RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorFMAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QScaled"Mul: ü PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KTranspose RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_ScaleFactorFMAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KScaled"Mul: ü MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QScaled MAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_KScaledRAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeight"MatMul: š RAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeightPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnCast"Cast* to : † PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnCast OAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnBiasTZAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeightWithBias"Add: à ZAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeightWithBiasYAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeightSoftcap"Identity: Á YAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_QKAttnWeightSoftcapQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SoftmaxCast"Cast* to : ˇ QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SoftmaxCastWAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnWeightSoftmax"Softmax: ž WAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_AttnWeightSoftmaxPAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SoftmaxOut"Cast* to : † PAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_SoftmaxOut UAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_VAttentionInputQAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_YPreReshape"MatMul: Å QAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_YPreReshapePAttention_test_attention_3d_diff_heads_sizes_causal_expanded_function_YTranspose" Transpose* perm@@@@ : Ō 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ą>…(><?zÔđ>§JÕ>†đŧ>l˙Æ=äúõ>[Ē@>Ķ"?3ʞ>˙ēß>Šû˜>䈉>Ä(?ˆS?€{9?[? ?ŗo?ĩ Ã>-ˆ?†'Ī>=Î?Bö>¸>Â?+ ?A<?ÃĨš>sĩš>5ô1>¯+?]?Ú ?ĸlˇ>}ĩ>uí?˙•›>5É ?-ՙ>l{Á>ūā>ķx>L?Ū?ú.:?ĶČ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_expanded/000077500000000000000000000000001511334557700321305ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_expanded/model.onnx000066400000000000000000000347131511334557700341440ustar00rootroot00000000000000  backend-test:˛s p QHAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize"Shape* start * end : € QFAttention_test_attention_3d_diff_heads_sizes_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KGAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : oLAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeadsAttr"Constant* value*: : pMAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeadsAttr"Constant* value*: : qEAttention_test_attention_3d_diff_heads_sizes_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ‘ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize FAttention_test_attention_3d_diff_heads_sizes_expanded_function_QSeqLen LAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeadsAttr EAttention_test_attention_3d_diff_heads_sizes_expanded_function_NegOneQAttention_test_attention_3d_diff_heads_sizes_expanded_function_QIntermediateShape"Concat* axis : ” HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize GAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVSeqLen MAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeadsAttr EAttention_test_attention_3d_diff_heads_sizes_expanded_function_NegOneRAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVIntermediateShape"Concat* axis : ¯ Q QAttention_test_attention_3d_diff_heads_sizes_expanded_function_QIntermediateShapeLAttention_test_attention_3d_diff_heads_sizes_expanded_function_QIntermediate"Reshape: ° K RAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVIntermediateShapeLAttention_test_attention_3d_diff_heads_sizes_expanded_function_KIntermediate"Reshape: ° V RAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVIntermediateShapeLAttention_test_attention_3d_diff_heads_sizes_expanded_function_VIntermediate"Reshape: ¸ LAttention_test_attention_3d_diff_heads_sizes_expanded_function_QIntermediateHAttention_test_attention_3d_diff_heads_sizes_expanded_function_QReshaped" Transpose* perm@@@@ : ¸ LAttention_test_attention_3d_diff_heads_sizes_expanded_function_KIntermediateHAttention_test_attention_3d_diff_heads_sizes_expanded_function_KReshaped" Transpose* perm@@@@ : ¸ LAttention_test_attention_3d_diff_heads_sizes_expanded_function_VIntermediateHAttention_test_attention_3d_diff_heads_sizes_expanded_function_VReshaped" Transpose* perm@@@@ : ˇ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QReshapedHAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads"Shape* start * end : ¸ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_KReshapedIAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeads"Shape* start * end : ¸ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QReshapedIAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSize"Shape* start * end : Ē IAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSizeJAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSizeF"Cast* to : ˇ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_VReshapedHAttention_test_attention_3d_diff_heads_sizes_expanded_function_VHeadSize"Shape* start * end : Ą JAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSizeFKAttention_test_attention_3d_diff_heads_sizes_expanded_function_SqrtHeadSize"Sqrt: gDAttention_test_attention_3d_diff_heads_sizes_expanded_function_One1D"Constant* value*: : kEAttention_test_attention_3d_diff_heads_sizes_expanded_function_One1DF"Constant* value* "€? : hEAttention_test_attention_3d_diff_heads_sizes_expanded_function_Zero1D"Constant* value*: : ë EAttention_test_attention_3d_diff_heads_sizes_expanded_function_One1DF KAttention_test_attention_3d_diff_heads_sizes_expanded_function_SqrtHeadSizeNAttention_test_attention_3d_diff_heads_sizes_expanded_function_CalculatedScale"Div: iEAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleF"Constant* value*"€? : ¨ NAttention_test_attention_3d_diff_heads_sizes_expanded_function_CalculatedScaleJAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactor"Identity: ¤ JAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorNAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorSqrt"Sqrt: ° NAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorSqrtKAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorF"Cast* to : Ą HAttention_test_attention_3d_diff_heads_sizes_expanded_function_KReshapedIAttention_test_attention_3d_diff_heads_sizes_expanded_function_PresentKey"Identity: nKAttention_test_attention_3d_diff_heads_sizes_expanded_function_PastKVSeqLen"Constant* value*: : Ŗ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_VReshapedKAttention_test_attention_3d_diff_heads_sizes_expanded_function_PresentValue"Identity: Ė IAttention_test_attention_3d_diff_heads_sizes_expanded_function_PresentKeyJAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ų FAttention_test_attention_3d_diff_heads_sizes_expanded_function_QSeqLen JAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLenLAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasShape"Concat* axis : pJAttention_test_attention_3d_diff_heads_sizes_expanded_function_FloatNegInf"Constant* value* "€˙ : oIAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScalarZero"Constant* value* " : Ē LAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasShapeGAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBias"ConstantOfShape: Š GAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasRAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasCausalOrNot"Identity: ą RAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasCausalOrNotHAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasT"Cast* to : č HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeadsHAttention_test_attention_3d_diff_heads_sizes_expanded_function_NGQACond1"Equal: š HAttention_test_attention_3d_diff_heads_sizes_expanded_function_NGQACond1GAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond1"Not: č HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeadsJAttention_test_attention_3d_diff_heads_sizes_expanded_function_DivNumHeads"Div: Ŧ JAttention_test_attention_3d_diff_heads_sizes_expanded_function_DivNumHeadsKAttention_test_attention_3d_diff_heads_sizes_expanded_function_IDivNumHeads"Cast* to : î HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeadsPAttention_test_attention_3d_diff_heads_sizes_expanded_function_RemainderNumHeads"Mod: ë PAttention_test_attention_3d_diff_heads_sizes_expanded_function_RemainderNumHeads EAttention_test_attention_3d_diff_heads_sizes_expanded_function_Zero1DGAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond2"Equal: á GAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond1 GAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond2FAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond"And: ˛ FAttention_test_attention_3d_diff_heads_sizes_expanded_function_GQACond KAttention_test_attention_3d_diff_heads_sizes_expanded_function_IDivNumHeads DAttention_test_attention_3d_diff_heads_sizes_expanded_function_One1DLAttention_test_attention_3d_diff_heads_sizes_expanded_function_InterleaveDim"Where: gDAttention_test_attention_3d_diff_heads_sizes_expanded_function_Two1D"Constant* value*: : ę IAttention_test_attention_3d_diff_heads_sizes_expanded_function_PresentKey DAttention_test_attention_3d_diff_heads_sizes_expanded_function_Two1DJAttention_test_attention_3d_diff_heads_sizes_expanded_function_KUnsqueezed" Unsqueeze: ė KAttention_test_attention_3d_diff_heads_sizes_expanded_function_PresentValue DAttention_test_attention_3d_diff_heads_sizes_expanded_function_Two1DJAttention_test_attention_3d_diff_heads_sizes_expanded_function_VUnsqueezed" Unsqueeze: Ū HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeads LAttention_test_attention_3d_diff_heads_sizes_expanded_function_InterleaveDim JAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLen IAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSizeKAttention_test_attention_3d_diff_heads_sizes_expanded_function_KExpandShape"Concat* axis : í JAttention_test_attention_3d_diff_heads_sizes_expanded_function_KUnsqueezed KAttention_test_attention_3d_diff_heads_sizes_expanded_function_KExpandShapeHAttention_test_attention_3d_diff_heads_sizes_expanded_function_KExpanded"Expand: Ũ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KVNumHeads LAttention_test_attention_3d_diff_heads_sizes_expanded_function_InterleaveDim JAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLen HAttention_test_attention_3d_diff_heads_sizes_expanded_function_VHeadSizeKAttention_test_attention_3d_diff_heads_sizes_expanded_function_VExpandShape"Concat* axis : í JAttention_test_attention_3d_diff_heads_sizes_expanded_function_VUnsqueezed KAttention_test_attention_3d_diff_heads_sizes_expanded_function_VExpandShapeHAttention_test_attention_3d_diff_heads_sizes_expanded_function_VExpanded"Expand: ’ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads JAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLen IAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKHeadSizeNAttention_test_attention_3d_diff_heads_sizes_expanded_function_KAttentionShape"Concat* axis : ‘ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_BatchSize HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QNumHeads JAttention_test_attention_3d_diff_heads_sizes_expanded_function_NewKVSeqLen HAttention_test_attention_3d_diff_heads_sizes_expanded_function_VHeadSizeNAttention_test_attention_3d_diff_heads_sizes_expanded_function_VAttentionShape"Concat* axis : õ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_KExpanded NAttention_test_attention_3d_diff_heads_sizes_expanded_function_KAttentionShapeNAttention_test_attention_3d_diff_heads_sizes_expanded_function_KAttentionInput"Reshape: õ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_VExpanded NAttention_test_attention_3d_diff_heads_sizes_expanded_function_VAttentionShapeNAttention_test_attention_3d_diff_heads_sizes_expanded_function_VAttentionInput"Reshape: ģ NAttention_test_attention_3d_diff_heads_sizes_expanded_function_KAttentionInputIAttention_test_attention_3d_diff_heads_sizes_expanded_function_KTranspose" Transpose* perm@@@@ : æ HAttention_test_attention_3d_diff_heads_sizes_expanded_function_QReshaped KAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorFFAttention_test_attention_3d_diff_heads_sizes_expanded_function_QScaled"Mul: į IAttention_test_attention_3d_diff_heads_sizes_expanded_function_KTranspose KAttention_test_attention_3d_diff_heads_sizes_expanded_function_ScaleFactorFFAttention_test_attention_3d_diff_heads_sizes_expanded_function_KScaled"Mul: į FAttention_test_attention_3d_diff_heads_sizes_expanded_function_QScaled FAttention_test_attention_3d_diff_heads_sizes_expanded_function_KScaledKAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeight"MatMul: Ģ KAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeightIAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnCast"Cast* to : ņ IAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnCast HAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnBiasTSAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeightWithBias"Add: ĩ SAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeightWithBiasRAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeightSoftcap"Identity: ŗ RAttention_test_attention_3d_diff_heads_sizes_expanded_function_QKAttnWeightSoftcapJAttention_test_attention_3d_diff_heads_sizes_expanded_function_SoftmaxCast"Cast* to : Š JAttention_test_attention_3d_diff_heads_sizes_expanded_function_SoftmaxCastPAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnWeightSoftmax"Softmax: ° PAttention_test_attention_3d_diff_heads_sizes_expanded_function_AttnWeightSoftmaxIAttention_test_attention_3d_diff_heads_sizes_expanded_function_SoftmaxOut"Cast* to : ņ IAttention_test_attention_3d_diff_heads_sizes_expanded_function_SoftmaxOut NAttention_test_attention_3d_diff_heads_sizes_expanded_function_VAttentionInputJAttention_test_attention_3d_diff_heads_sizes_expanded_function_YPreReshape"MatMul: ˇ JAttention_test_attention_3d_diff_heads_sizes_expanded_function_YPreReshapeIAttention_test_attention_3d_diff_heads_sizes_expanded_function_YTranspose" Transpose* perm@@@@ : ļ EAttention_test_attention_3d_diff_heads_sizes_expanded_function_Zero1D 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5?ˆô>`ßŨ>ÉŨÄ>˜åķ>"Ĩ>ŲEķ>š>ÆíŲ>Ž?!.Ę>ķ:9?KĢė>/Å4?ņũ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_scaled_expanded/000077500000000000000000000000001511334557700334435ustar00rootroot00000000000000model.onnx000066400000000000000000000371571511334557700354050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_scaled_expanded  backend-test:Ö| w QOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_BatchSize"Shape* start * end : ‡ QMAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˆ KNAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : vSAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeadsAttr"Constant* value*: : wTAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVNumHeadsAttr"Constant* value*: : 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XAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QIntermediate"Reshape: ž K YAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KIntermediate"Reshape: ž V YAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVIntermediateShapeSAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VIntermediate"Reshape: Æ SAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QIntermediateOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QReshaped" Transpose* perm@@@@ : Æ SAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KIntermediateOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KReshaped" Transpose* perm@@@@ : Æ SAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VIntermediateOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VReshaped" Transpose* perm@@@@ : Å OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QReshapedOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeads"Shape* start * end : Æ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KReshapedPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVNumHeads"Shape* start * end : Æ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QReshapedPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKHeadSize"Shape* start * end : ¸ PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKHeadSizeQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKHeadSizeF"Cast* to : Å 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value*" ×#< : ­ LAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactor"Identity: ˛ QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorUAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorSqrt"Sqrt: ž UAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorSqrtRAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorF"Cast* to : ¯ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KReshapedPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_PresentKey"Identity: uRAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_PastKVSeqLen"Constant* value*: : ą OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VReshapedRAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_PresentValue"Identity: Ú PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_PresentKeyQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Ž MAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QSeqLen QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLenSAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasShape"Concat* axis : wQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_FloatNegInf"Constant* value* "€˙ : vPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScalarZero"Constant* value* " : ¸ SAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasShapeNAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBias"ConstantOfShape: ˇ NAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasYAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasCausalOrNot"Identity: ŋ YAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasCausalOrNotOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasT"Cast* to : ũ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeads PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVNumHeadsOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NGQACond1"Equal: ¨ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NGQACond1NAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_GQACond1"Not: ũ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeads 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OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KVNumHeads SAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_InterleaveDim QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VHeadSizeRAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VExpandShape"Concat* axis : ‚ QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VUnsqueezed RAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VExpandShapeOAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VExpanded"Expand: ĩ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_BatchSize OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKHeadSizeUAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KAttentionShape"Concat* axis : ´ OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_BatchSize OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VHeadSizeUAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VAttentionShape"Concat* axis : Š OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KExpanded UAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KAttentionShapeUAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KAttentionInput"Reshape: Š OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VExpanded UAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VAttentionShapeUAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VAttentionInput"Reshape: É UAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KAttentionInputPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : û OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QReshaped RAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorFMAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QScaled"Mul: ü PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KTranspose RAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_ScaleFactorFMAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KScaled"Mul: ü MAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QScaled MAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_KScaledRAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeight"MatMul: š RAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnCast"Cast* to : † PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnCast OAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnBiasTZAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightWithBias"Add: à ZAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightWithBiasYAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightSoftcap"Identity: Á YAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightSoftcapQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_SoftmaxCast"Cast* to : ˇ QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_SoftmaxCastWAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnWeightSoftmax"Softmax: ž WAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_AttnWeightSoftmaxPAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_SoftmaxOut"Cast* to : † PAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_SoftmaxOut UAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_VAttentionInputQAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_YPreReshape"MatMul: Å QAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_YPreReshapePAttention_test_attention_3d_diff_heads_sizes_scaled_expanded_function_YTranspose" Transpose* perm@@@@ : Ō 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Å>8Z:?=ā>L|*?˜æ#?ŅV÷>†I?Ûhã>”Ē$?Yĩ™>Ȩđ>dË>Méĸ>q ?ųŗđ>fx?&ķ‘>„BØ>Aq>yá?q\1?@î>ŌhĐ>Ą‚´>Rû>ļ6¤>ŗQđ> IÎ>ŸkÜ>;%?"1É> q:?ũâā>ߡ+?ič$?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_softcap_expanded/000077500000000000000000000000001511334557700336475ustar00rootroot00000000000000model.onnx000066400000000000000000000411211511334557700355730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_sizes_softcap_expanded  backend-test:ˇ„ x QPAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize"Shape* start * end : ˆ QNAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‰ KOAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : wTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeadsAttr"Constant* value*: : xUAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsAttr"Constant* value*: : yMAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : š PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QSeqLen TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeadsAttr MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NegOneYAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QIntermediateShape"Concat* axis : ŧ PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVSeqLen UAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsAttr MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NegOneZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVIntermediateShape"Concat* axis : ŋ Q YAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QIntermediateShapeTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QIntermediate"Reshape: Ā K ZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVIntermediateShapeTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KIntermediate"Reshape: Ā V ZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVIntermediateShapeTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VIntermediate"Reshape: Č TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QIntermediatePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QReshaped" Transpose* perm@@@@ : Č TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KIntermediatePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KReshaped" Transpose* perm@@@@ : Č TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VIntermediatePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VReshaped" Transpose* perm@@@@ : Į PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QReshapedPAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads"Shape* start * end : Č PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KReshapedQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeads"Shape* start * end : Č PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QReshapedQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSize"Shape* start * end : ē QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeF"Cast* to : Į PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VReshapedPAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VHeadSize"Shape* start * end : ą RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeFSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SqrtHeadSize"Sqrt: oLAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_One1D"Constant* value*: : sMAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_One1DF"Constant* value* "€? : pMAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Zero1D"Constant* value*: : ƒ MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_One1DF SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SqrtHeadSizeVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_CalculatedScale"Div: qMAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleF"Constant* value*"€? : ¸ VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_CalculatedScaleRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactor"Identity: ´ RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorSqrt"Sqrt: Ā VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorSqrtSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorF"Cast* to : ą PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KReshapedQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PresentKey"Identity: vSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PastKVSeqLen"Constant* value*: : ŗ PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VReshapedSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PresentValue"Identity: Ü QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PresentKeyRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‘ NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QSeqLen RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLenTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasShape"Concat* axis : xRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : wQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScalarZero"Constant* value* " : ē TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasShapeOAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBias"ConstantOfShape: š OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasCausalOrNot"Identity: Á ZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasCausalOrNotPAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasT"Cast* to : € PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsPAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NGQACond1"Equal: Ē PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NGQACond1OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond1"Not: € PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_DivNumHeads"Div: ŧ RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_DivNumHeadsSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_IDivNumHeads"Cast* to : † PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsXAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_RemainderNumHeads"Mod: ƒ XAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_RemainderNumHeads MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Zero1DOAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond2"Equal: ų OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond1 OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond2NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond"And: Ō NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_GQACond SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_IDivNumHeads LAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_One1DTAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_InterleaveDim"Where: oLAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Two1D"Constant* value*: : ‚ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PresentKey LAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Two1DRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KUnsqueezed" Unsqueeze: „ SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_PresentValue LAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Two1DRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VUnsqueezed" Unsqueeze: Ž PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeads TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_InterleaveDim RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KExpandShape"Concat* axis : … RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KUnsqueezed SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KExpandShapePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KExpanded"Expand:  PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KVNumHeads TAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_InterleaveDim RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VHeadSizeSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VExpandShape"Concat* axis : … RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VUnsqueezed SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VExpandShapePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VExpanded"Expand: ē PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KAttentionShape"Concat* axis : š PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_BatchSize PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QNumHeads RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VHeadSizeVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VAttentionShape"Concat* axis :  PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KExpanded VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KAttentionShapeVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KAttentionInput"Reshape:  PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VExpanded VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VAttentionShapeVAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VAttentionInput"Reshape: Ë VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KAttentionInputQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : ū PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QReshaped SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorFNAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QScaled"Mul: ˙ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KTranspose SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_ScaleFactorFNAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KScaled"Mul: ˙ NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QScaled NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_KScaledSAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeight"MatMul: ģ SAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeightQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnCast"Cast* to : ‰ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnCast PAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnBiasT[Attention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeightWithBias"Add: tNAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Softcap"Constant* value* "@@ : ´ NAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapOAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapF"Cast* to : ˆ [Attention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeightWithBias OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapFQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapDiv"Div: ¯ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapDivRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapTanh"Tanh: ˆ RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapTanh OAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftcapFZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeightSoftcap"Mul: à ZAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_QKAttnWeightSoftcapRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftmaxCast"Cast* to : š RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftmaxCastXAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnWeightSoftmax"Softmax: Ā XAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_AttnWeightSoftmaxQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftmaxOut"Cast* to : ‰ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_SoftmaxOut VAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_VAttentionInputRAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_YPreReshape"MatMul: Į RAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_YPreReshapeQAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_YTranspose" Transpose* perm@@@@ : Ö MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Zero1D MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_Zero1D MAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_NegOnePAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_YNewShape"Concat* axis : ŗ QAttention_test_attention_3d_diff_heads_sizes_softcap_expanded_function_YTranspose 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Ņ/?=“>ŨD?KRĒ=Ҋy?WßI=÷ön?v>ÃB?š8Â+‚>Å??-G?lë=׉É>HĀ>s?Œ+?§@W?•ū>ˇČ>–n>áN?qŸ6?*>Ņ>ûˇ?lI*?ĖÂ(>éÍŪ<؏ĸ>Cx?o$ų>CK1?1×Q?"ú>K} >ÂÂY?‘2?‹l=?įd4?ēÜw?…2—>Ú^Ė>ĒŨ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī>)ŋ>j6d>÷í¤=~ˇŽ=Āĩb>,ÔĖ=Iŗ‡>_y‡=Ø[†=ę4[?­E?ļĀ>šÕē>F•…>Ōīũ>‚†.?‹˙>Á=?eđ=|Ž#>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>pUw?Ÿ¸J?ĩv?ly|>Ö¯?Iąđ=†Ķy?Uŧn?œ™Č>Ēũw>24€>\÷>ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">üŽ4?î9ģ>FsĘ>!l>"°>“Ãr?Ų˕>öä{>ˆH?J„>_ō>“ŒU?îk>LwÚ>I?VŽ ?v‡y?ŋ,.?m=?mŠw?91Ô>Xôĩ>Š3= <>×ár>˜č;>„9A?Ą/ ? ę*?Ī R?Pl>ßĻ>W5?ŗÉ>œÉī–„h?DkŅ>ˆĒ>„Q}?kø$?dģ>žīĐ=€°I?fD5?Ŧl?}^>u]ë=Ņ\9?GP>‘T4>ÉŊŖ>tQ? ?•Í;=šƒí>J/?„Ō ?Œ?,f>rY?Ôˇ?J—6?t[{?Û<Û>•a?’•î;Ö=–?LvŸ>6<~>‹MŽ>ļŖ>Qœ:?Ôļ?CūI?ÇT?”ĘW?=LÔ>&ą×>Ãm?Wi)?ûˤ=ÄĖ ?•Fļ>ŠČ|?íē_<ãˇ?]?9?—ë“>’@y?˜ \?9hj?Œ<$ã?fܖ>ņYY?o"?Øķ ?4­ę= L ?xÄ!?Ģļt?įÅ?‘§w?Ō+v?‡s&?0ƒ?gšî>Ųīc?zįčQ#?„‘?>‹Å>Ō?Í'%? Ī>ŋ_¤>bZõ<˛ŧYG?/F?„Žé>ģ >îbL>°Ũ>Y:?×é˛> H?¸?=ہx?ķũ|;ĢŨ6>Öä?ĨĻ=øÃa?98?Uew?gô?÷ų,?OÄz>ųĻ]>q*>Į9l?7‘–>øûį> čü>A6G?ČX?~Ī#=ˇÃ#? Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?test_attention_3d_diff_heads_with_past_and_present_expanded/000077500000000000000000000000001511334557700353005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000433421511334557700373120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_with_past_and_present_expanded  backend-test:ȍ € QXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize"Shape* start * end :  QVAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‘ KWAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeadsAttr"Constant* value*: : €]Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsAttr"Constant* value*: : UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : á XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QSeqLen \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeadsAttr UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NegOneaAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QIntermediateShape"Concat* axis : ä XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize WAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVSeqLen ]Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsAttr UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NegOnebAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVIntermediateShape"Concat* axis : Ī Q aAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QIntermediateShape\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QIntermediate"Reshape: Đ K bAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVIntermediateShape\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KIntermediate"Reshape: Đ V bAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVIntermediateShape\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VIntermediate"Reshape: Ø \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QIntermediateXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QReshaped" Transpose* perm@@@@ : Ø \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KIntermediateXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KReshaped" Transpose* perm@@@@ : Ø \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VIntermediateXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VReshaped" Transpose* perm@@@@ : × XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QReshapedXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads"Shape* start * end : Ø XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KReshapedYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end : Ø XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QReshapedYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : Ę YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : × XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VReshapedXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : Á ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeF[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: wTAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_One1D"Constant* value*: : {UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : xUAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Zero1D"Constant* value*: : › UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_One1DF [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SqrtHeadSize^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_CalculatedScale"Div: yUAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : Č ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_CalculatedScaleZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactor"Identity: Ä ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactor^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt: Đ ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactorSqrt[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Ö past_key XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KReshapedYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentKey"Concat* axis : œ past_key[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : t YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ú past_value XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VReshaped[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentValue"Concat* axis : x [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentValue present_value"Identity: ė YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentKeyZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Š VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QSeqLen ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : €ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : u attn_mask\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasShort"Identity: à \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasShortWAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBias"Identity: É WAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasbAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: Ņ bAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasCausalOrNotXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ˜ XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NGQACond1"Equal: ē XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NGQACond1WAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond1"Not: ˜ XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_DivNumHeads"Div: Ė ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_DivNumHeads[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ž XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeads`Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_RemainderNumHeads"Mod: › `Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_RemainderNumHeads UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Zero1DWAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond2"Equal: ‘ WAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond1 WAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond2VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond"And: ō VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_GQACond [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_IDivNumHeads TAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_One1D\Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_InterleaveDim"Where: wTAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Two1D"Constant* value*: : š YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentKey TAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Two1DZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: œ [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_PresentValue TAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Two1DZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ž XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeads \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_InterleaveDim ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSize[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KExpandShape"Concat* axis :  ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KUnsqueezed [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KExpandShapeXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KExpanded"Expand: Ŋ XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KVNumHeads \Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_InterleaveDim ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VHeadSize[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VExpandShape"Concat* axis :  ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VUnsqueezed [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VExpandShapeXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VExpanded"Expand: â XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKHeadSize^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : á XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_BatchSize XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QNumHeads ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VHeadSize^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VAttentionShape"Concat* axis : Ĩ XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KExpanded ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KAttentionShape^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KAttentionInput"Reshape: Ĩ XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VExpanded ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VAttentionShape^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VAttentionInput"Reshape: Û ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KAttentionInputYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ : – XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QReshaped [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactorFVAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QScaled"Mul: — YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KTranspose [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_ScaleFactorFVAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KScaled"Mul: — VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QScaled VAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_KScaled[Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeight"MatMul: Ë [Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnCast"Cast* to : Ą YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnCast XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnBiasTcAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: Õ cAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightWithBiasbAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Ķ bAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightSoftcapZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : É ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SoftmaxCast`Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax: Đ `Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_AttnWeightSoftmaxYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : Ą YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_SoftmaxOut ^Attention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_VAttentionInputZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YPreReshape"MatMul: × ZAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YPreReshapeYAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YTranspose" Transpose* perm@@@@ : ö UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Zero1D UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_Zero1D UAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_NegOneXAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YNewShape"Concat* axis : à YAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YTranspose XAttention_test_attention_3d_diff_heads_with_past_and_present_expanded_function_YNewShapeY"Reshape:;test_attention_3d_diff_heads_with_past_and_present_expandedZ Q    Z K    Z V    Z attn_mask   Z" past_key     Z$ past_value      b Y    b% present_key     b' present_value      B test_data_set_0/000077500000000000000000000000001511334557700403425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_with_past_and_present_expandedinput_0.pb000066400000000000000000000014161511334557700422450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_diff_heads_with_past_and_present_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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Sø>2|æ> u“>ĻP?+Ë&?ØÄ>á´¯>˛õ?!îô>Æä>Ęœ>VQ?šā'?))Æ>_¤Ģ>‡?Ŗųķ>ŸØŪ>ž›—>ëũP?b”–>Ug0?– ?^˜”>0Zâ>"¸:?+¤ø>Ô´ ?f”>…‹6?íœ?-l—>îá>eÅ4?Åô>&  ?1U—>Ąi4?3×?í•>Îã>Đ=?ŅÁũ>Āĸ ?öŧō>ƒ]L?Fvč>GCĩ>u'?1 Ë>kO>ŋŗ>ķ~ö>õ)M?~ũë>šāģ>čŠ?ėË>×G>0,´>7ę>âøL?ŽĐë>7Ãļ>é?זË>ĨEH>€āŽ>GZ?úŠš>Ŋ8ŧ>t?*?RuĖ>N˛ŋ>ËzÁ>„S?™?•ĩ>Í^ž>7”&?@žÍ>PÍš>BŪĘ>m˙R?ū¤?¸cĩ>IéŊ>×ĸ#? Ō>‹Aģ>“Ë>ŪĖR?„Ī­>-,?°˛?;ũx>ƒËí>ũƒ,? ļ?ē%ü>ר>§+?qų>;‡€>ŠĻę> "'?Ã?ocú>Ņû˛>]Ô0?“÷?Îl>ŧāī>)ŋ.?ĩâ?žčũ>iAė>tCG?§ĸŅ>‘…ļ>YĻ#?dŽÖ>¸žY>?H¸>\Ré>~ŗH?pŲ>VÉģ>TJ"?Ač×>ňI>Ë´>vā>°G?^ÄÔ> Fš>\!$?tĄ×>sQ>Ÿ˛>?ų?ÎĨÁ>ú8˛>ɋ#? Û>=˜ŧ>Ø>ē>ępP?î ?Į@ŋ>&ĩ>jŊ?ŧėã>LlÂ>OĀ>˙^P?Úã$?ŌÆ>(O°>*?öŠâ>œĀ>XЎ>بN?bħ>ņ-,?ŧŧ?‰>Nō>đ‰ 3?sK?×$Š>Ę>ņ>MĢ>?. ?üē ?ØÕŠ>W/?˛?SŠ>:ō>6É>?‹{?EĘ ?m¨?—yM?)žä>uģ>“ų?}ÍŲ>‘P2>ßwĮ>F“ú>ōM?Öá>s…ģ>¨@?ŲÔŲ>’•3>57Į>ØÚô>î¯L?ģbŲ> åĩ>Į?õŌØ>.ûA>V Č>ü¸?1­¯>Ō¯Į>y' ?éęã>ԘÂ>ËXį>ÎūQ?ėŲ?ūC¸> @Ŋ>O=?=pŨ>ŖĢŋ>ęÎĪ>âƒQ?â?Œ&´>>üÂ>ĀW?- Ü>CÂ>7Ų>OR?˜ŧ>5=?ĪË?85t>öŒū>ƒī9?ēø?}?]š>߯@?­Ģ?Ty>āķü>Œ8?׿?``?ģ>Ģ,A?“:?ŖYr>•‚ũ>­8?ŗ?,`?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_attn_mask_expanded/000077500000000000000000000000001511334557700314505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_attn_mask_expanded/model.onnx000066400000000000000000000341021511334557700334540ustar00rootroot00000000000000  backend-test:Šp m QEAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize"Shape* start * end : } QCAttention_test_attention_3d_gqa_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ~ KDAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : lIAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeadsAttr"Constant* value*:  : mJAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeadsAttr"Constant* value*: : nBAttention_test_attention_3d_gqa_attn_mask_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ‚ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize CAttention_test_attention_3d_gqa_attn_mask_expanded_function_QSeqLen IAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeadsAttr BAttention_test_attention_3d_gqa_attn_mask_expanded_function_NegOneNAttention_test_attention_3d_gqa_attn_mask_expanded_function_QIntermediateShape"Concat* axis : … EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize DAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVSeqLen JAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeadsAttr BAttention_test_attention_3d_gqa_attn_mask_expanded_function_NegOneOAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVIntermediateShape"Concat* axis : Š Q NAttention_test_attention_3d_gqa_attn_mask_expanded_function_QIntermediateShapeIAttention_test_attention_3d_gqa_attn_mask_expanded_function_QIntermediate"Reshape: Ē K OAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVIntermediateShapeIAttention_test_attention_3d_gqa_attn_mask_expanded_function_KIntermediate"Reshape: Ē V OAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVIntermediateShapeIAttention_test_attention_3d_gqa_attn_mask_expanded_function_VIntermediate"Reshape: ˛ IAttention_test_attention_3d_gqa_attn_mask_expanded_function_QIntermediateEAttention_test_attention_3d_gqa_attn_mask_expanded_function_QReshaped" Transpose* perm@@@@ : ˛ IAttention_test_attention_3d_gqa_attn_mask_expanded_function_KIntermediateEAttention_test_attention_3d_gqa_attn_mask_expanded_function_KReshaped" Transpose* perm@@@@ : ˛ IAttention_test_attention_3d_gqa_attn_mask_expanded_function_VIntermediateEAttention_test_attention_3d_gqa_attn_mask_expanded_function_VReshaped" Transpose* perm@@@@ : ą EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QReshapedEAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads"Shape* start * end : ˛ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_KReshapedFAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeads"Shape* start * end : ˛ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QReshapedFAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSize"Shape* start * end : ¤ FAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSizeGAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSizeF"Cast* to : ą EAttention_test_attention_3d_gqa_attn_mask_expanded_function_VReshapedEAttention_test_attention_3d_gqa_attn_mask_expanded_function_VHeadSize"Shape* start * end : › GAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSizeFHAttention_test_attention_3d_gqa_attn_mask_expanded_function_SqrtHeadSize"Sqrt: dAAttention_test_attention_3d_gqa_attn_mask_expanded_function_One1D"Constant* value*: : hBAttention_test_attention_3d_gqa_attn_mask_expanded_function_One1DF"Constant* value* "€? : eBAttention_test_attention_3d_gqa_attn_mask_expanded_function_Zero1D"Constant* value*: : â BAttention_test_attention_3d_gqa_attn_mask_expanded_function_One1DF HAttention_test_attention_3d_gqa_attn_mask_expanded_function_SqrtHeadSizeKAttention_test_attention_3d_gqa_attn_mask_expanded_function_CalculatedScale"Div: fBAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleF"Constant* value*"€? : ĸ KAttention_test_attention_3d_gqa_attn_mask_expanded_function_CalculatedScaleGAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactor"Identity: ž GAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorKAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: Ē KAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorSqrtHAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorF"Cast* to : › EAttention_test_attention_3d_gqa_attn_mask_expanded_function_KReshapedFAttention_test_attention_3d_gqa_attn_mask_expanded_function_PresentKey"Identity: kHAttention_test_attention_3d_gqa_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: :  EAttention_test_attention_3d_gqa_attn_mask_expanded_function_VReshapedHAttention_test_attention_3d_gqa_attn_mask_expanded_function_PresentValue"Identity: Æ FAttention_test_attention_3d_gqa_attn_mask_expanded_function_PresentKeyGAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : đ CAttention_test_attention_3d_gqa_attn_mask_expanded_function_QSeqLen GAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLenIAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasShape"Concat* axis : mGAttention_test_attention_3d_gqa_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : lFAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScalarZero"Constant* value* " : b attn_maskIAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasShort"Identity:  IAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasShortDAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBias"Identity: Ŗ DAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasOAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Ģ OAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasCausalOrNotEAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasT"Cast* to : ß EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeadsEAttention_test_attention_3d_gqa_attn_mask_expanded_function_NGQACond1"Equal: ” EAttention_test_attention_3d_gqa_attn_mask_expanded_function_NGQACond1DAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond1"Not: ß EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeadsGAttention_test_attention_3d_gqa_attn_mask_expanded_function_DivNumHeads"Div: Ļ GAttention_test_attention_3d_gqa_attn_mask_expanded_function_DivNumHeadsHAttention_test_attention_3d_gqa_attn_mask_expanded_function_IDivNumHeads"Cast* to : å EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeadsMAttention_test_attention_3d_gqa_attn_mask_expanded_function_RemainderNumHeads"Mod: â MAttention_test_attention_3d_gqa_attn_mask_expanded_function_RemainderNumHeads BAttention_test_attention_3d_gqa_attn_mask_expanded_function_Zero1DDAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond2"Equal: Ø DAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond1 DAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond2CAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond"And: Ļ CAttention_test_attention_3d_gqa_attn_mask_expanded_function_GQACond HAttention_test_attention_3d_gqa_attn_mask_expanded_function_IDivNumHeads AAttention_test_attention_3d_gqa_attn_mask_expanded_function_One1DIAttention_test_attention_3d_gqa_attn_mask_expanded_function_InterleaveDim"Where: dAAttention_test_attention_3d_gqa_attn_mask_expanded_function_Two1D"Constant* value*: : á FAttention_test_attention_3d_gqa_attn_mask_expanded_function_PresentKey AAttention_test_attention_3d_gqa_attn_mask_expanded_function_Two1DGAttention_test_attention_3d_gqa_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: ã HAttention_test_attention_3d_gqa_attn_mask_expanded_function_PresentValue AAttention_test_attention_3d_gqa_attn_mask_expanded_function_Two1DGAttention_test_attention_3d_gqa_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: Ė EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeads IAttention_test_attention_3d_gqa_attn_mask_expanded_function_InterleaveDim GAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLen FAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSizeHAttention_test_attention_3d_gqa_attn_mask_expanded_function_KExpandShape"Concat* axis : ä GAttention_test_attention_3d_gqa_attn_mask_expanded_function_KUnsqueezed HAttention_test_attention_3d_gqa_attn_mask_expanded_function_KExpandShapeEAttention_test_attention_3d_gqa_attn_mask_expanded_function_KExpanded"Expand: Ë EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KVNumHeads IAttention_test_attention_3d_gqa_attn_mask_expanded_function_InterleaveDim GAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLen EAttention_test_attention_3d_gqa_attn_mask_expanded_function_VHeadSizeHAttention_test_attention_3d_gqa_attn_mask_expanded_function_VExpandShape"Concat* axis : ä GAttention_test_attention_3d_gqa_attn_mask_expanded_function_VUnsqueezed HAttention_test_attention_3d_gqa_attn_mask_expanded_function_VExpandShapeEAttention_test_attention_3d_gqa_attn_mask_expanded_function_VExpanded"Expand: ƒ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads GAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLen FAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKHeadSizeKAttention_test_attention_3d_gqa_attn_mask_expanded_function_KAttentionShape"Concat* axis : ‚ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_BatchSize EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QNumHeads GAttention_test_attention_3d_gqa_attn_mask_expanded_function_NewKVSeqLen EAttention_test_attention_3d_gqa_attn_mask_expanded_function_VHeadSizeKAttention_test_attention_3d_gqa_attn_mask_expanded_function_VAttentionShape"Concat* axis : ė EAttention_test_attention_3d_gqa_attn_mask_expanded_function_KExpanded KAttention_test_attention_3d_gqa_attn_mask_expanded_function_KAttentionShapeKAttention_test_attention_3d_gqa_attn_mask_expanded_function_KAttentionInput"Reshape: ė EAttention_test_attention_3d_gqa_attn_mask_expanded_function_VExpanded KAttention_test_attention_3d_gqa_attn_mask_expanded_function_VAttentionShapeKAttention_test_attention_3d_gqa_attn_mask_expanded_function_VAttentionInput"Reshape: ĩ KAttention_test_attention_3d_gqa_attn_mask_expanded_function_KAttentionInputFAttention_test_attention_3d_gqa_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : Ũ EAttention_test_attention_3d_gqa_attn_mask_expanded_function_QReshaped HAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorFCAttention_test_attention_3d_gqa_attn_mask_expanded_function_QScaled"Mul: Ū FAttention_test_attention_3d_gqa_attn_mask_expanded_function_KTranspose HAttention_test_attention_3d_gqa_attn_mask_expanded_function_ScaleFactorFCAttention_test_attention_3d_gqa_attn_mask_expanded_function_KScaled"Mul: Ū CAttention_test_attention_3d_gqa_attn_mask_expanded_function_QScaled CAttention_test_attention_3d_gqa_attn_mask_expanded_function_KScaledHAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeight"MatMul: Ĩ HAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeightFAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnCast"Cast* to : č FAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnCast EAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnBiasTPAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeightWithBias"Add: ¯ PAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeightWithBiasOAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: ­ OAttention_test_attention_3d_gqa_attn_mask_expanded_function_QKAttnWeightSoftcapGAttention_test_attention_3d_gqa_attn_mask_expanded_function_SoftmaxCast"Cast* to : Ŗ GAttention_test_attention_3d_gqa_attn_mask_expanded_function_SoftmaxCastMAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: Ē MAttention_test_attention_3d_gqa_attn_mask_expanded_function_AttnWeightSoftmaxFAttention_test_attention_3d_gqa_attn_mask_expanded_function_SoftmaxOut"Cast* to : č FAttention_test_attention_3d_gqa_attn_mask_expanded_function_SoftmaxOut KAttention_test_attention_3d_gqa_attn_mask_expanded_function_VAttentionInputGAttention_test_attention_3d_gqa_attn_mask_expanded_function_YPreReshape"MatMul: ą GAttention_test_attention_3d_gqa_attn_mask_expanded_function_YPreReshapeFAttention_test_attention_3d_gqa_attn_mask_expanded_function_YTranspose" Transpose* perm@@@@ : Ē BAttention_test_attention_3d_gqa_attn_mask_expanded_function_Zero1D BAttention_test_attention_3d_gqa_attn_mask_expanded_function_Zero1D BAttention_test_attention_3d_gqa_attn_mask_expanded_function_NegOneEAttention_test_attention_3d_gqa_attn_mask_expanded_function_YNewShape"Concat* axis :  FAttention_test_attention_3d_gqa_attn_mask_expanded_function_YTranspose 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K?Rˆđ>ņ ?cĖš>=Ø>Hß7>4x4?$ ?š¤J?ųĮę>ũP?jvļ>Ō¸Ø>œô6>ü2?‘E?t]?mÂ?Äēæ>‚Ĩ?F4ķ>hŅ>GŽ?.įĐ>šö?é­?“˜é>€3? põ>ąĘÔ>Ųn?>įÎ>w”?tW?Ž×į>× ?ĀĨį>_ Í>Šq?úŲ>yE?Zä>îæô>'?“!?ņO?“Ė?ũNæ>fa?üãá>í)ü>Ã|?ųĀ!?e?-ˆ?D™ë>šM?Չį>t}õ>Jp ?œm ?*?1&?_Õ>^-?~™æ>Îë>N‹ž>īVŪ> Û{>‡P>?Ŋ?ō+?įôę>ĢÚā>¯åÄ>3Eā>°K‚>ÔjA?û?Ōđ'?U6Ú>>œķ> Dą>CEÛ>^€>a;?ßš?¯Ėe>$t?č?”Ü=9“>Õé>{ŸĢ¯Ėe>$t?č?”Ü=9“>Õé>{ŸĢ¯Ėe>$t?č?”Ü=9“>Õé>{ŸĢRšú>ą†y> ą?XÔ@?‡~q>Ų?Iž#?ŠĶr?Ršú>ą†y> ą?XÔ@?‡~q>Ų?Iž#?ŠĶr?Ršú>ą†y> ą?XÔ@?‡~q>Ų?Iž#?ŠĶr?=G?(-Y?Qû>Ė=>Āí~?Öu>Ûbņ>et‹==G?(-Y?Qû>Ė=>Āí~?Öu>Ûbņ>et‹==G?(-Y?Qû>Ė=>Āí~?Öu>Ûbņ>et‹=ö ?`ru?ü$?L¨^>#†‹>šeŊ>ߨ=Bí>ņV?č‹u?.3&?Bg>šīŠ>āē>Õ3•=gHī>Žq?IXu?6ž#?¸ŪU>ņŒ>lŨĀ>ô…=i0ë>!Ŗ>%>{?ē˜=?>Oå?ôP%?ZÖf?æņ™>‘>Ôû?ÕA=?ņ˛÷=‚a?;{%?”e?͇›>ÎŊ>›å?ÕP=?û=§Đ?îs%?ŊËe?LQ?βj?ąęš>cJ´=ņ°4?ķ÷M>MÃ+?:%>ķÂ?„l?Û'ŗ>ĖŸ=ˆũ,?˙—U>|1?"/>§5?œ"m?EÚ°>ô’˜=^*?î0X>sŨ2?ë2>öĘķ> ŋg?`ā*?’t§>%=Ō>ŧÅ>!I=˙vŖ>ę*ė>BÔf?kÂ*?eLŠ>œ×>‚īÆ>!­==—ë>jâ>›¯f?NØ)?P;Ļ>TĖ×>üšÉ>ņ“1=šū›>œÉ>\P>…aČ>7ōS?PéĶ>2Ã>]Ę>$Äj?ŅË>ĐÍP>1¤Ę>÷‰S?ÄŌ>ŦÆ>gJÍ>ģ!k?<&Î>/öH>$Þ>žęU?ķOá>¯Ŋ>ø^ŋ>1Yk?ūĖ?–Ū6?)Eč>ôBŋ=x?ķÕ> Á)?õ܅>ü™?ŧá??ķ@Ų>ņŪĄ=}™?TĖ> /1?i†>’Ž?ZÜ8?˚ã>‰qŗ=áK?†(Õ>pÄ,?‘¯‡>.´ë>Ė“_?ßŗ ?ËFõ>[Į?5Îß>•÷B=Ø?Í>Ķä>[¨`?ēs ?Škë>¨Į?á>f*<=}ēÎ>ķ)â>Ema? ¨ ?˛Ėæ>hĸ˙>]Éá> <=S…Ō>‰Ž?“Y™>Ķ(Ė>ŌĻ?Ë'ø>~-?éēĒ>_?E?ŨŒ>*ŋ>į*?Ķe?¨ŗû>ywĄ>Įsa?>\?ˆ3>ƒČ>Ō(?žÆö>¨Ŗ?ˇĢ>ŋa?Ą&Á>eFI?Hâų>ƒüT>õéü>v ?oč?ÄbĶ>ô_Ŋ>ŗP?ãķķ>7ŠY>Ųsú>6° ?4=?ŋÕ>U^Ŋ>FĻM?*-ö>cœW>@pų>˛c ?-d?‘ Ö>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_causal_expanded/000077500000000000000000000000001511334557700307375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_causal_expanded/model.onnx000066400000000000000000000403731511334557700327520ustar00rootroot00000000000000  backend-test:ၠj QBAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize"Shape* start * end : z Q@Attention_test_attention_3d_gqa_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { KAAttention_test_attention_3d_gqa_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : iFAttention_test_attention_3d_gqa_causal_expanded_function_QNumHeadsAttr"Constant* value*:  : jGAttention_test_attention_3d_gqa_causal_expanded_function_KVNumHeadsAttr"Constant* value*: : k?Attention_test_attention_3d_gqa_causal_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ķ BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize @Attention_test_attention_3d_gqa_causal_expanded_function_QSeqLen FAttention_test_attention_3d_gqa_causal_expanded_function_QNumHeadsAttr ?Attention_test_attention_3d_gqa_causal_expanded_function_NegOneKAttention_test_attention_3d_gqa_causal_expanded_function_QIntermediateShape"Concat* axis : ö BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize AAttention_test_attention_3d_gqa_causal_expanded_function_KVSeqLen GAttention_test_attention_3d_gqa_causal_expanded_function_KVNumHeadsAttr ?Attention_test_attention_3d_gqa_causal_expanded_function_NegOneLAttention_test_attention_3d_gqa_causal_expanded_function_KVIntermediateShape"Concat* axis : Ŗ Q KAttention_test_attention_3d_gqa_causal_expanded_function_QIntermediateShapeFAttention_test_attention_3d_gqa_causal_expanded_function_QIntermediate"Reshape: ¤ K LAttention_test_attention_3d_gqa_causal_expanded_function_KVIntermediateShapeFAttention_test_attention_3d_gqa_causal_expanded_function_KIntermediate"Reshape: ¤ V LAttention_test_attention_3d_gqa_causal_expanded_function_KVIntermediateShapeFAttention_test_attention_3d_gqa_causal_expanded_function_VIntermediate"Reshape: Ŧ FAttention_test_attention_3d_gqa_causal_expanded_function_QIntermediateBAttention_test_attention_3d_gqa_causal_expanded_function_QReshaped" Transpose* perm@@@@ : Ŧ FAttention_test_attention_3d_gqa_causal_expanded_function_KIntermediateBAttention_test_attention_3d_gqa_causal_expanded_function_KReshaped" Transpose* perm@@@@ : Ŧ FAttention_test_attention_3d_gqa_causal_expanded_function_VIntermediateBAttention_test_attention_3d_gqa_causal_expanded_function_VReshaped" Transpose* perm@@@@ : Ģ BAttention_test_attention_3d_gqa_causal_expanded_function_QReshapedBAttention_test_attention_3d_gqa_causal_expanded_function_QNumHeads"Shape* start * end : Ŧ BAttention_test_attention_3d_gqa_causal_expanded_function_KReshapedCAttention_test_attention_3d_gqa_causal_expanded_function_KVNumHeads"Shape* start * end : Ŧ BAttention_test_attention_3d_gqa_causal_expanded_function_QReshapedCAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSize"Shape* start * end : ž CAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSizeDAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSizeF"Cast* to : Ģ BAttention_test_attention_3d_gqa_causal_expanded_function_VReshapedBAttention_test_attention_3d_gqa_causal_expanded_function_VHeadSize"Shape* start * end : • DAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSizeFEAttention_test_attention_3d_gqa_causal_expanded_function_SqrtHeadSize"Sqrt: a>Attention_test_attention_3d_gqa_causal_expanded_function_One1D"Constant* value*: : e?Attention_test_attention_3d_gqa_causal_expanded_function_One1DF"Constant* value* "€? : b?Attention_test_attention_3d_gqa_causal_expanded_function_Zero1D"Constant* value*: : Ų ?Attention_test_attention_3d_gqa_causal_expanded_function_One1DF EAttention_test_attention_3d_gqa_causal_expanded_function_SqrtHeadSizeHAttention_test_attention_3d_gqa_causal_expanded_function_CalculatedScale"Div: c?Attention_test_attention_3d_gqa_causal_expanded_function_ScaleF"Constant* value*"€? : œ HAttention_test_attention_3d_gqa_causal_expanded_function_CalculatedScaleDAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactor"Identity: ˜ DAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorHAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorSqrt"Sqrt: ¤ HAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorSqrtEAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorF"Cast* to : • BAttention_test_attention_3d_gqa_causal_expanded_function_KReshapedCAttention_test_attention_3d_gqa_causal_expanded_function_PresentKey"Identity: hEAttention_test_attention_3d_gqa_causal_expanded_function_PastKVSeqLen"Constant* value*: : — BAttention_test_attention_3d_gqa_causal_expanded_function_VReshapedEAttention_test_attention_3d_gqa_causal_expanded_function_PresentValue"Identity: Ā CAttention_test_attention_3d_gqa_causal_expanded_function_PresentKeyDAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : į @Attention_test_attention_3d_gqa_causal_expanded_function_QSeqLen DAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLenFAttention_test_attention_3d_gqa_causal_expanded_function_AttnBiasShape"Concat* axis : jDAttention_test_attention_3d_gqa_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : iCAttention_test_attention_3d_gqa_causal_expanded_function_ScalarZero"Constant* value* " : ž FAttention_test_attention_3d_gqa_causal_expanded_function_AttnBiasShapeAAttention_test_attention_3d_gqa_causal_expanded_function_AttnBias"ConstantOfShape: `=Attention_test_attention_3d_gqa_causal_expanded_function_Zero"Constant* value*: : _Attention_test_attention_3d_gqa_causal_expanded_function_One1DFAttention_test_attention_3d_gqa_causal_expanded_function_InterleaveDim"Where: a>Attention_test_attention_3d_gqa_causal_expanded_function_Two1D"Constant* value*: : Ø CAttention_test_attention_3d_gqa_causal_expanded_function_PresentKey >Attention_test_attention_3d_gqa_causal_expanded_function_Two1DDAttention_test_attention_3d_gqa_causal_expanded_function_KUnsqueezed" Unsqueeze: Ú EAttention_test_attention_3d_gqa_causal_expanded_function_PresentValue >Attention_test_attention_3d_gqa_causal_expanded_function_Two1DDAttention_test_attention_3d_gqa_causal_expanded_function_VUnsqueezed" Unsqueeze: ē BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize CAttention_test_attention_3d_gqa_causal_expanded_function_KVNumHeads FAttention_test_attention_3d_gqa_causal_expanded_function_InterleaveDim DAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSizeEAttention_test_attention_3d_gqa_causal_expanded_function_KExpandShape"Concat* axis : Û DAttention_test_attention_3d_gqa_causal_expanded_function_KUnsqueezed EAttention_test_attention_3d_gqa_causal_expanded_function_KExpandShapeBAttention_test_attention_3d_gqa_causal_expanded_function_KExpanded"Expand: š BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize CAttention_test_attention_3d_gqa_causal_expanded_function_KVNumHeads FAttention_test_attention_3d_gqa_causal_expanded_function_InterleaveDim DAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLen BAttention_test_attention_3d_gqa_causal_expanded_function_VHeadSizeEAttention_test_attention_3d_gqa_causal_expanded_function_VExpandShape"Concat* axis : Û DAttention_test_attention_3d_gqa_causal_expanded_function_VUnsqueezed EAttention_test_attention_3d_gqa_causal_expanded_function_VExpandShapeBAttention_test_attention_3d_gqa_causal_expanded_function_VExpanded"Expand: ô BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize BAttention_test_attention_3d_gqa_causal_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_causal_expanded_function_QKHeadSizeHAttention_test_attention_3d_gqa_causal_expanded_function_KAttentionShape"Concat* axis : ķ BAttention_test_attention_3d_gqa_causal_expanded_function_BatchSize BAttention_test_attention_3d_gqa_causal_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_causal_expanded_function_NewKVSeqLen BAttention_test_attention_3d_gqa_causal_expanded_function_VHeadSizeHAttention_test_attention_3d_gqa_causal_expanded_function_VAttentionShape"Concat* axis : ã BAttention_test_attention_3d_gqa_causal_expanded_function_KExpanded HAttention_test_attention_3d_gqa_causal_expanded_function_KAttentionShapeHAttention_test_attention_3d_gqa_causal_expanded_function_KAttentionInput"Reshape: ã BAttention_test_attention_3d_gqa_causal_expanded_function_VExpanded HAttention_test_attention_3d_gqa_causal_expanded_function_VAttentionShapeHAttention_test_attention_3d_gqa_causal_expanded_function_VAttentionInput"Reshape: ¯ HAttention_test_attention_3d_gqa_causal_expanded_function_KAttentionInputCAttention_test_attention_3d_gqa_causal_expanded_function_KTranspose" Transpose* perm@@@@ : Ô BAttention_test_attention_3d_gqa_causal_expanded_function_QReshaped EAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorF@Attention_test_attention_3d_gqa_causal_expanded_function_QScaled"Mul: Õ CAttention_test_attention_3d_gqa_causal_expanded_function_KTranspose EAttention_test_attention_3d_gqa_causal_expanded_function_ScaleFactorF@Attention_test_attention_3d_gqa_causal_expanded_function_KScaled"Mul: Õ @Attention_test_attention_3d_gqa_causal_expanded_function_QScaled @Attention_test_attention_3d_gqa_causal_expanded_function_KScaledEAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeight"MatMul: Ÿ EAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeightCAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnCast"Cast* to : ß CAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnCast BAttention_test_attention_3d_gqa_causal_expanded_function_AttnBiasTMAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeightWithBias"Add: Š MAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeightWithBiasLAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeightSoftcap"Identity: § LAttention_test_attention_3d_gqa_causal_expanded_function_QKAttnWeightSoftcapDAttention_test_attention_3d_gqa_causal_expanded_function_SoftmaxCast"Cast* to :  DAttention_test_attention_3d_gqa_causal_expanded_function_SoftmaxCastJAttention_test_attention_3d_gqa_causal_expanded_function_AttnWeightSoftmax"Softmax: ¤ JAttention_test_attention_3d_gqa_causal_expanded_function_AttnWeightSoftmaxCAttention_test_attention_3d_gqa_causal_expanded_function_SoftmaxOut"Cast* to : ß CAttention_test_attention_3d_gqa_causal_expanded_function_SoftmaxOut HAttention_test_attention_3d_gqa_causal_expanded_function_VAttentionInputDAttention_test_attention_3d_gqa_causal_expanded_function_YPreReshape"MatMul: Ģ DAttention_test_attention_3d_gqa_causal_expanded_function_YPreReshapeCAttention_test_attention_3d_gqa_causal_expanded_function_YTranspose" Transpose* perm@@@@ : ž ?Attention_test_attention_3d_gqa_causal_expanded_function_Zero1D ?Attention_test_attention_3d_gqa_causal_expanded_function_Zero1D ?Attention_test_attention_3d_gqa_causal_expanded_function_NegOneBAttention_test_attention_3d_gqa_causal_expanded_function_YNewShape"Concat* axis : — CAttention_test_attention_3d_gqa_causal_expanded_function_YTranspose 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b?Attention_test_attention_3d_gqa_expanded_function_QNumHeadsAttr"Constant* value*:  : c@Attention_test_attention_3d_gqa_expanded_function_KVNumHeadsAttr"Constant* value*: : d8Attention_test_attention_3d_gqa_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Đ ;Attention_test_attention_3d_gqa_expanded_function_BatchSize 9Attention_test_attention_3d_gqa_expanded_function_QSeqLen ?Attention_test_attention_3d_gqa_expanded_function_QNumHeadsAttr 8Attention_test_attention_3d_gqa_expanded_function_NegOneDAttention_test_attention_3d_gqa_expanded_function_QIntermediateShape"Concat* axis : Ķ ;Attention_test_attention_3d_gqa_expanded_function_BatchSize :Attention_test_attention_3d_gqa_expanded_function_KVSeqLen @Attention_test_attention_3d_gqa_expanded_function_KVNumHeadsAttr 8Attention_test_attention_3d_gqa_expanded_function_NegOneEAttention_test_attention_3d_gqa_expanded_function_KVIntermediateShape"Concat* axis : • Q DAttention_test_attention_3d_gqa_expanded_function_QIntermediateShape?Attention_test_attention_3d_gqa_expanded_function_QIntermediate"Reshape: – K EAttention_test_attention_3d_gqa_expanded_function_KVIntermediateShape?Attention_test_attention_3d_gqa_expanded_function_KIntermediate"Reshape: – V EAttention_test_attention_3d_gqa_expanded_function_KVIntermediateShape?Attention_test_attention_3d_gqa_expanded_function_VIntermediate"Reshape: ž ?Attention_test_attention_3d_gqa_expanded_function_QIntermediate;Attention_test_attention_3d_gqa_expanded_function_QReshaped" Transpose* perm@@@@ : ž ?Attention_test_attention_3d_gqa_expanded_function_KIntermediate;Attention_test_attention_3d_gqa_expanded_function_KReshaped" Transpose* perm@@@@ : ž ?Attention_test_attention_3d_gqa_expanded_function_VIntermediate;Attention_test_attention_3d_gqa_expanded_function_VReshaped" Transpose* perm@@@@ :  ;Attention_test_attention_3d_gqa_expanded_function_QReshaped;Attention_test_attention_3d_gqa_expanded_function_QNumHeads"Shape* start * end : ž ;Attention_test_attention_3d_gqa_expanded_function_KReshapedAttention_test_attention_3d_gqa_expanded_function_SqrtHeadSize"Sqrt: Z7Attention_test_attention_3d_gqa_expanded_function_One1D"Constant* value*: : ^8Attention_test_attention_3d_gqa_expanded_function_One1DF"Constant* value* "€? : [8Attention_test_attention_3d_gqa_expanded_function_Zero1D"Constant* value*: : Ä 8Attention_test_attention_3d_gqa_expanded_function_One1DF >Attention_test_attention_3d_gqa_expanded_function_SqrtHeadSizeAAttention_test_attention_3d_gqa_expanded_function_CalculatedScale"Div: \8Attention_test_attention_3d_gqa_expanded_function_ScaleF"Constant* value*"€? : Ž AAttention_test_attention_3d_gqa_expanded_function_CalculatedScale=Attention_test_attention_3d_gqa_expanded_function_ScaleFactor"Identity: Š =Attention_test_attention_3d_gqa_expanded_function_ScaleFactorAAttention_test_attention_3d_gqa_expanded_function_ScaleFactorSqrt"Sqrt: – AAttention_test_attention_3d_gqa_expanded_function_ScaleFactorSqrt>Attention_test_attention_3d_gqa_expanded_function_ScaleFactorF"Cast* to : ‡ ;Attention_test_attention_3d_gqa_expanded_function_KReshapedAttention_test_attention_3d_gqa_expanded_function_PastKVSeqLen"Constant* value*: : ‰ ;Attention_test_attention_3d_gqa_expanded_function_VReshaped>Attention_test_attention_3d_gqa_expanded_function_PresentValue"Identity: ˛ Attention_test_attention_3d_gqa_expanded_function_IDivNumHeads"Cast* to : Į ;Attention_test_attention_3d_gqa_expanded_function_QNumHeads Attention_test_attention_3d_gqa_expanded_function_IDivNumHeads 7Attention_test_attention_3d_gqa_expanded_function_One1D?Attention_test_attention_3d_gqa_expanded_function_InterleaveDim"Where: Z7Attention_test_attention_3d_gqa_expanded_function_Two1D"Constant* value*: : à Attention_test_attention_3d_gqa_expanded_function_PresentValue 7Attention_test_attention_3d_gqa_expanded_function_Two1D=Attention_test_attention_3d_gqa_expanded_function_VUnsqueezed" Unsqueeze:  ;Attention_test_attention_3d_gqa_expanded_function_BatchSize Attention_test_attention_3d_gqa_expanded_function_KExpandShape"Concat* axis : Æ =Attention_test_attention_3d_gqa_expanded_function_KUnsqueezed >Attention_test_attention_3d_gqa_expanded_function_KExpandShape;Attention_test_attention_3d_gqa_expanded_function_KExpanded"Expand:  ;Attention_test_attention_3d_gqa_expanded_function_BatchSize Attention_test_attention_3d_gqa_expanded_function_VExpandShape"Concat* axis : Æ =Attention_test_attention_3d_gqa_expanded_function_VUnsqueezed >Attention_test_attention_3d_gqa_expanded_function_VExpandShape;Attention_test_attention_3d_gqa_expanded_function_VExpanded"Expand: Ņ ;Attention_test_attention_3d_gqa_expanded_function_BatchSize ;Attention_test_attention_3d_gqa_expanded_function_QNumHeads =Attention_test_attention_3d_gqa_expanded_function_NewKVSeqLen Attention_test_attention_3d_gqa_expanded_function_ScaleFactorF9Attention_test_attention_3d_gqa_expanded_function_QScaled"Mul: Ā Attention_test_attention_3d_gqa_expanded_function_ScaleFactorF9Attention_test_attention_3d_gqa_expanded_function_KScaled"Mul: Ā 9Attention_test_attention_3d_gqa_expanded_function_QScaled 9Attention_test_attention_3d_gqa_expanded_function_KScaled>Attention_test_attention_3d_gqa_expanded_function_QKAttnWeight"MatMul: ‘ >Attention_test_attention_3d_gqa_expanded_function_QKAttnWeightQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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QBAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize"Shape* start * end : z Q@Attention_test_attention_3d_gqa_scaled_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { KAAttention_test_attention_3d_gqa_scaled_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : iFAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeadsAttr"Constant* value*:  : jGAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeadsAttr"Constant* value*: : k?Attention_test_attention_3d_gqa_scaled_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ķ BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize @Attention_test_attention_3d_gqa_scaled_expanded_function_QSeqLen FAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeadsAttr ?Attention_test_attention_3d_gqa_scaled_expanded_function_NegOneKAttention_test_attention_3d_gqa_scaled_expanded_function_QIntermediateShape"Concat* axis : ö BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize AAttention_test_attention_3d_gqa_scaled_expanded_function_KVSeqLen GAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeadsAttr ?Attention_test_attention_3d_gqa_scaled_expanded_function_NegOneLAttention_test_attention_3d_gqa_scaled_expanded_function_KVIntermediateShape"Concat* axis : Ŗ Q KAttention_test_attention_3d_gqa_scaled_expanded_function_QIntermediateShapeFAttention_test_attention_3d_gqa_scaled_expanded_function_QIntermediate"Reshape: ¤ K LAttention_test_attention_3d_gqa_scaled_expanded_function_KVIntermediateShapeFAttention_test_attention_3d_gqa_scaled_expanded_function_KIntermediate"Reshape: ¤ V LAttention_test_attention_3d_gqa_scaled_expanded_function_KVIntermediateShapeFAttention_test_attention_3d_gqa_scaled_expanded_function_VIntermediate"Reshape: Ŧ FAttention_test_attention_3d_gqa_scaled_expanded_function_QIntermediateBAttention_test_attention_3d_gqa_scaled_expanded_function_QReshaped" Transpose* perm@@@@ : Ŧ FAttention_test_attention_3d_gqa_scaled_expanded_function_KIntermediateBAttention_test_attention_3d_gqa_scaled_expanded_function_KReshaped" Transpose* perm@@@@ : Ŧ FAttention_test_attention_3d_gqa_scaled_expanded_function_VIntermediateBAttention_test_attention_3d_gqa_scaled_expanded_function_VReshaped" Transpose* perm@@@@ : Ģ BAttention_test_attention_3d_gqa_scaled_expanded_function_QReshapedBAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads"Shape* start * end : Ŧ BAttention_test_attention_3d_gqa_scaled_expanded_function_KReshapedCAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeads"Shape* start * end : Ŧ BAttention_test_attention_3d_gqa_scaled_expanded_function_QReshapedCAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSize"Shape* start * end : ž CAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSizeDAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSizeF"Cast* to : Ģ BAttention_test_attention_3d_gqa_scaled_expanded_function_VReshapedBAttention_test_attention_3d_gqa_scaled_expanded_function_VHeadSize"Shape* start * end : • DAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSizeFEAttention_test_attention_3d_gqa_scaled_expanded_function_SqrtHeadSize"Sqrt: a>Attention_test_attention_3d_gqa_scaled_expanded_function_One1D"Constant* value*: : e?Attention_test_attention_3d_gqa_scaled_expanded_function_One1DF"Constant* value* "€? : b?Attention_test_attention_3d_gqa_scaled_expanded_function_Zero1D"Constant* value*: : Ų ?Attention_test_attention_3d_gqa_scaled_expanded_function_One1DF EAttention_test_attention_3d_gqa_scaled_expanded_function_SqrtHeadSizeHAttention_test_attention_3d_gqa_scaled_expanded_function_CalculatedScale"Div: c?Attention_test_attention_3d_gqa_scaled_expanded_function_ScaleF"Constant* value*" ×#< : “ ?Attention_test_attention_3d_gqa_scaled_expanded_function_ScaleFDAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactor"Identity: ˜ DAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorHAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorSqrt"Sqrt: ¤ HAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorSqrtEAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorF"Cast* to : • BAttention_test_attention_3d_gqa_scaled_expanded_function_KReshapedCAttention_test_attention_3d_gqa_scaled_expanded_function_PresentKey"Identity: hEAttention_test_attention_3d_gqa_scaled_expanded_function_PastKVSeqLen"Constant* value*: : — BAttention_test_attention_3d_gqa_scaled_expanded_function_VReshapedEAttention_test_attention_3d_gqa_scaled_expanded_function_PresentValue"Identity: Ā CAttention_test_attention_3d_gqa_scaled_expanded_function_PresentKeyDAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : į @Attention_test_attention_3d_gqa_scaled_expanded_function_QSeqLen DAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLenFAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasShape"Concat* axis : jDAttention_test_attention_3d_gqa_scaled_expanded_function_FloatNegInf"Constant* value* "€˙ : iCAttention_test_attention_3d_gqa_scaled_expanded_function_ScalarZero"Constant* value* " : ž FAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasShapeAAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBias"ConstantOfShape:  AAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasLAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasCausalOrNot"Identity: Ĩ LAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasCausalOrNotBAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasT"Cast* to : Ö BAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeadsBAttention_test_attention_3d_gqa_scaled_expanded_function_NGQACond1"Equal: Ž BAttention_test_attention_3d_gqa_scaled_expanded_function_NGQACond1AAttention_test_attention_3d_gqa_scaled_expanded_function_GQACond1"Not: Ö BAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeadsDAttention_test_attention_3d_gqa_scaled_expanded_function_DivNumHeads"Div:   DAttention_test_attention_3d_gqa_scaled_expanded_function_DivNumHeadsEAttention_test_attention_3d_gqa_scaled_expanded_function_IDivNumHeads"Cast* to : Ü BAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeadsJAttention_test_attention_3d_gqa_scaled_expanded_function_RemainderNumHeads"Mod: Ų JAttention_test_attention_3d_gqa_scaled_expanded_function_RemainderNumHeads ?Attention_test_attention_3d_gqa_scaled_expanded_function_Zero1DAAttention_test_attention_3d_gqa_scaled_expanded_function_GQACond2"Equal: Ī AAttention_test_attention_3d_gqa_scaled_expanded_function_GQACond1 AAttention_test_attention_3d_gqa_scaled_expanded_function_GQACond2@Attention_test_attention_3d_gqa_scaled_expanded_function_GQACond"And: š @Attention_test_attention_3d_gqa_scaled_expanded_function_GQACond EAttention_test_attention_3d_gqa_scaled_expanded_function_IDivNumHeads >Attention_test_attention_3d_gqa_scaled_expanded_function_One1DFAttention_test_attention_3d_gqa_scaled_expanded_function_InterleaveDim"Where: a>Attention_test_attention_3d_gqa_scaled_expanded_function_Two1D"Constant* value*: : Ø CAttention_test_attention_3d_gqa_scaled_expanded_function_PresentKey >Attention_test_attention_3d_gqa_scaled_expanded_function_Two1DDAttention_test_attention_3d_gqa_scaled_expanded_function_KUnsqueezed" Unsqueeze: Ú EAttention_test_attention_3d_gqa_scaled_expanded_function_PresentValue >Attention_test_attention_3d_gqa_scaled_expanded_function_Two1DDAttention_test_attention_3d_gqa_scaled_expanded_function_VUnsqueezed" Unsqueeze: ē BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize CAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeads FAttention_test_attention_3d_gqa_scaled_expanded_function_InterleaveDim DAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSizeEAttention_test_attention_3d_gqa_scaled_expanded_function_KExpandShape"Concat* axis : Û DAttention_test_attention_3d_gqa_scaled_expanded_function_KUnsqueezed EAttention_test_attention_3d_gqa_scaled_expanded_function_KExpandShapeBAttention_test_attention_3d_gqa_scaled_expanded_function_KExpanded"Expand: š BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize CAttention_test_attention_3d_gqa_scaled_expanded_function_KVNumHeads FAttention_test_attention_3d_gqa_scaled_expanded_function_InterleaveDim DAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLen BAttention_test_attention_3d_gqa_scaled_expanded_function_VHeadSizeEAttention_test_attention_3d_gqa_scaled_expanded_function_VExpandShape"Concat* axis : Û DAttention_test_attention_3d_gqa_scaled_expanded_function_VUnsqueezed EAttention_test_attention_3d_gqa_scaled_expanded_function_VExpandShapeBAttention_test_attention_3d_gqa_scaled_expanded_function_VExpanded"Expand: ô BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize BAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_scaled_expanded_function_QKHeadSizeHAttention_test_attention_3d_gqa_scaled_expanded_function_KAttentionShape"Concat* axis : ķ BAttention_test_attention_3d_gqa_scaled_expanded_function_BatchSize BAttention_test_attention_3d_gqa_scaled_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_scaled_expanded_function_NewKVSeqLen BAttention_test_attention_3d_gqa_scaled_expanded_function_VHeadSizeHAttention_test_attention_3d_gqa_scaled_expanded_function_VAttentionShape"Concat* axis : ã BAttention_test_attention_3d_gqa_scaled_expanded_function_KExpanded HAttention_test_attention_3d_gqa_scaled_expanded_function_KAttentionShapeHAttention_test_attention_3d_gqa_scaled_expanded_function_KAttentionInput"Reshape: ã BAttention_test_attention_3d_gqa_scaled_expanded_function_VExpanded HAttention_test_attention_3d_gqa_scaled_expanded_function_VAttentionShapeHAttention_test_attention_3d_gqa_scaled_expanded_function_VAttentionInput"Reshape: ¯ HAttention_test_attention_3d_gqa_scaled_expanded_function_KAttentionInputCAttention_test_attention_3d_gqa_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : Ô BAttention_test_attention_3d_gqa_scaled_expanded_function_QReshaped EAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorF@Attention_test_attention_3d_gqa_scaled_expanded_function_QScaled"Mul: Õ CAttention_test_attention_3d_gqa_scaled_expanded_function_KTranspose EAttention_test_attention_3d_gqa_scaled_expanded_function_ScaleFactorF@Attention_test_attention_3d_gqa_scaled_expanded_function_KScaled"Mul: Õ @Attention_test_attention_3d_gqa_scaled_expanded_function_QScaled @Attention_test_attention_3d_gqa_scaled_expanded_function_KScaledEAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeight"MatMul: Ÿ EAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeightCAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnCast"Cast* to : ß CAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnCast BAttention_test_attention_3d_gqa_scaled_expanded_function_AttnBiasTMAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeightWithBias"Add: Š MAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeightWithBiasLAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeightSoftcap"Identity: § LAttention_test_attention_3d_gqa_scaled_expanded_function_QKAttnWeightSoftcapDAttention_test_attention_3d_gqa_scaled_expanded_function_SoftmaxCast"Cast* to :  DAttention_test_attention_3d_gqa_scaled_expanded_function_SoftmaxCastJAttention_test_attention_3d_gqa_scaled_expanded_function_AttnWeightSoftmax"Softmax: ¤ JAttention_test_attention_3d_gqa_scaled_expanded_function_AttnWeightSoftmaxCAttention_test_attention_3d_gqa_scaled_expanded_function_SoftmaxOut"Cast* to : ß CAttention_test_attention_3d_gqa_scaled_expanded_function_SoftmaxOut HAttention_test_attention_3d_gqa_scaled_expanded_function_VAttentionInputDAttention_test_attention_3d_gqa_scaled_expanded_function_YPreReshape"MatMul: Ģ DAttention_test_attention_3d_gqa_scaled_expanded_function_YPreReshapeCAttention_test_attention_3d_gqa_scaled_expanded_function_YTranspose" Transpose* perm@@@@ : ž ?Attention_test_attention_3d_gqa_scaled_expanded_function_Zero1D ?Attention_test_attention_3d_gqa_scaled_expanded_function_Zero1D ?Attention_test_attention_3d_gqa_scaled_expanded_function_NegOneBAttention_test_attention_3d_gqa_scaled_expanded_function_YNewShape"Concat* axis : — CAttention_test_attention_3d_gqa_scaled_expanded_function_YTranspose BAttention_test_attention_3d_gqa_scaled_expanded_function_YNewShapeY"Reshape:%test_attention_3d_gqa_scaled_expandedZ Q    HZ K    Z V    b Y    HB 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?kęv>)Šû>°<*?Ļ@?_>ü>ę‡ē>xŒū>æI*?ŸŦ?ú>¸gû>ĨĀQ?k˜ī>cÂ>0Ô?ķ@Ø>R >|‚Æ> Yî>t1R?:€ė> cĀ>k‡?ҊØ>Āf>ŠŋÆ>õˆč>ßUQ?íæ>‡Šē>†^?į×>Âč)>ŋäĮ>ōĄ?äŌ¯> …Ę>œ?V•Ü>šéÕ>3›×>tģT?~á?ÁŽ´>ėĀĀ>S{!?>Ų>˜˛Ī>B}Å>+‘T?āR?åŗą>1‘Æ>>k?Ö>ôãŌ>ēÍ>DCU?dŊ>Kõžˇ˙>m*?/‘?+ĸø>Jē>Ē…@?Å ?Inv>`Ūũ>îG)?K^?+Ųú>ځŧ>É@?§‚ ?¸0o>û\ū>œN)?T.?”ĩø>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_softcap_expanded/000077500000000000000000000000001511334557700311265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_softcap_expanded/model.onnx000066400000000000000000000344521511334557700331420ustar00rootroot00000000000000  backend-test:‘r k QCAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize"Shape* start * end : { QAAttention_test_attention_3d_gqa_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : | KBAttention_test_attention_3d_gqa_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : jGAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeadsAttr"Constant* value*:  : kHAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeadsAttr"Constant* value*: : l@Attention_test_attention_3d_gqa_softcap_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ø CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize AAttention_test_attention_3d_gqa_softcap_expanded_function_QSeqLen GAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeadsAttr @Attention_test_attention_3d_gqa_softcap_expanded_function_NegOneLAttention_test_attention_3d_gqa_softcap_expanded_function_QIntermediateShape"Concat* axis : û CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize BAttention_test_attention_3d_gqa_softcap_expanded_function_KVSeqLen HAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeadsAttr @Attention_test_attention_3d_gqa_softcap_expanded_function_NegOneMAttention_test_attention_3d_gqa_softcap_expanded_function_KVIntermediateShape"Concat* axis : Ĩ Q LAttention_test_attention_3d_gqa_softcap_expanded_function_QIntermediateShapeGAttention_test_attention_3d_gqa_softcap_expanded_function_QIntermediate"Reshape: Ļ K MAttention_test_attention_3d_gqa_softcap_expanded_function_KVIntermediateShapeGAttention_test_attention_3d_gqa_softcap_expanded_function_KIntermediate"Reshape: Ļ V MAttention_test_attention_3d_gqa_softcap_expanded_function_KVIntermediateShapeGAttention_test_attention_3d_gqa_softcap_expanded_function_VIntermediate"Reshape: Ž GAttention_test_attention_3d_gqa_softcap_expanded_function_QIntermediateCAttention_test_attention_3d_gqa_softcap_expanded_function_QReshaped" Transpose* perm@@@@ : Ž GAttention_test_attention_3d_gqa_softcap_expanded_function_KIntermediateCAttention_test_attention_3d_gqa_softcap_expanded_function_KReshaped" Transpose* perm@@@@ : Ž GAttention_test_attention_3d_gqa_softcap_expanded_function_VIntermediateCAttention_test_attention_3d_gqa_softcap_expanded_function_VReshaped" Transpose* perm@@@@ : ­ CAttention_test_attention_3d_gqa_softcap_expanded_function_QReshapedCAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads"Shape* start * end : Ž CAttention_test_attention_3d_gqa_softcap_expanded_function_KReshapedDAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeads"Shape* start * end : Ž CAttention_test_attention_3d_gqa_softcap_expanded_function_QReshapedDAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSize"Shape* start * end :   DAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSizeEAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSizeF"Cast* to : ­ CAttention_test_attention_3d_gqa_softcap_expanded_function_VReshapedCAttention_test_attention_3d_gqa_softcap_expanded_function_VHeadSize"Shape* start * end : — EAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSizeFFAttention_test_attention_3d_gqa_softcap_expanded_function_SqrtHeadSize"Sqrt: b?Attention_test_attention_3d_gqa_softcap_expanded_function_One1D"Constant* value*: : f@Attention_test_attention_3d_gqa_softcap_expanded_function_One1DF"Constant* value* "€? : c@Attention_test_attention_3d_gqa_softcap_expanded_function_Zero1D"Constant* value*: : Ü @Attention_test_attention_3d_gqa_softcap_expanded_function_One1DF FAttention_test_attention_3d_gqa_softcap_expanded_function_SqrtHeadSizeIAttention_test_attention_3d_gqa_softcap_expanded_function_CalculatedScale"Div: d@Attention_test_attention_3d_gqa_softcap_expanded_function_ScaleF"Constant* value*"€? : ž IAttention_test_attention_3d_gqa_softcap_expanded_function_CalculatedScaleEAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactor"Identity: š EAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorIAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorSqrt"Sqrt: Ļ IAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorSqrtFAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorF"Cast* to : — CAttention_test_attention_3d_gqa_softcap_expanded_function_KReshapedDAttention_test_attention_3d_gqa_softcap_expanded_function_PresentKey"Identity: iFAttention_test_attention_3d_gqa_softcap_expanded_function_PastKVSeqLen"Constant* value*: : ™ CAttention_test_attention_3d_gqa_softcap_expanded_function_VReshapedFAttention_test_attention_3d_gqa_softcap_expanded_function_PresentValue"Identity:  DAttention_test_attention_3d_gqa_softcap_expanded_function_PresentKeyEAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ę AAttention_test_attention_3d_gqa_softcap_expanded_function_QSeqLen EAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLenGAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasShape"Concat* axis : kEAttention_test_attention_3d_gqa_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : jDAttention_test_attention_3d_gqa_softcap_expanded_function_ScalarZero"Constant* value* " :   GAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasShapeBAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBias"ConstantOfShape: Ÿ BAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasMAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasCausalOrNot"Identity: § MAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasCausalOrNotCAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasT"Cast* to : Ų CAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeadsCAttention_test_attention_3d_gqa_softcap_expanded_function_NGQACond1"Equal:  CAttention_test_attention_3d_gqa_softcap_expanded_function_NGQACond1BAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond1"Not: Ų CAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeadsEAttention_test_attention_3d_gqa_softcap_expanded_function_DivNumHeads"Div: ĸ EAttention_test_attention_3d_gqa_softcap_expanded_function_DivNumHeadsFAttention_test_attention_3d_gqa_softcap_expanded_function_IDivNumHeads"Cast* to : ß CAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeadsKAttention_test_attention_3d_gqa_softcap_expanded_function_RemainderNumHeads"Mod: Ü KAttention_test_attention_3d_gqa_softcap_expanded_function_RemainderNumHeads @Attention_test_attention_3d_gqa_softcap_expanded_function_Zero1DBAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond2"Equal: Ō BAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond1 BAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond2AAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond"And: ž AAttention_test_attention_3d_gqa_softcap_expanded_function_GQACond FAttention_test_attention_3d_gqa_softcap_expanded_function_IDivNumHeads ?Attention_test_attention_3d_gqa_softcap_expanded_function_One1DGAttention_test_attention_3d_gqa_softcap_expanded_function_InterleaveDim"Where: b?Attention_test_attention_3d_gqa_softcap_expanded_function_Two1D"Constant* value*: : Û DAttention_test_attention_3d_gqa_softcap_expanded_function_PresentKey ?Attention_test_attention_3d_gqa_softcap_expanded_function_Two1DEAttention_test_attention_3d_gqa_softcap_expanded_function_KUnsqueezed" Unsqueeze: Ũ FAttention_test_attention_3d_gqa_softcap_expanded_function_PresentValue ?Attention_test_attention_3d_gqa_softcap_expanded_function_Two1DEAttention_test_attention_3d_gqa_softcap_expanded_function_VUnsqueezed" Unsqueeze: Ā CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize DAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeads GAttention_test_attention_3d_gqa_softcap_expanded_function_InterleaveDim EAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLen DAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSizeFAttention_test_attention_3d_gqa_softcap_expanded_function_KExpandShape"Concat* axis : Ū EAttention_test_attention_3d_gqa_softcap_expanded_function_KUnsqueezed FAttention_test_attention_3d_gqa_softcap_expanded_function_KExpandShapeCAttention_test_attention_3d_gqa_softcap_expanded_function_KExpanded"Expand: ŋ CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize DAttention_test_attention_3d_gqa_softcap_expanded_function_KVNumHeads GAttention_test_attention_3d_gqa_softcap_expanded_function_InterleaveDim EAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_softcap_expanded_function_VHeadSizeFAttention_test_attention_3d_gqa_softcap_expanded_function_VExpandShape"Concat* axis : Ū EAttention_test_attention_3d_gqa_softcap_expanded_function_VUnsqueezed FAttention_test_attention_3d_gqa_softcap_expanded_function_VExpandShapeCAttention_test_attention_3d_gqa_softcap_expanded_function_VExpanded"Expand: ų CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize CAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads EAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLen DAttention_test_attention_3d_gqa_softcap_expanded_function_QKHeadSizeIAttention_test_attention_3d_gqa_softcap_expanded_function_KAttentionShape"Concat* axis : ø CAttention_test_attention_3d_gqa_softcap_expanded_function_BatchSize CAttention_test_attention_3d_gqa_softcap_expanded_function_QNumHeads EAttention_test_attention_3d_gqa_softcap_expanded_function_NewKVSeqLen CAttention_test_attention_3d_gqa_softcap_expanded_function_VHeadSizeIAttention_test_attention_3d_gqa_softcap_expanded_function_VAttentionShape"Concat* axis : æ CAttention_test_attention_3d_gqa_softcap_expanded_function_KExpanded IAttention_test_attention_3d_gqa_softcap_expanded_function_KAttentionShapeIAttention_test_attention_3d_gqa_softcap_expanded_function_KAttentionInput"Reshape: æ CAttention_test_attention_3d_gqa_softcap_expanded_function_VExpanded IAttention_test_attention_3d_gqa_softcap_expanded_function_VAttentionShapeIAttention_test_attention_3d_gqa_softcap_expanded_function_VAttentionInput"Reshape: ą IAttention_test_attention_3d_gqa_softcap_expanded_function_KAttentionInputDAttention_test_attention_3d_gqa_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : × CAttention_test_attention_3d_gqa_softcap_expanded_function_QReshaped FAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorFAAttention_test_attention_3d_gqa_softcap_expanded_function_QScaled"Mul: Ø DAttention_test_attention_3d_gqa_softcap_expanded_function_KTranspose FAttention_test_attention_3d_gqa_softcap_expanded_function_ScaleFactorFAAttention_test_attention_3d_gqa_softcap_expanded_function_KScaled"Mul: Ø AAttention_test_attention_3d_gqa_softcap_expanded_function_QScaled AAttention_test_attention_3d_gqa_softcap_expanded_function_KScaledFAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeight"MatMul: Ą FAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeightDAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnCast"Cast* to : â DAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnCast CAttention_test_attention_3d_gqa_softcap_expanded_function_AttnBiasTNAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeightWithBias"Add: gAAttention_test_attention_3d_gqa_softcap_expanded_function_Softcap"Constant* value* "@@ : š AAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapBAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapF"Cast* to : á NAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeightWithBias BAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapFDAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapDiv"Div: • DAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapDivEAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapTanh"Tanh: á EAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapTanh BAttention_test_attention_3d_gqa_softcap_expanded_function_SoftcapFMAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeightSoftcap"Mul: Š MAttention_test_attention_3d_gqa_softcap_expanded_function_QKAttnWeightSoftcapEAttention_test_attention_3d_gqa_softcap_expanded_function_SoftmaxCast"Cast* to : Ÿ EAttention_test_attention_3d_gqa_softcap_expanded_function_SoftmaxCastKAttention_test_attention_3d_gqa_softcap_expanded_function_AttnWeightSoftmax"Softmax: Ļ KAttention_test_attention_3d_gqa_softcap_expanded_function_AttnWeightSoftmaxDAttention_test_attention_3d_gqa_softcap_expanded_function_SoftmaxOut"Cast* to : â DAttention_test_attention_3d_gqa_softcap_expanded_function_SoftmaxOut IAttention_test_attention_3d_gqa_softcap_expanded_function_VAttentionInputEAttention_test_attention_3d_gqa_softcap_expanded_function_YPreReshape"MatMul: ­ EAttention_test_attention_3d_gqa_softcap_expanded_function_YPreReshapeDAttention_test_attention_3d_gqa_softcap_expanded_function_YTranspose" Transpose* perm@@@@ : ĸ @Attention_test_attention_3d_gqa_softcap_expanded_function_Zero1D @Attention_test_attention_3d_gqa_softcap_expanded_function_Zero1D 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QQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize"Shape* start * end : ‰ QOAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Š KPAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : xUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeadsAttr"Constant* value*:  : yVAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeadsAttr"Constant* value*: : zNAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ž QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QSeqLen UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeadsAttr NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NegOneZAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QIntermediateShape"Concat* axis : Á QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize PAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVSeqLen VAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeadsAttr NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NegOne[Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVIntermediateShape"Concat* axis : Á Q ZAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QIntermediateShapeUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QIntermediate"Reshape:  K [Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVIntermediateShapeUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KIntermediate"Reshape:  V [Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVIntermediateShapeUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VIntermediate"Reshape: Ę UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QIntermediateQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QReshaped" Transpose* perm@@@@ : Ę UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KIntermediateQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KReshaped" Transpose* perm@@@@ : Ę UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VIntermediateQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VReshaped" Transpose* perm@@@@ : É QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QReshapedQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads"Shape* start * end : Ę QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KReshapedRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end : Ę QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QReshapedRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : ŧ RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSizeSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : É QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VReshapedQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : ŗ SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSizeFTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: pMAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_One1D"Constant* value*: : tNAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : qNAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Zero1D"Constant* value*: : † NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_One1DF TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SqrtHeadSizeWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_CalculatedScale"Div: rNAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : ē WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_CalculatedScaleSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactor"Identity: ļ SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt:  WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorSqrtTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Č past_key QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KReshapedRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentKey"Concat* axis : • past_keyTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : m RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ė past_value QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VReshapedTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentValue"Concat* axis : q TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentValue present_value"Identity: Ū RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentKeySAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ” OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QSeqLen SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLenUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : ySAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : xRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : n attn_maskUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasShort"Identity: ĩ UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasShortPAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBias"Identity: ģ PAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBias[Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: à [Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasCausalOrNotQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ƒ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeadsQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NGQACond1"Equal: Ŧ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NGQACond1PAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond1"Not: ƒ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeadsSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_DivNumHeads"Div: ž SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_DivNumHeadsTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ‰ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeadsYAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_RemainderNumHeads"Mod: † YAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_RemainderNumHeads NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Zero1DPAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond2"Equal: ü PAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond1 PAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond2OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond"And: Ö OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_GQACond TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_IDivNumHeads MAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_One1DUAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_InterleaveDim"Where: pMAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Two1D"Constant* value*: : … RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentKey MAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Two1DSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: ‡ TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_PresentValue MAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Two1DSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ” QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeads UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_InterleaveDim SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLen RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSizeTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KExpandShape"Concat* axis : ˆ SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KUnsqueezed TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KExpandShapeQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KExpanded"Expand: “ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KVNumHeads UAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_InterleaveDim SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLen QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VHeadSizeTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VExpandShape"Concat* axis : ˆ SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VUnsqueezed TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VExpandShapeQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VExpanded"Expand: ŋ QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLen RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKHeadSizeWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : ž QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_BatchSize QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QNumHeads SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NewKVSeqLen QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VHeadSizeWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VAttentionShape"Concat* axis :  QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KExpanded WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KAttentionShapeWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KAttentionInput"Reshape:  QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VExpanded WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VAttentionShapeWAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VAttentionInput"Reshape: Í WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KAttentionInputRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ :  QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QReshaped TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorFOAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QScaled"Mul: ‚ RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KTranspose TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_ScaleFactorFOAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KScaled"Mul: ‚ OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QScaled OAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_KScaledTAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeight"MatMul: Ŋ TAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeightRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnCast"Cast* to : Œ RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnCast QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnBiasT\Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: Į \Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeightWithBias[Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Å [Attention_test_attention_3d_gqa_with_past_and_present_expanded_function_QKAttnWeightSoftcapSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : ģ SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SoftmaxCastYAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax:  YAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_AttnWeightSoftmaxRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : Œ RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_SoftmaxOut WAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_VAttentionInputSAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YPreReshape"MatMul: É SAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YPreReshapeRAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YTranspose" Transpose* perm@@@@ : Ú NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Zero1D NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_Zero1D NAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_NegOneQAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YNewShape"Concat* axis : ĩ RAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YTranspose QAttention_test_attention_3d_gqa_with_past_and_present_expanded_function_YNewShapeY"Reshape:4test_attention_3d_gqa_with_past_and_present_expandedZ Q    HZ K    Z V    Z attn_mask   Z" past_key     Z$ past_value     b Y    Hb% present_key     b' present_value     B test_data_set_0/000077500000000000000000000000001511334557700370365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_with_past_and_present_expandedinput_0.pb000066400000000000000000000044161511334557700407440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_gqa_with_past_and_present_expanded/test_data_set_0HBQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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Ž?ĪpŌ>ķ´> ôÆ> w ?,ëĨ>™éÃ>2t*?ōQx>Uå>O–Å>) ?œŊ/?íõ>īû ?& {>)˛˙>Ōf?Wlü>ũí>P+?8ą>Ô ?VB;?ģ?QhŌ>ŽØŗ>§ŪÆ>‚ƒ ?2ũĨ>wÄ>š*?Cux>V0å>A™Å>ō˙?iČ/?L€õ>ŧ ?ã{>B˜˙>•H?ē[ü>‹î>m +?'[ą>÷?{C;?Cļ?ØqŌ>,´>įÜÆ>tA ?ĀĨ>HĻÃ>6u*?ZŨw>ßÍå>ՌÅ>/ū?×/?øKõ>u ?ÍØz>­Ž˙>#U?˙sü>™ņí>î+?EÚą>0ī?ÄN;?D™? OŌ>&Đŗ>ŅÆ>¯x ?AčĨ>ūÄ>í‹*?¤{x> \å>ŨĸÅ>– ?X˛/?ŧ]õ>C˙ ?HÕz>Ūp˙>Ūd?‘#ü>ĸĶí>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_scaled_expanded/000077500000000000000000000000001511334557700300725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_scaled_expanded/model.onnx000066400000000000000000000314221511334557700321000ustar00rootroot00000000000000  backend-test:ųe f Q>Attention_test_attention_3d_scaled_expanded_function_BatchSize"Shape* start * end : v QAttention_test_attention_3d_scaled_expanded_function_BatchSize Attention_test_attention_3d_scaled_expanded_function_BatchSize =Attention_test_attention_3d_scaled_expanded_function_KVSeqLen CAttention_test_attention_3d_scaled_expanded_function_KVNumHeadsAttr ;Attention_test_attention_3d_scaled_expanded_function_NegOneHAttention_test_attention_3d_scaled_expanded_function_KVIntermediateShape"Concat* axis : › Q GAttention_test_attention_3d_scaled_expanded_function_QIntermediateShapeBAttention_test_attention_3d_scaled_expanded_function_QIntermediate"Reshape: œ K HAttention_test_attention_3d_scaled_expanded_function_KVIntermediateShapeBAttention_test_attention_3d_scaled_expanded_function_KIntermediate"Reshape: œ V HAttention_test_attention_3d_scaled_expanded_function_KVIntermediateShapeBAttention_test_attention_3d_scaled_expanded_function_VIntermediate"Reshape: ¤ BAttention_test_attention_3d_scaled_expanded_function_QIntermediate>Attention_test_attention_3d_scaled_expanded_function_QReshaped" Transpose* perm@@@@ : ¤ BAttention_test_attention_3d_scaled_expanded_function_KIntermediate>Attention_test_attention_3d_scaled_expanded_function_KReshaped" Transpose* perm@@@@ : ¤ BAttention_test_attention_3d_scaled_expanded_function_VIntermediate>Attention_test_attention_3d_scaled_expanded_function_VReshaped" Transpose* perm@@@@ : Ŗ >Attention_test_attention_3d_scaled_expanded_function_QReshaped>Attention_test_attention_3d_scaled_expanded_function_QNumHeads"Shape* start * end : ¤ >Attention_test_attention_3d_scaled_expanded_function_KReshaped?Attention_test_attention_3d_scaled_expanded_function_KVNumHeads"Shape* start * end : ¤ >Attention_test_attention_3d_scaled_expanded_function_QReshaped?Attention_test_attention_3d_scaled_expanded_function_QKHeadSize"Shape* start * end : – ?Attention_test_attention_3d_scaled_expanded_function_QKHeadSize@Attention_test_attention_3d_scaled_expanded_function_QKHeadSizeF"Cast* to : Ŗ >Attention_test_attention_3d_scaled_expanded_function_VReshaped>Attention_test_attention_3d_scaled_expanded_function_VHeadSize"Shape* start * end :  @Attention_test_attention_3d_scaled_expanded_function_QKHeadSizeFAAttention_test_attention_3d_scaled_expanded_function_SqrtHeadSize"Sqrt: ]:Attention_test_attention_3d_scaled_expanded_function_One1D"Constant* value*: : a;Attention_test_attention_3d_scaled_expanded_function_One1DF"Constant* value* "€? : ^;Attention_test_attention_3d_scaled_expanded_function_Zero1D"Constant* value*: : Í ;Attention_test_attention_3d_scaled_expanded_function_One1DF AAttention_test_attention_3d_scaled_expanded_function_SqrtHeadSizeDAttention_test_attention_3d_scaled_expanded_function_CalculatedScale"Div: _;Attention_test_attention_3d_scaled_expanded_function_ScaleF"Constant* value*" ×#< : ‹ ;Attention_test_attention_3d_scaled_expanded_function_ScaleF@Attention_test_attention_3d_scaled_expanded_function_ScaleFactor"Identity:  @Attention_test_attention_3d_scaled_expanded_function_ScaleFactorDAttention_test_attention_3d_scaled_expanded_function_ScaleFactorSqrt"Sqrt: œ DAttention_test_attention_3d_scaled_expanded_function_ScaleFactorSqrtAAttention_test_attention_3d_scaled_expanded_function_ScaleFactorF"Cast* to :  >Attention_test_attention_3d_scaled_expanded_function_KReshaped?Attention_test_attention_3d_scaled_expanded_function_PresentKey"Identity: dAAttention_test_attention_3d_scaled_expanded_function_PastKVSeqLen"Constant* value*: :  >Attention_test_attention_3d_scaled_expanded_function_VReshapedAAttention_test_attention_3d_scaled_expanded_function_PresentValue"Identity: ¸ ?Attention_test_attention_3d_scaled_expanded_function_PresentKey@Attention_test_attention_3d_scaled_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Û Attention_test_attention_3d_scaled_expanded_function_AttnBiasT"Cast* to : Ę >Attention_test_attention_3d_scaled_expanded_function_QNumHeads ?Attention_test_attention_3d_scaled_expanded_function_KVNumHeads>Attention_test_attention_3d_scaled_expanded_function_NGQACond1"Equal: † >Attention_test_attention_3d_scaled_expanded_function_NGQACond1=Attention_test_attention_3d_scaled_expanded_function_GQACond1"Not: Ę >Attention_test_attention_3d_scaled_expanded_function_QNumHeads ?Attention_test_attention_3d_scaled_expanded_function_KVNumHeads@Attention_test_attention_3d_scaled_expanded_function_DivNumHeads"Div: ˜ @Attention_test_attention_3d_scaled_expanded_function_DivNumHeadsAAttention_test_attention_3d_scaled_expanded_function_IDivNumHeads"Cast* to : Đ >Attention_test_attention_3d_scaled_expanded_function_QNumHeads ?Attention_test_attention_3d_scaled_expanded_function_KVNumHeadsFAttention_test_attention_3d_scaled_expanded_function_RemainderNumHeads"Mod: Í FAttention_test_attention_3d_scaled_expanded_function_RemainderNumHeads ;Attention_test_attention_3d_scaled_expanded_function_Zero1D=Attention_test_attention_3d_scaled_expanded_function_GQACond2"Equal: à =Attention_test_attention_3d_scaled_expanded_function_GQACond1 =Attention_test_attention_3d_scaled_expanded_function_GQACond2Attention_test_attention_3d_scaled_expanded_function_BatchSize ?Attention_test_attention_3d_scaled_expanded_function_KVNumHeads BAttention_test_attention_3d_scaled_expanded_function_InterleaveDim @Attention_test_attention_3d_scaled_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_scaled_expanded_function_QKHeadSizeAAttention_test_attention_3d_scaled_expanded_function_KExpandShape"Concat* axis : Ī @Attention_test_attention_3d_scaled_expanded_function_KUnsqueezed AAttention_test_attention_3d_scaled_expanded_function_KExpandShape>Attention_test_attention_3d_scaled_expanded_function_KExpanded"Expand: Ą >Attention_test_attention_3d_scaled_expanded_function_BatchSize ?Attention_test_attention_3d_scaled_expanded_function_KVNumHeads BAttention_test_attention_3d_scaled_expanded_function_InterleaveDim @Attention_test_attention_3d_scaled_expanded_function_NewKVSeqLen >Attention_test_attention_3d_scaled_expanded_function_VHeadSizeAAttention_test_attention_3d_scaled_expanded_function_VExpandShape"Concat* axis : Ī @Attention_test_attention_3d_scaled_expanded_function_VUnsqueezed AAttention_test_attention_3d_scaled_expanded_function_VExpandShape>Attention_test_attention_3d_scaled_expanded_function_VExpanded"Expand: ā >Attention_test_attention_3d_scaled_expanded_function_BatchSize >Attention_test_attention_3d_scaled_expanded_function_QNumHeads @Attention_test_attention_3d_scaled_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_scaled_expanded_function_QKHeadSizeDAttention_test_attention_3d_scaled_expanded_function_KAttentionShape"Concat* axis : ß >Attention_test_attention_3d_scaled_expanded_function_BatchSize >Attention_test_attention_3d_scaled_expanded_function_QNumHeads @Attention_test_attention_3d_scaled_expanded_function_NewKVSeqLen >Attention_test_attention_3d_scaled_expanded_function_VHeadSizeDAttention_test_attention_3d_scaled_expanded_function_VAttentionShape"Concat* axis : × >Attention_test_attention_3d_scaled_expanded_function_KExpanded DAttention_test_attention_3d_scaled_expanded_function_KAttentionShapeDAttention_test_attention_3d_scaled_expanded_function_KAttentionInput"Reshape: × >Attention_test_attention_3d_scaled_expanded_function_VExpanded DAttention_test_attention_3d_scaled_expanded_function_VAttentionShapeDAttention_test_attention_3d_scaled_expanded_function_VAttentionInput"Reshape: § DAttention_test_attention_3d_scaled_expanded_function_KAttentionInput?Attention_test_attention_3d_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : Č >Attention_test_attention_3d_scaled_expanded_function_QReshaped AAttention_test_attention_3d_scaled_expanded_function_ScaleFactorFAttention_test_attention_3d_scaled_expanded_function_AttnBiasTIAttention_test_attention_3d_scaled_expanded_function_QKAttnWeightWithBias"Add: Ą IAttention_test_attention_3d_scaled_expanded_function_QKAttnWeightWithBiasHAttention_test_attention_3d_scaled_expanded_function_QKAttnWeightSoftcap"Identity: Ÿ HAttention_test_attention_3d_scaled_expanded_function_QKAttnWeightSoftcap@Attention_test_attention_3d_scaled_expanded_function_SoftmaxCast"Cast* to : • @Attention_test_attention_3d_scaled_expanded_function_SoftmaxCastFAttention_test_attention_3d_scaled_expanded_function_AttnWeightSoftmax"Softmax: œ FAttention_test_attention_3d_scaled_expanded_function_AttnWeightSoftmax?Attention_test_attention_3d_scaled_expanded_function_SoftmaxOut"Cast* to : Ķ ?Attention_test_attention_3d_scaled_expanded_function_SoftmaxOut DAttention_test_attention_3d_scaled_expanded_function_VAttentionInput@Attention_test_attention_3d_scaled_expanded_function_YPreReshape"MatMul: Ŗ @Attention_test_attention_3d_scaled_expanded_function_YPreReshape?Attention_test_attention_3d_scaled_expanded_function_YTranspose" Transpose* perm@@@@ : Ž ;Attention_test_attention_3d_scaled_expanded_function_Zero1D ;Attention_test_attention_3d_scaled_expanded_function_Zero1D ;Attention_test_attention_3d_scaled_expanded_function_NegOne>Attention_test_attention_3d_scaled_expanded_function_YNewShape"Concat* axis :  ?Attention_test_attention_3d_scaled_expanded_function_YTranspose >Attention_test_attention_3d_scaled_expanded_function_YNewShapeY"Reshape:!test_attention_3d_scaled_expandedZ Q    Z K    Z V    b Y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_scaled_expanded/test_data_set_0/000077500000000000000000000000001511334557700331345ustar00rootroot00000000000000input_0.pb000066400000000000000000000014161511334557700347600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_scaled_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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OŌ>&Đŗ>ŅÆ>¯x ?AčĨ>ūÄ>í‹*?¤{x> \å>ŨĸÅ>– ?X˛/?ŧ]õ>C˙ ?HÕz>Ūp˙>Ūd?‘#ü>ĸĶí>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/000077500000000000000000000000001511334557700264265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/model.onnx000066400000000000000000000003551511334557700304350ustar00rootroot00000000000000  backend-test:Ô S Q K VY" Attention* kv_num_heads * q_num_heads * softcap@@ test_attention_3d_softcapZ Q    Z K    Z V    b Y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/test_data_set_0/000077500000000000000000000000001511334557700314705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/test_data_set_0/input_0.pb000066400000000000000000000014161511334557700333730ustar00rootroot00000000000000BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/test_data_set_0/input_1.pb000066400000000000000000000022161511334557700333730ustar00rootroot00000000000000BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/test_data_set_0/input_2.pb000066400000000000000000000022161511334557700333740ustar00rootroot00000000000000BVJ€ -$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?g(1;ŽŽ%?Nŗ?¤ˇ?xv?w6Š<ŦL2?>MP?š‚?rũĒ>€tJ?M'Į=Râ>™?!§1?L"ē=É9i> Ō>=?Ũc?dk? Ē>M{?Z-_?O˛?˙l?ī— ?É]l?'tT?žáw?ãvk?2˜=o÷2>Š<Į> ŋs?d™>ĄQ$>Ũäb?Īä>‰jh?u$>˙>)?@já>Ĩœ=iK2?V}>ãC"='ˆu=}-z=0ah?i=?jįe?[.,?›h?eā›>tz?Øpš>æøđ>ZŠÁ>GÂz?ĻŲ2> î§>U+.?˙r=˛t?Žô>rh‘>œ"t>ˇ?aŧ>úŧé>ÖÉŦ>Frx?Y¤>'AÆ= ҝ>ŠM?Ęŋ(?=eË>¯Đ?R+´>Ž8?Ÿ8#?L$P?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>TŨ>ŒĄ?ÎčE? pu?¸Eđ=ũ$Û=<ö?hŌ>?b Y?ą’o?ŌÁ{?ΞĖ>KģÂ>([>ŨW/?!(?"°\?3/Į=Üū>ÉÁ?ģZw>˙->} \?QÂo=9õđ>`:í=›ę>ĐŪz? đØ>Šl[?$Cđ=áŠ>ėŊÎ>-´Ė>Ęß+?å~°>mš6?ÁĄ#?Ú^Ė>ĒŨ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī> Ī>ŋ_¤>bZõ<˛ŧę0?_îē>ų#C>Ÿ§œj6d>÷í¤=~ˇŽ=Āĩb>,ÔĖ=Iŗ‡>_y‡=Ø[†=ę4[?Û&>YG?/F?„Žé>ģ >îbL>°Ũ>Y:?×é˛> H?ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?V`B?˜Ĩē>­E?ļĀ>šÕē>F•…>Ōīũ>‚†.?‹˙>Á=?eđ=|Ž#>¸?=ہx?ķũ|;ĢŨ6>Öä?ĨĻ=øÃa?98?Uew?gô?ƒÎ™>Ŧ ?#Jn?ŸP?\Έ>5`?"lž>†Qĩ:$Ą}>īĸ>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap/test_data_set_0/output_0.pb000066400000000000000000000014161511334557700335740ustar00rootroot00000000000000BYJ€Fk>’ ?Ė~? p>8(Ļ>Úgķ>Ą°>F§į>˛”-?ĈÛ>ŪØ?jn?ą7Ŋ>T6?S?He5?eē+?L’ô>a@ã>0ģ(?r–>ĻČã>o”%?žå?~|k>0r?EŽ?7‘>ØJŖ>åõ>ū"¯>nåč>,?TØ>zp ?G7?65Đ>;v?,° ? 90?3b,?Ũöō>™á>U(?"ļ”>Āā>"=$?ōü ?CLZ>ĒÅ?m]ö>§Ą>”žŠ>Wmõ>RĢ>€`Õ>,/?Ųŋā>˙?(?dâ×>žĢ?Ą9 ?˛.*?ô!.?‚sũ>„4ę>Ŋh%?П”>‚á>č%?öŸ?b\> Û?Õđ>댡>$Š>1éķ> Lą>ëžŨ>‚ -?5‘Ü>vˇ?ö7?ûŒÉ>Č?} ?Ÿō/?†&,?E ö>ˇâ>Ė-*?į”>sCå>\w%?-!?#/?Ģ>’A?u79?…Ō?Ŋ”Û>ãĶÁ>đĐ>¤b ?eĻ>´‰Ę>î )?˜ >lá>ārÄ>5s ?}Û-?Õ4ō>m?´`ƒ>y8 ?9n$?4Ī?v ę>î .?ƒ<Ĩ>UK?RŸ8?Ģ?Ļ­Ú>M*ģ>žĶÍ>Qõ ?ų–¨>Å0Î>š—,?Xƒ>ŽÜ>#ÃÄ>7í?iß.? Bō>ōz?iä>{?!Ū ?…r?ūė>D -?˙ >Ā×?Rļ8?¨Ĩ?Û>ÄbÃ>3$Í>'Q?äļ >ÚÁ>QC)?ÍŲr>@ ņ>īžÂ>Ģ…?‰á0?4qė>ø˛?å—~>Fđ?ļk"?TF?ŧč> °-?IƝ>p?Ī.:?CB?Ąë×>fģ>ßĖ>û° ?ĪĨ>ۈĪ>āÜ+?ē„>G1â>DÎÅ>œ› ?Š=,?ú{î>ÚT?å~>Ž"?Į.$?r-?S3å>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap_expanded/000077500000000000000000000000001511334557700302765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_softcap_expanded/model.onnx000066400000000000000000000331321511334557700323040ustar00rootroot00000000000000  backend-test:Ál g Q?Attention_test_attention_3d_softcap_expanded_function_BatchSize"Shape* start * end : w Q=Attention_test_attention_3d_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : x K>Attention_test_attention_3d_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : fCAttention_test_attention_3d_softcap_expanded_function_QNumHeadsAttr"Constant* value*: : gDAttention_test_attention_3d_softcap_expanded_function_KVNumHeadsAttr"Constant* value*: : hAttention_test_attention_3d_softcap_expanded_function_KVSeqLen DAttention_test_attention_3d_softcap_expanded_function_KVNumHeadsAttr Attention_test_attention_3d_softcap_expanded_function_AttnBias"ConstantOfShape: — >Attention_test_attention_3d_softcap_expanded_function_AttnBiasIAttention_test_attention_3d_softcap_expanded_function_AttnBiasCausalOrNot"Identity: Ÿ IAttention_test_attention_3d_softcap_expanded_function_AttnBiasCausalOrNot?Attention_test_attention_3d_softcap_expanded_function_AttnBiasT"Cast* to : Í ?Attention_test_attention_3d_softcap_expanded_function_QNumHeads @Attention_test_attention_3d_softcap_expanded_function_KVNumHeads?Attention_test_attention_3d_softcap_expanded_function_NGQACond1"Equal: ˆ ?Attention_test_attention_3d_softcap_expanded_function_NGQACond1>Attention_test_attention_3d_softcap_expanded_function_GQACond1"Not: Í ?Attention_test_attention_3d_softcap_expanded_function_QNumHeads @Attention_test_attention_3d_softcap_expanded_function_KVNumHeadsAAttention_test_attention_3d_softcap_expanded_function_DivNumHeads"Div: š AAttention_test_attention_3d_softcap_expanded_function_DivNumHeadsBAttention_test_attention_3d_softcap_expanded_function_IDivNumHeads"Cast* to : Ķ ?Attention_test_attention_3d_softcap_expanded_function_QNumHeads @Attention_test_attention_3d_softcap_expanded_function_KVNumHeadsGAttention_test_attention_3d_softcap_expanded_function_RemainderNumHeads"Mod: Đ GAttention_test_attention_3d_softcap_expanded_function_RemainderNumHeads Attention_test_attention_3d_softcap_expanded_function_GQACond2"Equal: Æ >Attention_test_attention_3d_softcap_expanded_function_GQACond1 >Attention_test_attention_3d_softcap_expanded_function_GQACond2=Attention_test_attention_3d_softcap_expanded_function_GQACond"And: Ž =Attention_test_attention_3d_softcap_expanded_function_GQACond BAttention_test_attention_3d_softcap_expanded_function_IDivNumHeads ;Attention_test_attention_3d_softcap_expanded_function_One1DCAttention_test_attention_3d_softcap_expanded_function_InterleaveDim"Where: ^;Attention_test_attention_3d_softcap_expanded_function_Two1D"Constant* value*: : Ī @Attention_test_attention_3d_softcap_expanded_function_PresentKey ;Attention_test_attention_3d_softcap_expanded_function_Two1DAAttention_test_attention_3d_softcap_expanded_function_KUnsqueezed" Unsqueeze: Ņ BAttention_test_attention_3d_softcap_expanded_function_PresentValue ;Attention_test_attention_3d_softcap_expanded_function_Two1DAAttention_test_attention_3d_softcap_expanded_function_VUnsqueezed" Unsqueeze: ¨ ?Attention_test_attention_3d_softcap_expanded_function_BatchSize @Attention_test_attention_3d_softcap_expanded_function_KVNumHeads CAttention_test_attention_3d_softcap_expanded_function_InterleaveDim AAttention_test_attention_3d_softcap_expanded_function_NewKVSeqLen @Attention_test_attention_3d_softcap_expanded_function_QKHeadSizeBAttention_test_attention_3d_softcap_expanded_function_KExpandShape"Concat* axis : Ō AAttention_test_attention_3d_softcap_expanded_function_KUnsqueezed BAttention_test_attention_3d_softcap_expanded_function_KExpandShape?Attention_test_attention_3d_softcap_expanded_function_KExpanded"Expand: § ?Attention_test_attention_3d_softcap_expanded_function_BatchSize @Attention_test_attention_3d_softcap_expanded_function_KVNumHeads CAttention_test_attention_3d_softcap_expanded_function_InterleaveDim AAttention_test_attention_3d_softcap_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_softcap_expanded_function_VHeadSizeBAttention_test_attention_3d_softcap_expanded_function_VExpandShape"Concat* axis : Ō AAttention_test_attention_3d_softcap_expanded_function_VUnsqueezed BAttention_test_attention_3d_softcap_expanded_function_VExpandShape?Attention_test_attention_3d_softcap_expanded_function_VExpanded"Expand: å ?Attention_test_attention_3d_softcap_expanded_function_BatchSize ?Attention_test_attention_3d_softcap_expanded_function_QNumHeads AAttention_test_attention_3d_softcap_expanded_function_NewKVSeqLen @Attention_test_attention_3d_softcap_expanded_function_QKHeadSizeEAttention_test_attention_3d_softcap_expanded_function_KAttentionShape"Concat* axis : ä ?Attention_test_attention_3d_softcap_expanded_function_BatchSize ?Attention_test_attention_3d_softcap_expanded_function_QNumHeads AAttention_test_attention_3d_softcap_expanded_function_NewKVSeqLen ?Attention_test_attention_3d_softcap_expanded_function_VHeadSizeEAttention_test_attention_3d_softcap_expanded_function_VAttentionShape"Concat* axis : Ú ?Attention_test_attention_3d_softcap_expanded_function_KExpanded EAttention_test_attention_3d_softcap_expanded_function_KAttentionShapeEAttention_test_attention_3d_softcap_expanded_function_KAttentionInput"Reshape: Ú ?Attention_test_attention_3d_softcap_expanded_function_VExpanded EAttention_test_attention_3d_softcap_expanded_function_VAttentionShapeEAttention_test_attention_3d_softcap_expanded_function_VAttentionInput"Reshape: Š EAttention_test_attention_3d_softcap_expanded_function_KAttentionInput@Attention_test_attention_3d_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : Ë ?Attention_test_attention_3d_softcap_expanded_function_QReshaped BAttention_test_attention_3d_softcap_expanded_function_ScaleFactorF=Attention_test_attention_3d_softcap_expanded_function_QScaled"Mul: Ė @Attention_test_attention_3d_softcap_expanded_function_KTranspose BAttention_test_attention_3d_softcap_expanded_function_ScaleFactorF=Attention_test_attention_3d_softcap_expanded_function_KScaled"Mul: Ė =Attention_test_attention_3d_softcap_expanded_function_QScaled =Attention_test_attention_3d_softcap_expanded_function_KScaledBAttention_test_attention_3d_softcap_expanded_function_QKAttnWeight"MatMul: ™ BAttention_test_attention_3d_softcap_expanded_function_QKAttnWeight@Attention_test_attention_3d_softcap_expanded_function_QKAttnCast"Cast* to : Ö @Attention_test_attention_3d_softcap_expanded_function_QKAttnCast ?Attention_test_attention_3d_softcap_expanded_function_AttnBiasTJAttention_test_attention_3d_softcap_expanded_function_QKAttnWeightWithBias"Add: c=Attention_test_attention_3d_softcap_expanded_function_Softcap"Constant* value* "@@ : ’ =Attention_test_attention_3d_softcap_expanded_function_Softcap>Attention_test_attention_3d_softcap_expanded_function_SoftcapF"Cast* to : Õ JAttention_test_attention_3d_softcap_expanded_function_QKAttnWeightWithBias >Attention_test_attention_3d_softcap_expanded_function_SoftcapF@Attention_test_attention_3d_softcap_expanded_function_SoftcapDiv"Div:  @Attention_test_attention_3d_softcap_expanded_function_SoftcapDivAAttention_test_attention_3d_softcap_expanded_function_SoftcapTanh"Tanh: Õ AAttention_test_attention_3d_softcap_expanded_function_SoftcapTanh >Attention_test_attention_3d_softcap_expanded_function_SoftcapFIAttention_test_attention_3d_softcap_expanded_function_QKAttnWeightSoftcap"Mul: Ą IAttention_test_attention_3d_softcap_expanded_function_QKAttnWeightSoftcapAAttention_test_attention_3d_softcap_expanded_function_SoftmaxCast"Cast* to : — AAttention_test_attention_3d_softcap_expanded_function_SoftmaxCastGAttention_test_attention_3d_softcap_expanded_function_AttnWeightSoftmax"Softmax: ž GAttention_test_attention_3d_softcap_expanded_function_AttnWeightSoftmax@Attention_test_attention_3d_softcap_expanded_function_SoftmaxOut"Cast* to : Ö @Attention_test_attention_3d_softcap_expanded_function_SoftmaxOut EAttention_test_attention_3d_softcap_expanded_function_VAttentionInputAAttention_test_attention_3d_softcap_expanded_function_YPreReshape"MatMul: Ĩ AAttention_test_attention_3d_softcap_expanded_function_YPreReshape@Attention_test_attention_3d_softcap_expanded_function_YTranspose" Transpose* perm@@@@ : ’ QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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KMAttention_test_attention_3d_transpose_verification_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : uRAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeadsAttr"Constant* value*: : vSAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeadsAttr"Constant* value*: : wKAttention_test_attention_3d_transpose_verification_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ¯ NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize LAttention_test_attention_3d_transpose_verification_expanded_function_QSeqLen RAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeadsAttr KAttention_test_attention_3d_transpose_verification_expanded_function_NegOneWAttention_test_attention_3d_transpose_verification_expanded_function_QIntermediateShape"Concat* axis : ˛ NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize MAttention_test_attention_3d_transpose_verification_expanded_function_KVSeqLen SAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeadsAttr KAttention_test_attention_3d_transpose_verification_expanded_function_NegOneXAttention_test_attention_3d_transpose_verification_expanded_function_KVIntermediateShape"Concat* axis : ģ Q WAttention_test_attention_3d_transpose_verification_expanded_function_QIntermediateShapeRAttention_test_attention_3d_transpose_verification_expanded_function_QIntermediate"Reshape: ŧ K XAttention_test_attention_3d_transpose_verification_expanded_function_KVIntermediateShapeRAttention_test_attention_3d_transpose_verification_expanded_function_KIntermediate"Reshape: ŧ V XAttention_test_attention_3d_transpose_verification_expanded_function_KVIntermediateShapeRAttention_test_attention_3d_transpose_verification_expanded_function_VIntermediate"Reshape: Ä RAttention_test_attention_3d_transpose_verification_expanded_function_QIntermediateNAttention_test_attention_3d_transpose_verification_expanded_function_QReshaped" Transpose* perm@@@@ : Ä RAttention_test_attention_3d_transpose_verification_expanded_function_KIntermediateNAttention_test_attention_3d_transpose_verification_expanded_function_KReshaped" Transpose* perm@@@@ : Ä RAttention_test_attention_3d_transpose_verification_expanded_function_VIntermediateNAttention_test_attention_3d_transpose_verification_expanded_function_VReshaped" Transpose* perm@@@@ : à NAttention_test_attention_3d_transpose_verification_expanded_function_QReshapedNAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads"Shape* start * end : Ä NAttention_test_attention_3d_transpose_verification_expanded_function_KReshapedOAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeads"Shape* start * end : Ä NAttention_test_attention_3d_transpose_verification_expanded_function_QReshapedOAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSize"Shape* start * end : ļ OAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSizePAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSizeF"Cast* to : à NAttention_test_attention_3d_transpose_verification_expanded_function_VReshapedNAttention_test_attention_3d_transpose_verification_expanded_function_VHeadSize"Shape* start * end : ­ PAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSizeFQAttention_test_attention_3d_transpose_verification_expanded_function_SqrtHeadSize"Sqrt: mJAttention_test_attention_3d_transpose_verification_expanded_function_One1D"Constant* value*: : qKAttention_test_attention_3d_transpose_verification_expanded_function_One1DF"Constant* value* "€? : nKAttention_test_attention_3d_transpose_verification_expanded_function_Zero1D"Constant* value*: : ũ KAttention_test_attention_3d_transpose_verification_expanded_function_One1DF QAttention_test_attention_3d_transpose_verification_expanded_function_SqrtHeadSizeTAttention_test_attention_3d_transpose_verification_expanded_function_CalculatedScale"Div: oKAttention_test_attention_3d_transpose_verification_expanded_function_ScaleF"Constant* value*"€? : ´ TAttention_test_attention_3d_transpose_verification_expanded_function_CalculatedScalePAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactor"Identity: ° PAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorTAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorSqrt"Sqrt: ŧ TAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorSqrtQAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorF"Cast* to : ­ NAttention_test_attention_3d_transpose_verification_expanded_function_KReshapedOAttention_test_attention_3d_transpose_verification_expanded_function_PresentKey"Identity: tQAttention_test_attention_3d_transpose_verification_expanded_function_PastKVSeqLen"Constant* value*: : ¯ NAttention_test_attention_3d_transpose_verification_expanded_function_VReshapedQAttention_test_attention_3d_transpose_verification_expanded_function_PresentValue"Identity: Ø OAttention_test_attention_3d_transpose_verification_expanded_function_PresentKeyPAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‹ LAttention_test_attention_3d_transpose_verification_expanded_function_QSeqLen PAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLenRAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasShape"Concat* axis : vPAttention_test_attention_3d_transpose_verification_expanded_function_FloatNegInf"Constant* value* "€˙ : uOAttention_test_attention_3d_transpose_verification_expanded_function_ScalarZero"Constant* value* " : ļ RAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasShapeMAttention_test_attention_3d_transpose_verification_expanded_function_AttnBias"ConstantOfShape: ĩ MAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasXAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasCausalOrNot"Identity: Ŋ XAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasCausalOrNotNAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasT"Cast* to : ú NAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads OAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeadsNAttention_test_attention_3d_transpose_verification_expanded_function_NGQACond1"Equal: Ļ NAttention_test_attention_3d_transpose_verification_expanded_function_NGQACond1MAttention_test_attention_3d_transpose_verification_expanded_function_GQACond1"Not: ú NAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads OAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeadsPAttention_test_attention_3d_transpose_verification_expanded_function_DivNumHeads"Div: ¸ PAttention_test_attention_3d_transpose_verification_expanded_function_DivNumHeadsQAttention_test_attention_3d_transpose_verification_expanded_function_IDivNumHeads"Cast* to : € NAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads OAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeadsVAttention_test_attention_3d_transpose_verification_expanded_function_RemainderNumHeads"Mod: ũ VAttention_test_attention_3d_transpose_verification_expanded_function_RemainderNumHeads KAttention_test_attention_3d_transpose_verification_expanded_function_Zero1DMAttention_test_attention_3d_transpose_verification_expanded_function_GQACond2"Equal: ķ MAttention_test_attention_3d_transpose_verification_expanded_function_GQACond1 MAttention_test_attention_3d_transpose_verification_expanded_function_GQACond2LAttention_test_attention_3d_transpose_verification_expanded_function_GQACond"And: Ę LAttention_test_attention_3d_transpose_verification_expanded_function_GQACond QAttention_test_attention_3d_transpose_verification_expanded_function_IDivNumHeads JAttention_test_attention_3d_transpose_verification_expanded_function_One1DRAttention_test_attention_3d_transpose_verification_expanded_function_InterleaveDim"Where: mJAttention_test_attention_3d_transpose_verification_expanded_function_Two1D"Constant* value*: : ü OAttention_test_attention_3d_transpose_verification_expanded_function_PresentKey JAttention_test_attention_3d_transpose_verification_expanded_function_Two1DPAttention_test_attention_3d_transpose_verification_expanded_function_KUnsqueezed" Unsqueeze: ū QAttention_test_attention_3d_transpose_verification_expanded_function_PresentValue JAttention_test_attention_3d_transpose_verification_expanded_function_Two1DPAttention_test_attention_3d_transpose_verification_expanded_function_VUnsqueezed" Unsqueeze: ‚ NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize OAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeads RAttention_test_attention_3d_transpose_verification_expanded_function_InterleaveDim PAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLen OAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSizeQAttention_test_attention_3d_transpose_verification_expanded_function_KExpandShape"Concat* axis : ˙ PAttention_test_attention_3d_transpose_verification_expanded_function_KUnsqueezed QAttention_test_attention_3d_transpose_verification_expanded_function_KExpandShapeNAttention_test_attention_3d_transpose_verification_expanded_function_KExpanded"Expand:  NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize OAttention_test_attention_3d_transpose_verification_expanded_function_KVNumHeads RAttention_test_attention_3d_transpose_verification_expanded_function_InterleaveDim PAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLen NAttention_test_attention_3d_transpose_verification_expanded_function_VHeadSizeQAttention_test_attention_3d_transpose_verification_expanded_function_VExpandShape"Concat* axis : ˙ PAttention_test_attention_3d_transpose_verification_expanded_function_VUnsqueezed QAttention_test_attention_3d_transpose_verification_expanded_function_VExpandShapeNAttention_test_attention_3d_transpose_verification_expanded_function_VExpanded"Expand: ° NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize NAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads PAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLen OAttention_test_attention_3d_transpose_verification_expanded_function_QKHeadSizeTAttention_test_attention_3d_transpose_verification_expanded_function_KAttentionShape"Concat* axis : ¯ NAttention_test_attention_3d_transpose_verification_expanded_function_BatchSize NAttention_test_attention_3d_transpose_verification_expanded_function_QNumHeads PAttention_test_attention_3d_transpose_verification_expanded_function_NewKVSeqLen NAttention_test_attention_3d_transpose_verification_expanded_function_VHeadSizeTAttention_test_attention_3d_transpose_verification_expanded_function_VAttentionShape"Concat* axis : ‡ NAttention_test_attention_3d_transpose_verification_expanded_function_KExpanded TAttention_test_attention_3d_transpose_verification_expanded_function_KAttentionShapeTAttention_test_attention_3d_transpose_verification_expanded_function_KAttentionInput"Reshape: ‡ NAttention_test_attention_3d_transpose_verification_expanded_function_VExpanded TAttention_test_attention_3d_transpose_verification_expanded_function_VAttentionShapeTAttention_test_attention_3d_transpose_verification_expanded_function_VAttentionInput"Reshape: Į TAttention_test_attention_3d_transpose_verification_expanded_function_KAttentionInputOAttention_test_attention_3d_transpose_verification_expanded_function_KTranspose" Transpose* perm@@@@ : ø NAttention_test_attention_3d_transpose_verification_expanded_function_QReshaped QAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorFLAttention_test_attention_3d_transpose_verification_expanded_function_QScaled"Mul: ų OAttention_test_attention_3d_transpose_verification_expanded_function_KTranspose QAttention_test_attention_3d_transpose_verification_expanded_function_ScaleFactorFLAttention_test_attention_3d_transpose_verification_expanded_function_KScaled"Mul: ų LAttention_test_attention_3d_transpose_verification_expanded_function_QScaled LAttention_test_attention_3d_transpose_verification_expanded_function_KScaledQAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeight"MatMul: ˇ QAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeightOAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnCast"Cast* to : ƒ OAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnCast NAttention_test_attention_3d_transpose_verification_expanded_function_AttnBiasTYAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeightWithBias"Add: Á YAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeightWithBiasXAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeightSoftcap"Identity: ŋ XAttention_test_attention_3d_transpose_verification_expanded_function_QKAttnWeightSoftcapPAttention_test_attention_3d_transpose_verification_expanded_function_SoftmaxCast"Cast* to : ĩ PAttention_test_attention_3d_transpose_verification_expanded_function_SoftmaxCastVAttention_test_attention_3d_transpose_verification_expanded_function_AttnWeightSoftmax"Softmax: ŧ VAttention_test_attention_3d_transpose_verification_expanded_function_AttnWeightSoftmaxOAttention_test_attention_3d_transpose_verification_expanded_function_SoftmaxOut"Cast* to : ƒ OAttention_test_attention_3d_transpose_verification_expanded_function_SoftmaxOut TAttention_test_attention_3d_transpose_verification_expanded_function_VAttentionInputPAttention_test_attention_3d_transpose_verification_expanded_function_YPreReshape"MatMul: à PAttention_test_attention_3d_transpose_verification_expanded_function_YPreReshapeOAttention_test_attention_3d_transpose_verification_expanded_function_YTranspose" Transpose* perm@@@@ : Î KAttention_test_attention_3d_transpose_verification_expanded_function_Zero1D KAttention_test_attention_3d_transpose_verification_expanded_function_Zero1D KAttention_test_attention_3d_transpose_verification_expanded_function_NegOneNAttention_test_attention_3d_transpose_verification_expanded_function_YNewShape"Concat* axis : ¯ OAttention_test_attention_3d_transpose_verification_expanded_function_YTranspose NAttention_test_attention_3d_transpose_verification_expanded_function_YNewShapeY"Reshape:1test_attention_3d_transpose_verification_expandedZ Q     Z K     Z V     b Y     B 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Y?ą’o?ŌÁ{?ΞĖ>KģÂ>([>ŨW/?!(?ėŊÎ>-´Ė>Ęß+?å~°>mš6?ÁĄ#?Ú^Ė>ĒŨ>YŠu?üĶ=Ģū]?\ īę0?_îē>/F?„Žé>ģ >îbL>°Ũ>Y:?×é˛> H?‹˙>Á=?eđ=|Ž#>¸?=ہx?ķũ|;ĢŨ6>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_expanded/000077500000000000000000000000001511334557700332235ustar00rootroot00000000000000model.onnx000066400000000000000000000375431511334557700351640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_expanded  backend-test:Ę~ u QMAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize"Shape* start * end : … QKAttention_test_attention_3d_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : † KLAttention_test_attention_3d_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : tQAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeadsAttr"Constant* value*: : uRAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeadsAttr"Constant* value*: : vJAttention_test_attention_3d_with_past_and_present_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ē MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize KAttention_test_attention_3d_with_past_and_present_expanded_function_QSeqLen QAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeadsAttr JAttention_test_attention_3d_with_past_and_present_expanded_function_NegOneVAttention_test_attention_3d_with_past_and_present_expanded_function_QIntermediateShape"Concat* axis : ­ MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize LAttention_test_attention_3d_with_past_and_present_expanded_function_KVSeqLen RAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeadsAttr JAttention_test_attention_3d_with_past_and_present_expanded_function_NegOneWAttention_test_attention_3d_with_past_and_present_expanded_function_KVIntermediateShape"Concat* axis : š Q VAttention_test_attention_3d_with_past_and_present_expanded_function_QIntermediateShapeQAttention_test_attention_3d_with_past_and_present_expanded_function_QIntermediate"Reshape: ē K WAttention_test_attention_3d_with_past_and_present_expanded_function_KVIntermediateShapeQAttention_test_attention_3d_with_past_and_present_expanded_function_KIntermediate"Reshape: ē V WAttention_test_attention_3d_with_past_and_present_expanded_function_KVIntermediateShapeQAttention_test_attention_3d_with_past_and_present_expanded_function_VIntermediate"Reshape:  QAttention_test_attention_3d_with_past_and_present_expanded_function_QIntermediateMAttention_test_attention_3d_with_past_and_present_expanded_function_QReshaped" Transpose* perm@@@@ :  QAttention_test_attention_3d_with_past_and_present_expanded_function_KIntermediateMAttention_test_attention_3d_with_past_and_present_expanded_function_KReshaped" Transpose* perm@@@@ :  QAttention_test_attention_3d_with_past_and_present_expanded_function_VIntermediateMAttention_test_attention_3d_with_past_and_present_expanded_function_VReshaped" Transpose* perm@@@@ : Á MAttention_test_attention_3d_with_past_and_present_expanded_function_QReshapedMAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads"Shape* start * end :  MAttention_test_attention_3d_with_past_and_present_expanded_function_KReshapedNAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end :  MAttention_test_attention_3d_with_past_and_present_expanded_function_QReshapedNAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : ´ NAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSizeOAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : Á MAttention_test_attention_3d_with_past_and_present_expanded_function_VReshapedMAttention_test_attention_3d_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : Ģ OAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSizeFPAttention_test_attention_3d_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: lIAttention_test_attention_3d_with_past_and_present_expanded_function_One1D"Constant* value*: : pJAttention_test_attention_3d_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : mJAttention_test_attention_3d_with_past_and_present_expanded_function_Zero1D"Constant* value*: : ú JAttention_test_attention_3d_with_past_and_present_expanded_function_One1DF PAttention_test_attention_3d_with_past_and_present_expanded_function_SqrtHeadSizeSAttention_test_attention_3d_with_past_and_present_expanded_function_CalculatedScale"Div: nJAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : ˛ SAttention_test_attention_3d_with_past_and_present_expanded_function_CalculatedScaleOAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactor"Identity: Ž OAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorSAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt: ē SAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorSqrtPAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Ā past_key MAttention_test_attention_3d_with_past_and_present_expanded_function_KReshapedNAttention_test_attention_3d_with_past_and_present_expanded_function_PresentKey"Concat* axis : ‘ past_keyPAttention_test_attention_3d_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : i NAttention_test_attention_3d_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ä past_value MAttention_test_attention_3d_with_past_and_present_expanded_function_VReshapedPAttention_test_attention_3d_with_past_and_present_expanded_function_PresentValue"Concat* axis : m PAttention_test_attention_3d_with_past_and_present_expanded_function_PresentValue present_value"Identity: Ö NAttention_test_attention_3d_with_past_and_present_expanded_function_PresentKeyOAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˆ KAttention_test_attention_3d_with_past_and_present_expanded_function_QSeqLen OAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLenQAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : uOAttention_test_attention_3d_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : tNAttention_test_attention_3d_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : j attn_maskQAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasShort"Identity: ­ QAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasShortLAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBias"Identity: ŗ LAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasWAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: ģ WAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasCausalOrNotMAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ÷ MAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeadsMAttention_test_attention_3d_with_past_and_present_expanded_function_NGQACond1"Equal: ¤ MAttention_test_attention_3d_with_past_and_present_expanded_function_NGQACond1LAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond1"Not: ÷ MAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeadsOAttention_test_attention_3d_with_past_and_present_expanded_function_DivNumHeads"Div: ļ OAttention_test_attention_3d_with_past_and_present_expanded_function_DivNumHeadsPAttention_test_attention_3d_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ũ MAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeadsUAttention_test_attention_3d_with_past_and_present_expanded_function_RemainderNumHeads"Mod: ú UAttention_test_attention_3d_with_past_and_present_expanded_function_RemainderNumHeads JAttention_test_attention_3d_with_past_and_present_expanded_function_Zero1DLAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond2"Equal: đ LAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond1 LAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond2KAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond"And: Æ KAttention_test_attention_3d_with_past_and_present_expanded_function_GQACond PAttention_test_attention_3d_with_past_and_present_expanded_function_IDivNumHeads IAttention_test_attention_3d_with_past_and_present_expanded_function_One1DQAttention_test_attention_3d_with_past_and_present_expanded_function_InterleaveDim"Where: lIAttention_test_attention_3d_with_past_and_present_expanded_function_Two1D"Constant* value*: : ų NAttention_test_attention_3d_with_past_and_present_expanded_function_PresentKey IAttention_test_attention_3d_with_past_and_present_expanded_function_Two1DOAttention_test_attention_3d_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: û PAttention_test_attention_3d_with_past_and_present_expanded_function_PresentValue IAttention_test_attention_3d_with_past_and_present_expanded_function_Two1DOAttention_test_attention_3d_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ü MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize NAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeads QAttention_test_attention_3d_with_past_and_present_expanded_function_InterleaveDim OAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLen NAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSizePAttention_test_attention_3d_with_past_and_present_expanded_function_KExpandShape"Concat* axis : ü OAttention_test_attention_3d_with_past_and_present_expanded_function_KUnsqueezed PAttention_test_attention_3d_with_past_and_present_expanded_function_KExpandShapeMAttention_test_attention_3d_with_past_and_present_expanded_function_KExpanded"Expand: û MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize NAttention_test_attention_3d_with_past_and_present_expanded_function_KVNumHeads QAttention_test_attention_3d_with_past_and_present_expanded_function_InterleaveDim OAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLen MAttention_test_attention_3d_with_past_and_present_expanded_function_VHeadSizePAttention_test_attention_3d_with_past_and_present_expanded_function_VExpandShape"Concat* axis : ü OAttention_test_attention_3d_with_past_and_present_expanded_function_VUnsqueezed PAttention_test_attention_3d_with_past_and_present_expanded_function_VExpandShapeMAttention_test_attention_3d_with_past_and_present_expanded_function_VExpanded"Expand: Ģ MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize MAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads OAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLen NAttention_test_attention_3d_with_past_and_present_expanded_function_QKHeadSizeSAttention_test_attention_3d_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : Ē MAttention_test_attention_3d_with_past_and_present_expanded_function_BatchSize MAttention_test_attention_3d_with_past_and_present_expanded_function_QNumHeads OAttention_test_attention_3d_with_past_and_present_expanded_function_NewKVSeqLen MAttention_test_attention_3d_with_past_and_present_expanded_function_VHeadSizeSAttention_test_attention_3d_with_past_and_present_expanded_function_VAttentionShape"Concat* axis : „ MAttention_test_attention_3d_with_past_and_present_expanded_function_KExpanded SAttention_test_attention_3d_with_past_and_present_expanded_function_KAttentionShapeSAttention_test_attention_3d_with_past_and_present_expanded_function_KAttentionInput"Reshape: „ MAttention_test_attention_3d_with_past_and_present_expanded_function_VExpanded SAttention_test_attention_3d_with_past_and_present_expanded_function_VAttentionShapeSAttention_test_attention_3d_with_past_and_present_expanded_function_VAttentionInput"Reshape: Å SAttention_test_attention_3d_with_past_and_present_expanded_function_KAttentionInputNAttention_test_attention_3d_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ : õ MAttention_test_attention_3d_with_past_and_present_expanded_function_QReshaped PAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorFKAttention_test_attention_3d_with_past_and_present_expanded_function_QScaled"Mul: ö NAttention_test_attention_3d_with_past_and_present_expanded_function_KTranspose PAttention_test_attention_3d_with_past_and_present_expanded_function_ScaleFactorFKAttention_test_attention_3d_with_past_and_present_expanded_function_KScaled"Mul: ö KAttention_test_attention_3d_with_past_and_present_expanded_function_QScaled KAttention_test_attention_3d_with_past_and_present_expanded_function_KScaledPAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeight"MatMul: ĩ PAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeightNAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnCast"Cast* to : € NAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnCast MAttention_test_attention_3d_with_past_and_present_expanded_function_AttnBiasTXAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: ŋ XAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeightWithBiasWAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Ŋ WAttention_test_attention_3d_with_past_and_present_expanded_function_QKAttnWeightSoftcapOAttention_test_attention_3d_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : ŗ OAttention_test_attention_3d_with_past_and_present_expanded_function_SoftmaxCastUAttention_test_attention_3d_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax: ē UAttention_test_attention_3d_with_past_and_present_expanded_function_AttnWeightSoftmaxNAttention_test_attention_3d_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : € NAttention_test_attention_3d_with_past_and_present_expanded_function_SoftmaxOut SAttention_test_attention_3d_with_past_and_present_expanded_function_VAttentionInputOAttention_test_attention_3d_with_past_and_present_expanded_function_YPreReshape"MatMul: Á OAttention_test_attention_3d_with_past_and_present_expanded_function_YPreReshapeNAttention_test_attention_3d_with_past_and_present_expanded_function_YTranspose" Transpose* perm@@@@ : Ę JAttention_test_attention_3d_with_past_and_present_expanded_function_Zero1D JAttention_test_attention_3d_with_past_and_present_expanded_function_Zero1D JAttention_test_attention_3d_with_past_and_present_expanded_function_NegOneMAttention_test_attention_3d_with_past_and_present_expanded_function_YNewShape"Concat* axis : ­ NAttention_test_attention_3d_with_past_and_present_expanded_function_YTranspose MAttention_test_attention_3d_with_past_and_present_expanded_function_YNewShapeY"Reshape:0test_attention_3d_with_past_and_present_expandedZ Q    Z K    Z V    Z attn_mask   Z" past_key     Z$ past_value     b Y    b% present_key     b' present_value     B test_data_set_0/000077500000000000000000000000001511334557700362065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_expandedinput_0.pb000066400000000000000000000014161511334557700401110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022161511334557700401110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? 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ÜŊ?test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded/000077500000000000000000000000001511334557700367355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000500251511334557700407430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded  backend-test:ûŸ ‡ Q_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize"Shape* start * end : — Q]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˜ K^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : †cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeadsAttr"Constant* value*: : ‡dAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeadsAttr"Constant* value*: : ˆ\Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : „ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QSeqLen cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeadsAttr \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NegOnehAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QIntermediateShape"Concat* axis : ‡ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVSeqLen dAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeadsAttr \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NegOneiAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVIntermediateShape"Concat* axis : Ũ Q hAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QIntermediateShapecAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QIntermediate"Reshape: Ū K iAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVIntermediateShapecAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KIntermediate"Reshape: Ū V iAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVIntermediateShapecAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VIntermediate"Reshape: æ cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QIntermediate_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QReshaped" Transpose* perm@@@@ : æ cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KIntermediate_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KReshaped" Transpose* perm@@@@ : æ cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VIntermediate_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VReshaped" Transpose* perm@@@@ : å _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QReshaped_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads"Shape* start * end : æ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KReshaped`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeads"Shape* start * end : æ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QReshaped`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSize"Shape* start * end : Ø `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSizeaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSizeF"Cast* to : å _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VReshaped_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VHeadSize"Shape* start * end : Ī aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSizeFbAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SqrtHeadSize"Sqrt: ~[Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_One1D"Constant* value*: : ‚\Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_One1DF"Constant* value* "€? : \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Zero1D"Constant* value*: : ° \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_One1DF bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SqrtHeadSizeeAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_CalculatedScale"Div: €\Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleF"Constant* value*"€? : Ö eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_CalculatedScaleaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactor"Identity: Ō aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactoreAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactorSqrt"Sqrt: Ū eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactorSqrtbAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactorF"Cast* to : ä past_key _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KReshaped`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentKey"Concat* axis : Ŗ past_keybAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentKey present_key"Identity: č past_value _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VReshapedbAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentValue"Concat* axis :  bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentValue present_value"Identity: ú `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentKeyaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ž ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QSeqLen aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLencAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasShape"Concat* axis : ‡aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : †`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScalarZero"Constant* value* " : | attn_maskcAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasShort"Identity: Ņ cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasShort^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBias"Identity: × ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasiAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasCausalOrNot"Identity: ß iAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasCausalOrNot_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasT"Cast* to : ­ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeads_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NGQACond1"Equal: Č _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NGQACond1^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond1"Not: ­ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeadsaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_DivNumHeads"Div: Ú aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_DivNumHeadsbAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_IDivNumHeads"Cast* to : ŗ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeadsgAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_RemainderNumHeads"Mod: ° gAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_RemainderNumHeads \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Zero1D^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond2"Equal: Ļ ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond1 ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond2]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond"And: Ž ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_GQACond bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_IDivNumHeads [Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_One1DcAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_InterleaveDim"Where: ~[Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Two1D"Constant* value*: : ¯ `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentKey [Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Two1DaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KUnsqueezed" Unsqueeze: ą bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_PresentValue [Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Two1DaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VUnsqueezed" Unsqueeze: č _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeads cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_InterleaveDim aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLen `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSizebAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KExpandShape"Concat* axis : ˛ aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KUnsqueezed bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KExpandShape_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KExpanded"Expand: į _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KVNumHeads cAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_InterleaveDim aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLen _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VHeadSizebAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VExpandShape"Concat* axis : ˛ aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VUnsqueezed bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VExpandShape_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VExpanded"Expand: … _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLen `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKHeadSizeeAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KAttentionShape"Concat* axis : „ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_BatchSize _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QNumHeads aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NewKVSeqLen _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VHeadSizeeAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VAttentionShape"Concat* axis : ē _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KExpanded eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KAttentionShapeeAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KAttentionInput"Reshape: ē _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VExpanded eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VAttentionShapeeAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VAttentionInput"Reshape: é eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KAttentionInput`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : Ģ _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QReshaped bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactorF]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QScaled"Mul: Ŧ `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KTranspose bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_ScaleFactorF]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KScaled"Mul: Ŧ ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QScaled ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_KScaledbAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeight"MatMul: Ų bAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeight`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnCast"Cast* to : ļ `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnCast _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnBiasTjAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeightWithBias"Add: ƒ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Softcap"Constant* value* "@ : Ō ]Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Softcap^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapF"Cast* to : ĩ jAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeightWithBias ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapF`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapDiv"Div: Í `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapDivaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapTanh"Tanh: ĩ aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapTanh ^Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftcapFiAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcap"Mul: á iAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcapaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftmaxCast"Cast* to : × aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftmaxCastgAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnWeightSoftmax"Softmax: Ū gAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_AttnWeightSoftmax`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftmaxOut"Cast* to : ‰ iAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcapqk_matmul_output"Identity: ļ `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_SoftmaxOut eAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_VAttentionInputaAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YPreReshape"MatMul: å aAttention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YPreReshape`Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YTranspose" Transpose* perm@@@@ : ’ \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Zero1D \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_Zero1D \Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_NegOne_Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YNewShape"Concat* axis : Ņ `Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YTranspose _Attention_test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded_function_YNewShapeY"Reshape:Btest_attention_3d_with_past_and_present_qk_matmul_softcap_expandedZ Q    Z K    Z V    Z attn_mask   Z" past_key     Z$ past_value     b Y    b% present_key     b' present_value     b* qk_matmul_output     B test_data_set_0/000077500000000000000000000000001511334557700417775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_qk_matmul_softcap_expandedinput_0.pb000066400000000000000000000014161511334557700437020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_3d_with_past_and_present_qk_matmul_softcap_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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?^Ûũ>^É?ž*ĩ>ˇ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_causal_expanded/000077500000000000000000000000001511334557700325375ustar00rootroot00000000000000model.onnx000066400000000000000000000372521511334557700344750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_causal_expanded  backend-test:‘} s QKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BatchSize"Shape* start * end : ƒ QIAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : „ KJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : \ QKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QReshaped"Identity: \ KKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KReshaped"Identity: \ VKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VReshaped"Identity: Ŋ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QReshapedKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads"Shape* start * end : ž KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KReshapedLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeads"Shape* start * end : ž KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QReshapedLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSize"Shape* start * end : ° LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSizeMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSizeF"Cast* to : Ŋ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VReshapedKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VHeadSize"Shape* start * end : § MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSizeFNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SqrtHeadSize"Sqrt: jGAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One1D"Constant* value*: : nHAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One1DF"Constant* value* "€? : kHAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Zero1D"Constant* value*: : ô HAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One1DF NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SqrtHeadSizeQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_CalculatedScale"Div: lHAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleF"Constant* value*"€? : Ž QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_CalculatedScaleMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactor"Identity: Ē MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorSqrt"Sqrt: ļ QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorSqrtNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorF"Cast* to : § KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KReshapedLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PresentKey"Identity: qNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PastKVSeqLen"Constant* value*: : Š KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VReshapedNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PresentValue"Identity: Ō LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PresentKeyMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‚ IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QSeqLen MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLenOAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasShape"Concat* axis : sMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : rLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScalarZero"Constant* value* " : h attn_maskOAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasShort"Identity: Š OAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasShortJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBias"Identity: iFAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Zero"Constant* value*: : hEAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One"Constant* value*: : č FAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Zero FAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroNoDim"Squeeze: æ EAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One FAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_OneNoDim"Squeeze: ú OAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasShape KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroNoDimPAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SequenceLength"Gather: ū OAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasShape JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_OneNoDimUAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_TotalSequenceLength"Gather: Ā KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroNoDim PAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SequenceLength JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_OneNoDimJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow"Range: î JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow EAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_OneLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow2D" Unsqueeze: Å KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroNoDim UAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_TotalSequenceLength JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_OneNoDimJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeCol"Range: ī JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeCol FAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ZeroLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeCol2D" Unsqueeze: ÷ LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow2D NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PastKVSeqLenPAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow2DPast"Add: ÷ PAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeRow2DPast LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RangeCol2DMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BoolMaskTri"Less: Ā MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BoolMaskTri MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_FloatNegInf LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScalarZeroIAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_MaskTri"Where: õ JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBias IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_MaskTriUAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasCausalOrNot"Add: ˇ UAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasCausalOrNotKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasT"Cast* to : ņ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeadsKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NGQACond1"Equal:   KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NGQACond1JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond1"Not: ņ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeadsMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_DivNumHeads"Div: ˛ MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_DivNumHeadsNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_IDivNumHeads"Cast* to : ÷ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeadsSAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RemainderNumHeads"Mod: ô SAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_RemainderNumHeads HAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Zero1DJAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond2"Equal: ę JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond1 JAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond2IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond"And: ž IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_GQACond NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_IDivNumHeads GAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_One1DOAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_InterleaveDim"Where: jGAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Two1D"Constant* value*: : ķ LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PresentKey GAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Two1DMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KUnsqueezed" Unsqueeze: õ NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_PresentValue GAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_Two1DMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VUnsqueezed" Unsqueeze: đ KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BatchSize LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeads OAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_InterleaveDim MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLen LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSizeNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KExpandShape"Concat* axis : ö MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KUnsqueezed NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KExpandShapeKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KExpanded"Expand: ī KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BatchSize LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KVNumHeads OAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_InterleaveDim MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLen KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VHeadSizeNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VExpandShape"Concat* axis : ö MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VUnsqueezed NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VExpandShapeKAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VExpanded"Expand: Ą KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BatchSize KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLen LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKHeadSizeQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KAttentionShape"Concat* axis :   KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_BatchSize KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QNumHeads MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_NewKVSeqLen KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VHeadSizeQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VAttentionShape"Concat* axis : ū KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KExpanded QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KAttentionShapeQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KAttentionInput"Reshape: ū KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VExpanded QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VAttentionShapeQAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VAttentionInput"Reshape: Á QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KAttentionInputLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KTranspose" Transpose* perm@@@@ : ī KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QReshaped NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorFIAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QScaled"Mul: đ LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KTranspose NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_ScaleFactorFIAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KScaled"Mul: đ IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QScaled IAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_KScaledNAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeight"MatMul: ą NAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeightLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnCast"Cast* to : ú LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnCast KAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnBiasTVAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeightWithBias"Add: ģ VAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeightWithBiasUAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeightSoftcap"Identity: š UAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_QKAttnWeightSoftcapMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SoftmaxCast"Cast* to : ¯ MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SoftmaxCastSAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnWeightSoftmax"Softmax: ļ SAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_AttnWeightSoftmaxLAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SoftmaxOut"Cast* to : ú LAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_SoftmaxOut QAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_VAttentionInputMAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_YPreReshape"MatMul: ^ MAttention_test_attention_4d_attn_mask_3d_causal_expanded_function_YPreReshapeY"Identity:.test_attention_4d_attn_mask_3d_causal_expandedZ Q     Z K     Z V     Z# attn_mask     b Y     B test_data_set_0/000077500000000000000000000000001511334557700355225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_causal_expandedinput_0.pb000066400000000000000000000014201511334557700374200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_causal_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700374200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_causal_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? 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?^Ûũ>^É?ž*ĩ>ˇ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_expanded/000077500000000000000000000000001511334557700312075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_3d_expanded/model.onnx000066400000000000000000000270551511334557700332240ustar00rootroot00000000000000  backend-test:”\ l QDAttention_test_attention_4d_attn_mask_3d_expanded_function_BatchSize"Shape* start * end : | QBAttention_test_attention_4d_attn_mask_3d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : } KCAttention_test_attention_4d_attn_mask_3d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : U QDAttention_test_attention_4d_attn_mask_3d_expanded_function_QReshaped"Identity: U KDAttention_test_attention_4d_attn_mask_3d_expanded_function_KReshaped"Identity: U VDAttention_test_attention_4d_attn_mask_3d_expanded_function_VReshaped"Identity: ¯ DAttention_test_attention_4d_attn_mask_3d_expanded_function_QReshapedDAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads"Shape* start * end : ° DAttention_test_attention_4d_attn_mask_3d_expanded_function_KReshapedEAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeads"Shape* start * end : ° DAttention_test_attention_4d_attn_mask_3d_expanded_function_QReshapedEAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSize"Shape* start * end : ĸ EAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSizeFAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSizeF"Cast* to : ¯ DAttention_test_attention_4d_attn_mask_3d_expanded_function_VReshapedDAttention_test_attention_4d_attn_mask_3d_expanded_function_VHeadSize"Shape* start * end : ™ FAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSizeFGAttention_test_attention_4d_attn_mask_3d_expanded_function_SqrtHeadSize"Sqrt: c@Attention_test_attention_4d_attn_mask_3d_expanded_function_One1D"Constant* value*: : gAAttention_test_attention_4d_attn_mask_3d_expanded_function_One1DF"Constant* value* "€? : dAAttention_test_attention_4d_attn_mask_3d_expanded_function_Zero1D"Constant* value*: : ß AAttention_test_attention_4d_attn_mask_3d_expanded_function_One1DF GAttention_test_attention_4d_attn_mask_3d_expanded_function_SqrtHeadSizeJAttention_test_attention_4d_attn_mask_3d_expanded_function_CalculatedScale"Div: eAAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleF"Constant* value*"€? :   JAttention_test_attention_4d_attn_mask_3d_expanded_function_CalculatedScaleFAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactor"Identity: œ FAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorJAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorSqrt"Sqrt: ¨ JAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorSqrtGAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorF"Cast* to : ™ DAttention_test_attention_4d_attn_mask_3d_expanded_function_KReshapedEAttention_test_attention_4d_attn_mask_3d_expanded_function_PresentKey"Identity: jGAttention_test_attention_4d_attn_mask_3d_expanded_function_PastKVSeqLen"Constant* value*: : › DAttention_test_attention_4d_attn_mask_3d_expanded_function_VReshapedGAttention_test_attention_4d_attn_mask_3d_expanded_function_PresentValue"Identity: Ä EAttention_test_attention_4d_attn_mask_3d_expanded_function_PresentKeyFAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : í BAttention_test_attention_4d_attn_mask_3d_expanded_function_QSeqLen FAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLenHAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasShape"Concat* axis : lFAttention_test_attention_4d_attn_mask_3d_expanded_function_FloatNegInf"Constant* value* "€˙ : kEAttention_test_attention_4d_attn_mask_3d_expanded_function_ScalarZero"Constant* value* " : a attn_maskHAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasShort"Identity: › HAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasShortCAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBias"Identity: Ą CAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasNAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasCausalOrNot"Identity: Š NAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasCausalOrNotDAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasT"Cast* to : Ü DAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeadsDAttention_test_attention_4d_attn_mask_3d_expanded_function_NGQACond1"Equal: ’ DAttention_test_attention_4d_attn_mask_3d_expanded_function_NGQACond1CAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond1"Not: Ü DAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeadsFAttention_test_attention_4d_attn_mask_3d_expanded_function_DivNumHeads"Div: ¤ FAttention_test_attention_4d_attn_mask_3d_expanded_function_DivNumHeadsGAttention_test_attention_4d_attn_mask_3d_expanded_function_IDivNumHeads"Cast* to : â DAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeadsLAttention_test_attention_4d_attn_mask_3d_expanded_function_RemainderNumHeads"Mod: ß LAttention_test_attention_4d_attn_mask_3d_expanded_function_RemainderNumHeads AAttention_test_attention_4d_attn_mask_3d_expanded_function_Zero1DCAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond2"Equal: Õ CAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond1 CAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond2BAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond"And: ĸ BAttention_test_attention_4d_attn_mask_3d_expanded_function_GQACond GAttention_test_attention_4d_attn_mask_3d_expanded_function_IDivNumHeads @Attention_test_attention_4d_attn_mask_3d_expanded_function_One1DHAttention_test_attention_4d_attn_mask_3d_expanded_function_InterleaveDim"Where: c@Attention_test_attention_4d_attn_mask_3d_expanded_function_Two1D"Constant* value*: : Ū EAttention_test_attention_4d_attn_mask_3d_expanded_function_PresentKey @Attention_test_attention_4d_attn_mask_3d_expanded_function_Two1DFAttention_test_attention_4d_attn_mask_3d_expanded_function_KUnsqueezed" Unsqueeze: ā GAttention_test_attention_4d_attn_mask_3d_expanded_function_PresentValue @Attention_test_attention_4d_attn_mask_3d_expanded_function_Two1DFAttention_test_attention_4d_attn_mask_3d_expanded_function_VUnsqueezed" Unsqueeze: Æ DAttention_test_attention_4d_attn_mask_3d_expanded_function_BatchSize EAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeads HAttention_test_attention_4d_attn_mask_3d_expanded_function_InterleaveDim FAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLen EAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSizeGAttention_test_attention_4d_attn_mask_3d_expanded_function_KExpandShape"Concat* axis : á FAttention_test_attention_4d_attn_mask_3d_expanded_function_KUnsqueezed GAttention_test_attention_4d_attn_mask_3d_expanded_function_KExpandShapeDAttention_test_attention_4d_attn_mask_3d_expanded_function_KExpanded"Expand: Å DAttention_test_attention_4d_attn_mask_3d_expanded_function_BatchSize EAttention_test_attention_4d_attn_mask_3d_expanded_function_KVNumHeads HAttention_test_attention_4d_attn_mask_3d_expanded_function_InterleaveDim FAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLen DAttention_test_attention_4d_attn_mask_3d_expanded_function_VHeadSizeGAttention_test_attention_4d_attn_mask_3d_expanded_function_VExpandShape"Concat* axis : á FAttention_test_attention_4d_attn_mask_3d_expanded_function_VUnsqueezed GAttention_test_attention_4d_attn_mask_3d_expanded_function_VExpandShapeDAttention_test_attention_4d_attn_mask_3d_expanded_function_VExpanded"Expand: ū DAttention_test_attention_4d_attn_mask_3d_expanded_function_BatchSize DAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads FAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLen EAttention_test_attention_4d_attn_mask_3d_expanded_function_QKHeadSizeJAttention_test_attention_4d_attn_mask_3d_expanded_function_KAttentionShape"Concat* axis : ũ DAttention_test_attention_4d_attn_mask_3d_expanded_function_BatchSize DAttention_test_attention_4d_attn_mask_3d_expanded_function_QNumHeads FAttention_test_attention_4d_attn_mask_3d_expanded_function_NewKVSeqLen DAttention_test_attention_4d_attn_mask_3d_expanded_function_VHeadSizeJAttention_test_attention_4d_attn_mask_3d_expanded_function_VAttentionShape"Concat* axis : é DAttention_test_attention_4d_attn_mask_3d_expanded_function_KExpanded JAttention_test_attention_4d_attn_mask_3d_expanded_function_KAttentionShapeJAttention_test_attention_4d_attn_mask_3d_expanded_function_KAttentionInput"Reshape: é DAttention_test_attention_4d_attn_mask_3d_expanded_function_VExpanded JAttention_test_attention_4d_attn_mask_3d_expanded_function_VAttentionShapeJAttention_test_attention_4d_attn_mask_3d_expanded_function_VAttentionInput"Reshape: ŗ JAttention_test_attention_4d_attn_mask_3d_expanded_function_KAttentionInputEAttention_test_attention_4d_attn_mask_3d_expanded_function_KTranspose" Transpose* perm@@@@ : Ú DAttention_test_attention_4d_attn_mask_3d_expanded_function_QReshaped GAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorFBAttention_test_attention_4d_attn_mask_3d_expanded_function_QScaled"Mul: Û EAttention_test_attention_4d_attn_mask_3d_expanded_function_KTranspose GAttention_test_attention_4d_attn_mask_3d_expanded_function_ScaleFactorFBAttention_test_attention_4d_attn_mask_3d_expanded_function_KScaled"Mul: Û BAttention_test_attention_4d_attn_mask_3d_expanded_function_QScaled BAttention_test_attention_4d_attn_mask_3d_expanded_function_KScaledGAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeight"MatMul: Ŗ GAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeightEAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnCast"Cast* to : å EAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnCast DAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnBiasTOAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeightWithBias"Add: ­ OAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeightWithBiasNAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeightSoftcap"Identity: Ģ NAttention_test_attention_4d_attn_mask_3d_expanded_function_QKAttnWeightSoftcapFAttention_test_attention_4d_attn_mask_3d_expanded_function_SoftmaxCast"Cast* to : Ą FAttention_test_attention_4d_attn_mask_3d_expanded_function_SoftmaxCastLAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnWeightSoftmax"Softmax: ¨ LAttention_test_attention_4d_attn_mask_3d_expanded_function_AttnWeightSoftmaxEAttention_test_attention_4d_attn_mask_3d_expanded_function_SoftmaxOut"Cast* to : å EAttention_test_attention_4d_attn_mask_3d_expanded_function_SoftmaxOut 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?ÜT"?ņeI?ãL?‡î>?oü? Œ>^ĩ>%×&?ŌlÔ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī>-°?Ü' >õã?FŊ,?øÂ?gų ?đŌ…>Sí>ņ=?^Æ>@ĩ*?Vdļ>[Cû>l€ø>Đč>ė{ß>á ?Į>>Ēō?Q@ņ>WĮŪ>¸Ųī>V}Û>õđÔ>ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?¸VA?ąū#? ķ‰>•”!?GČÁ>–X?đo?˙čT?Ád"?.Á#?íHT>“Ũ ?…°—>š‘2?¤ č>Kĩ.?Ķ+?_Īî>]ŋ>@­ū>Q6Ō>ųŒ'?R#˛>=T?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded/000077500000000000000000000000001511334557700325405ustar00rootroot00000000000000model.onnx000066400000000000000000000372521511334557700344760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded  backend-test:‘} s QKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BatchSize"Shape* start * end : ƒ QIAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : „ KJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : \ QKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QReshaped"Identity: \ KKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KReshaped"Identity: \ VKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VReshaped"Identity: Ŋ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QReshapedKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads"Shape* start * end : ž KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KReshapedLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeads"Shape* start * end : ž KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QReshapedLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSize"Shape* start * end : ° LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSizeMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSizeF"Cast* to : Ŋ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VReshapedKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VHeadSize"Shape* start * end : § MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSizeFNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SqrtHeadSize"Sqrt: jGAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One1D"Constant* value*: : nHAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One1DF"Constant* value* "€? : kHAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Zero1D"Constant* value*: : ô HAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One1DF NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SqrtHeadSizeQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_CalculatedScale"Div: lHAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleF"Constant* value*"€? : Ž QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_CalculatedScaleMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactor"Identity: Ē MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorSqrt"Sqrt: ļ QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorSqrtNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorF"Cast* to : § KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KReshapedLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PresentKey"Identity: qNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PastKVSeqLen"Constant* value*: : Š KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VReshapedNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PresentValue"Identity: Ō LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PresentKeyMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‚ IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QSeqLen MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLenOAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasShape"Concat* axis : sMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : rLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScalarZero"Constant* value* " : h attn_maskOAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasShort"Identity: Š OAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasShortJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBias"Identity: iFAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Zero"Constant* value*: : hEAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One"Constant* value*: : č FAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Zero FAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroNoDim"Squeeze: æ EAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One FAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_OneNoDim"Squeeze: ú OAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasShape KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroNoDimPAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SequenceLength"Gather: ū OAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasShape JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_OneNoDimUAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_TotalSequenceLength"Gather: Ā KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroNoDim PAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SequenceLength JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_OneNoDimJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow"Range: î JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow EAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_OneLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow2D" Unsqueeze: Å KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroNoDim UAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_TotalSequenceLength JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_OneNoDimJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeCol"Range: ī JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeCol FAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ZeroLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeCol2D" Unsqueeze: ÷ LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow2D NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PastKVSeqLenPAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow2DPast"Add: ÷ PAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeRow2DPast LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RangeCol2DMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BoolMaskTri"Less: Ā MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BoolMaskTri MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_FloatNegInf LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScalarZeroIAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_MaskTri"Where: õ JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBias IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_MaskTriUAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasCausalOrNot"Add: ˇ UAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasCausalOrNotKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasT"Cast* to : ņ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeadsKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NGQACond1"Equal:   KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NGQACond1JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond1"Not: ņ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeadsMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_DivNumHeads"Div: ˛ MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_DivNumHeadsNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_IDivNumHeads"Cast* to : ÷ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeadsSAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RemainderNumHeads"Mod: ô SAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_RemainderNumHeads HAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Zero1DJAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond2"Equal: ę JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond1 JAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond2IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond"And: ž IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_GQACond NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_IDivNumHeads GAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_One1DOAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_InterleaveDim"Where: jGAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Two1D"Constant* value*: : ķ LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PresentKey GAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Two1DMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KUnsqueezed" Unsqueeze: õ NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_PresentValue GAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_Two1DMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VUnsqueezed" Unsqueeze: đ KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BatchSize LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeads OAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_InterleaveDim MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLen LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSizeNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KExpandShape"Concat* axis : ö MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KUnsqueezed NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KExpandShapeKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KExpanded"Expand: ī KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BatchSize LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KVNumHeads OAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_InterleaveDim MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLen KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VHeadSizeNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VExpandShape"Concat* axis : ö MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VUnsqueezed NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VExpandShapeKAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VExpanded"Expand: Ą KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BatchSize KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLen LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKHeadSizeQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KAttentionShape"Concat* axis :   KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_BatchSize KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QNumHeads MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_NewKVSeqLen KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VHeadSizeQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VAttentionShape"Concat* axis : ū KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KExpanded QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KAttentionShapeQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KAttentionInput"Reshape: ū KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VExpanded QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VAttentionShapeQAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VAttentionInput"Reshape: Á QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KAttentionInputLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KTranspose" Transpose* perm@@@@ : ī KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QReshaped NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorFIAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QScaled"Mul: đ LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KTranspose NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_ScaleFactorFIAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KScaled"Mul: đ IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QScaled IAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_KScaledNAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeight"MatMul: ą NAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeightLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnCast"Cast* to : ú LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnCast KAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnBiasTVAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeightWithBias"Add: ģ VAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeightWithBiasUAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeightSoftcap"Identity: š UAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_QKAttnWeightSoftcapMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SoftmaxCast"Cast* to : ¯ MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SoftmaxCastSAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnWeightSoftmax"Softmax: ļ SAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_AttnWeightSoftmaxLAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SoftmaxOut"Cast* to : ú LAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_SoftmaxOut QAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_VAttentionInputMAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_YPreReshape"MatMul: ^ MAttention_test_attention_4d_attn_mask_4d_causal_expanded_function_YPreReshapeY"Identity:.test_attention_4d_attn_mask_4d_causal_expandedZ Q     Z K     Z V     Z# attn_mask     b Y     B test_data_set_0/000077500000000000000000000000001511334557700355235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expandedinput_0.pb000066400000000000000000000014201511334557700374210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700374210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>input_2.pb000066400000000000000000000022201511334557700374220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded/test_data_set_0BVJ€ -$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?g(1;ŽŽ%?Nŗ?¤ˇ?xv?w6Š<ŦL2?>MP?š‚?rũĒ>€tJ?M'Į=Râ>™?!§1?L"ē=É9i> Ō>=?Ũc?dk? 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?iÖ>)1>O‘Û=#=Q?Ė?ō>XŨa?Ö¸;?ĸĮŅ>Ö<ŋ>ā?o™c?Jž°÷>¯>;Ρ>įæo?Ŋ]l?ūΐ>ä­>§?”v?”Šƒ>lĄ_?LŲû>P"f?gø=>ø\?Đ §>Ūĸ>Íä>TŧŨ>,öļ>†;j?–S;?…@:?‰o”>Ää?NxG?ĪĢK?Lf°>ëWE?‹cšŽ]?ãôá>Ę ų>Ĩå>[^?ķ?d˙>Ûå]?:ŗ ?÷‡Í>ŸXÕ>“O?7F˛>›‡X>É;s=LC`?Ü%k?’ö=$@Ģ>ŧ”3>-\í=Ģ]f?!øh=output_0.pb000066400000000000000000000014201511334557700376220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_causal_expanded/test_data_set_0BYJ€-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?qbŦ>[wŧ>“-A>ÆDđ>i_ˇ>ē­ ?ÉŽŨ>nå5?4û÷>ŨßŅ>B‹n>rh?2Í>Û&Ũ>`ŗ˙>^-?'âŌ>4 ?úĘ>xŅŋ>Žl˛>ēé>¯Â>™?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=Ģžŧ>R?DĘ>0?j‰>úÆú>ŠâŨ>žáÅ>¤ ?-‡¸>æhō>ãá4?éÃ>ÛÅÕ>Å ?ī†>PKū>Ŗ€é>.W?"-ų>.ģ—>ZŨ>ŧs?īo?ĄQ$>Ũäb?Īä>‰jh?u$>˙>)?@já>Ĩœ=d­Č>įA?Ŧ›Š>†Ģ ?‡tđ=õ‚D?GÕ?gĶÜ>ĶVī>úb?K˜”>ņ×3?E„H>q+?Öā?Ht?Š1í>py?rP >Žá?fAv>&ú)?`ū>Øũ?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>ķÔ5?œĀ5?aËD?[S?’Qj>ÛÜ>˛¤>qäų>ŋ2?rC?^?Cč*?a‰>Ev>† ?ÜT"?ņeI?ãL?‡î>?oü? Œ>^ĩ>%×&?ŌlÔ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī>-°?Ü' >õã?FŊ,?øÂ?gų ?đŌ…>Sí>ņ=?^Æ>@ĩ*?Vdļ>[Cû>l€ø>Đč>ė{ß>á ?Į>>Ēō?Q@ņ>WĮŪ>¸Ųī>V}Û>õđÔ>ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?¸VA?ąū#? ķ‰>•”!?GČÁ>–X?đo?˙čT?Ád"?.Á#?íHT>“Ũ ?…°—>š‘2?¤ č>Kĩ.?Ķ+?_Īî>]ŋ>@­ū>Q6Ō>ųŒ'?R#˛>=T?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_expanded/000077500000000000000000000000001511334557700312105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_4d_expanded/model.onnx000066400000000000000000000270551511334557700332250ustar00rootroot00000000000000  backend-test:”\ l QDAttention_test_attention_4d_attn_mask_4d_expanded_function_BatchSize"Shape* start * end : | QBAttention_test_attention_4d_attn_mask_4d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : } KCAttention_test_attention_4d_attn_mask_4d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : U QDAttention_test_attention_4d_attn_mask_4d_expanded_function_QReshaped"Identity: U KDAttention_test_attention_4d_attn_mask_4d_expanded_function_KReshaped"Identity: U VDAttention_test_attention_4d_attn_mask_4d_expanded_function_VReshaped"Identity: ¯ DAttention_test_attention_4d_attn_mask_4d_expanded_function_QReshapedDAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads"Shape* start * end : ° DAttention_test_attention_4d_attn_mask_4d_expanded_function_KReshapedEAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeads"Shape* start * end : ° DAttention_test_attention_4d_attn_mask_4d_expanded_function_QReshapedEAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSize"Shape* start * end : ĸ EAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSizeFAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSizeF"Cast* to : ¯ DAttention_test_attention_4d_attn_mask_4d_expanded_function_VReshapedDAttention_test_attention_4d_attn_mask_4d_expanded_function_VHeadSize"Shape* start * end : ™ FAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSizeFGAttention_test_attention_4d_attn_mask_4d_expanded_function_SqrtHeadSize"Sqrt: c@Attention_test_attention_4d_attn_mask_4d_expanded_function_One1D"Constant* value*: : gAAttention_test_attention_4d_attn_mask_4d_expanded_function_One1DF"Constant* value* "€? : dAAttention_test_attention_4d_attn_mask_4d_expanded_function_Zero1D"Constant* value*: : ß AAttention_test_attention_4d_attn_mask_4d_expanded_function_One1DF GAttention_test_attention_4d_attn_mask_4d_expanded_function_SqrtHeadSizeJAttention_test_attention_4d_attn_mask_4d_expanded_function_CalculatedScale"Div: eAAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleF"Constant* value*"€? :   JAttention_test_attention_4d_attn_mask_4d_expanded_function_CalculatedScaleFAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactor"Identity: œ FAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorJAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorSqrt"Sqrt: ¨ JAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorSqrtGAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorF"Cast* to : ™ DAttention_test_attention_4d_attn_mask_4d_expanded_function_KReshapedEAttention_test_attention_4d_attn_mask_4d_expanded_function_PresentKey"Identity: jGAttention_test_attention_4d_attn_mask_4d_expanded_function_PastKVSeqLen"Constant* value*: : › DAttention_test_attention_4d_attn_mask_4d_expanded_function_VReshapedGAttention_test_attention_4d_attn_mask_4d_expanded_function_PresentValue"Identity: Ä EAttention_test_attention_4d_attn_mask_4d_expanded_function_PresentKeyFAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : í BAttention_test_attention_4d_attn_mask_4d_expanded_function_QSeqLen FAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLenHAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasShape"Concat* axis : lFAttention_test_attention_4d_attn_mask_4d_expanded_function_FloatNegInf"Constant* value* "€˙ : kEAttention_test_attention_4d_attn_mask_4d_expanded_function_ScalarZero"Constant* value* " : a attn_maskHAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasShort"Identity: › HAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasShortCAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBias"Identity: Ą CAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasNAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasCausalOrNot"Identity: Š NAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasCausalOrNotDAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasT"Cast* to : Ü DAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeadsDAttention_test_attention_4d_attn_mask_4d_expanded_function_NGQACond1"Equal: ’ DAttention_test_attention_4d_attn_mask_4d_expanded_function_NGQACond1CAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond1"Not: Ü DAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeadsFAttention_test_attention_4d_attn_mask_4d_expanded_function_DivNumHeads"Div: ¤ FAttention_test_attention_4d_attn_mask_4d_expanded_function_DivNumHeadsGAttention_test_attention_4d_attn_mask_4d_expanded_function_IDivNumHeads"Cast* to : â DAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads EAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeadsLAttention_test_attention_4d_attn_mask_4d_expanded_function_RemainderNumHeads"Mod: ß LAttention_test_attention_4d_attn_mask_4d_expanded_function_RemainderNumHeads AAttention_test_attention_4d_attn_mask_4d_expanded_function_Zero1DCAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond2"Equal: Õ CAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond1 CAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond2BAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond"And: ĸ BAttention_test_attention_4d_attn_mask_4d_expanded_function_GQACond GAttention_test_attention_4d_attn_mask_4d_expanded_function_IDivNumHeads @Attention_test_attention_4d_attn_mask_4d_expanded_function_One1DHAttention_test_attention_4d_attn_mask_4d_expanded_function_InterleaveDim"Where: c@Attention_test_attention_4d_attn_mask_4d_expanded_function_Two1D"Constant* value*: : Ū EAttention_test_attention_4d_attn_mask_4d_expanded_function_PresentKey @Attention_test_attention_4d_attn_mask_4d_expanded_function_Two1DFAttention_test_attention_4d_attn_mask_4d_expanded_function_KUnsqueezed" Unsqueeze: ā GAttention_test_attention_4d_attn_mask_4d_expanded_function_PresentValue @Attention_test_attention_4d_attn_mask_4d_expanded_function_Two1DFAttention_test_attention_4d_attn_mask_4d_expanded_function_VUnsqueezed" Unsqueeze: Æ DAttention_test_attention_4d_attn_mask_4d_expanded_function_BatchSize EAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeads HAttention_test_attention_4d_attn_mask_4d_expanded_function_InterleaveDim FAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLen EAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSizeGAttention_test_attention_4d_attn_mask_4d_expanded_function_KExpandShape"Concat* axis : á FAttention_test_attention_4d_attn_mask_4d_expanded_function_KUnsqueezed GAttention_test_attention_4d_attn_mask_4d_expanded_function_KExpandShapeDAttention_test_attention_4d_attn_mask_4d_expanded_function_KExpanded"Expand: Å DAttention_test_attention_4d_attn_mask_4d_expanded_function_BatchSize EAttention_test_attention_4d_attn_mask_4d_expanded_function_KVNumHeads HAttention_test_attention_4d_attn_mask_4d_expanded_function_InterleaveDim FAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLen DAttention_test_attention_4d_attn_mask_4d_expanded_function_VHeadSizeGAttention_test_attention_4d_attn_mask_4d_expanded_function_VExpandShape"Concat* axis : á FAttention_test_attention_4d_attn_mask_4d_expanded_function_VUnsqueezed GAttention_test_attention_4d_attn_mask_4d_expanded_function_VExpandShapeDAttention_test_attention_4d_attn_mask_4d_expanded_function_VExpanded"Expand: ū DAttention_test_attention_4d_attn_mask_4d_expanded_function_BatchSize DAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads FAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLen EAttention_test_attention_4d_attn_mask_4d_expanded_function_QKHeadSizeJAttention_test_attention_4d_attn_mask_4d_expanded_function_KAttentionShape"Concat* axis : ũ DAttention_test_attention_4d_attn_mask_4d_expanded_function_BatchSize DAttention_test_attention_4d_attn_mask_4d_expanded_function_QNumHeads FAttention_test_attention_4d_attn_mask_4d_expanded_function_NewKVSeqLen DAttention_test_attention_4d_attn_mask_4d_expanded_function_VHeadSizeJAttention_test_attention_4d_attn_mask_4d_expanded_function_VAttentionShape"Concat* axis : é DAttention_test_attention_4d_attn_mask_4d_expanded_function_KExpanded JAttention_test_attention_4d_attn_mask_4d_expanded_function_KAttentionShapeJAttention_test_attention_4d_attn_mask_4d_expanded_function_KAttentionInput"Reshape: é DAttention_test_attention_4d_attn_mask_4d_expanded_function_VExpanded JAttention_test_attention_4d_attn_mask_4d_expanded_function_VAttentionShapeJAttention_test_attention_4d_attn_mask_4d_expanded_function_VAttentionInput"Reshape: ŗ JAttention_test_attention_4d_attn_mask_4d_expanded_function_KAttentionInputEAttention_test_attention_4d_attn_mask_4d_expanded_function_KTranspose" Transpose* perm@@@@ : Ú DAttention_test_attention_4d_attn_mask_4d_expanded_function_QReshaped GAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorFBAttention_test_attention_4d_attn_mask_4d_expanded_function_QScaled"Mul: Û EAttention_test_attention_4d_attn_mask_4d_expanded_function_KTranspose GAttention_test_attention_4d_attn_mask_4d_expanded_function_ScaleFactorFBAttention_test_attention_4d_attn_mask_4d_expanded_function_KScaled"Mul: Û BAttention_test_attention_4d_attn_mask_4d_expanded_function_QScaled BAttention_test_attention_4d_attn_mask_4d_expanded_function_KScaledGAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeight"MatMul: Ŗ GAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeightEAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnCast"Cast* to : å EAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnCast DAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnBiasTOAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeightWithBias"Add: ­ OAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeightWithBiasNAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeightSoftcap"Identity: Ģ NAttention_test_attention_4d_attn_mask_4d_expanded_function_QKAttnWeightSoftcapFAttention_test_attention_4d_attn_mask_4d_expanded_function_SoftmaxCast"Cast* to : Ą FAttention_test_attention_4d_attn_mask_4d_expanded_function_SoftmaxCastLAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnWeightSoftmax"Softmax: ¨ LAttention_test_attention_4d_attn_mask_4d_expanded_function_AttnWeightSoftmaxEAttention_test_attention_4d_attn_mask_4d_expanded_function_SoftmaxOut"Cast* to : å EAttention_test_attention_4d_attn_mask_4d_expanded_function_SoftmaxOut JAttention_test_attention_4d_attn_mask_4d_expanded_function_VAttentionInputFAttention_test_attention_4d_attn_mask_4d_expanded_function_YPreReshape"MatMul: W FAttention_test_attention_4d_attn_mask_4d_expanded_function_YPreReshapeY"Identity:'test_attention_4d_attn_mask_4d_expandedZ Q     Z K     Z V     Z# attn_mask     b Y     B 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F#?UQ?‹pü>5-1?]Õō>!MĀ>ūž?î˙?ņu?#đ>(ˇ?ƒ)4?ŧ¯õ>4 ŋ>čē?\Ž?ŋ!?$Ļ?!_?Ÿ…0?Ãķ>vŦŋ>Åå?ú?“%?zCö>á5û>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_4d_expanded/000077500000000000000000000000001511334557700322235ustar00rootroot00000000000000model.onnx000066400000000000000000000306211511334557700341520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_4d_expanded  backend-test:øb q QIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_BatchSize"Shape* start * end :  QGAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‚ KHAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Z QIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QReshaped"Identity: Z KIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KReshaped"Identity: Z VIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VReshaped"Identity: š IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QReshapedIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads"Shape* start * end : ē IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KReshapedJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeads"Shape* start * end : ē IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QReshapedJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSize"Shape* start * end : Ŧ JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSizeKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSizeF"Cast* to : š IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VReshapedIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VHeadSize"Shape* start * end : Ŗ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSizeFLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SqrtHeadSize"Sqrt: hEAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_One1D"Constant* value*: : lFAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_One1DF"Constant* value* "€? : iFAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_Zero1D"Constant* value*: : î FAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_One1DF LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SqrtHeadSizeOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_CalculatedScale"Div: jFAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleF"Constant* value*"€? : Ē OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_CalculatedScaleKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactor"Identity: Ļ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorSqrt"Sqrt: ˛ OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorSqrtLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorF"Cast* to : Ŗ IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KReshapedJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PresentKey"Identity: oLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PastKVSeqLen"Constant* value*: : Ĩ IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VReshapedLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PresentValue"Identity: Î JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PresentKeyKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ü GAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QSeqLen KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLenMAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasShape"Concat* axis : qKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_FloatNegInf"Constant* value* "€˙ : pJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScalarZero"Constant* value* " : ü attn_mask JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScalarZero KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_FloatNegInfMAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasShort"Where: Ĩ MAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasShortHAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBias"Identity: Ģ HAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasSAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasCausalOrNot"Identity: ŗ SAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasCausalOrNotIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasT"Cast* to : ë IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeadsIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NGQACond1"Equal: œ IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NGQACond1HAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond1"Not: ë IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeadsKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_DivNumHeads"Div: Ž KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_DivNumHeadsLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_IDivNumHeads"Cast* to : ņ IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeadsQAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_RemainderNumHeads"Mod: î QAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_RemainderNumHeads FAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_Zero1DHAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond2"Equal: ä HAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond1 HAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond2GAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond"And: ļ GAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_GQACond LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_IDivNumHeads EAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_One1DMAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_InterleaveDim"Where: hEAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_Two1D"Constant* value*: : í JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PresentKey EAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_Two1DKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KUnsqueezed" Unsqueeze: ī LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_PresentValue EAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_Two1DKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VUnsqueezed" Unsqueeze: ä IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_BatchSize JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeads MAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_InterleaveDim KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLen JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSizeLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KExpandShape"Concat* axis : đ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KUnsqueezed LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KExpandShapeIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KExpanded"Expand: ã IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_BatchSize JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KVNumHeads MAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_InterleaveDim KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLen IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VHeadSizeLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VExpandShape"Concat* axis : đ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VUnsqueezed LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VExpandShapeIAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VExpanded"Expand: — IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_BatchSize IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLen JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKHeadSizeOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KAttentionShape"Concat* axis : – IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_BatchSize IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QNumHeads KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_NewKVSeqLen IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VHeadSizeOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VAttentionShape"Concat* axis : ø IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KExpanded OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KAttentionShapeOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KAttentionInput"Reshape: ø IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VExpanded OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VAttentionShapeOAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VAttentionInput"Reshape: Ŋ OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KAttentionInputJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KTranspose" Transpose* perm@@@@ : é IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QReshaped LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorFGAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QScaled"Mul: ę JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KTranspose LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_ScaleFactorFGAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KScaled"Mul: ę GAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QScaled GAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_KScaledLAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeight"MatMul: ­ LAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeightJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnCast"Cast* to : ô JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnCast IAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnBiasTTAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeightWithBias"Add: ˇ TAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeightWithBiasSAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeightSoftcap"Identity: ĩ SAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_QKAttnWeightSoftcapKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SoftmaxCast"Cast* to : Ģ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SoftmaxCastQAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnWeightSoftmax"Softmax: ˛ QAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_AttnWeightSoftmaxJAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SoftmaxOut"Cast* to : ô JAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_SoftmaxOut OAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_VAttentionInputKAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_YPreReshape"MatMul: \ KAttention_test_attention_4d_attn_mask_bool_4d_expanded_function_YPreReshapeY"Identity:,test_attention_4d_attn_mask_bool_4d_expandedZ Q     Z K     Z V     Z# attn_mask      b Y     B test_data_set_0/000077500000000000000000000000001511334557700352065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_4d_expandedinput_0.pb000066400000000000000000000014201511334557700371040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_4d_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700371040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_4d_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? 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F#?UQ?‹pü>5-1?]Õō>!MĀ>ūž?î˙?ņu?#đ>(ˇ?ƒ)4?ŧ¯õ>4 ŋ>čē?\Ž?ŋ!?$Ļ?!_?Ÿ…0?Ãķ>vŦŋ>Åå?ú?“%?zCö>á5û>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_expanded/000077500000000000000000000000001511334557700316345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_bool_expanded/model.onnx000066400000000000000000000277241511334557700336540ustar00rootroot00000000000000  backend-test:ģ_ n QFAttention_test_attention_4d_attn_mask_bool_expanded_function_BatchSize"Shape* start * end : ~ QDAttention_test_attention_4d_attn_mask_bool_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KEAttention_test_attention_4d_attn_mask_bool_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : W QFAttention_test_attention_4d_attn_mask_bool_expanded_function_QReshaped"Identity: W KFAttention_test_attention_4d_attn_mask_bool_expanded_function_KReshaped"Identity: W VFAttention_test_attention_4d_attn_mask_bool_expanded_function_VReshaped"Identity: ŗ FAttention_test_attention_4d_attn_mask_bool_expanded_function_QReshapedFAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads"Shape* start * end : ´ FAttention_test_attention_4d_attn_mask_bool_expanded_function_KReshapedGAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeads"Shape* start * end : ´ FAttention_test_attention_4d_attn_mask_bool_expanded_function_QReshapedGAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSize"Shape* start * end : Ļ GAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSizeHAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSizeF"Cast* to : ŗ FAttention_test_attention_4d_attn_mask_bool_expanded_function_VReshapedFAttention_test_attention_4d_attn_mask_bool_expanded_function_VHeadSize"Shape* start * end :  HAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSizeFIAttention_test_attention_4d_attn_mask_bool_expanded_function_SqrtHeadSize"Sqrt: eBAttention_test_attention_4d_attn_mask_bool_expanded_function_One1D"Constant* value*: : iCAttention_test_attention_4d_attn_mask_bool_expanded_function_One1DF"Constant* value* "€? : fCAttention_test_attention_4d_attn_mask_bool_expanded_function_Zero1D"Constant* value*: : å CAttention_test_attention_4d_attn_mask_bool_expanded_function_One1DF IAttention_test_attention_4d_attn_mask_bool_expanded_function_SqrtHeadSizeLAttention_test_attention_4d_attn_mask_bool_expanded_function_CalculatedScale"Div: gCAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleF"Constant* value*"€? : ¤ LAttention_test_attention_4d_attn_mask_bool_expanded_function_CalculatedScaleHAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactor"Identity:   HAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorLAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorSqrt"Sqrt: Ŧ LAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorSqrtIAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorF"Cast* to :  FAttention_test_attention_4d_attn_mask_bool_expanded_function_KReshapedGAttention_test_attention_4d_attn_mask_bool_expanded_function_PresentKey"Identity: lIAttention_test_attention_4d_attn_mask_bool_expanded_function_PastKVSeqLen"Constant* value*: : Ÿ FAttention_test_attention_4d_attn_mask_bool_expanded_function_VReshapedIAttention_test_attention_4d_attn_mask_bool_expanded_function_PresentValue"Identity: Č GAttention_test_attention_4d_attn_mask_bool_expanded_function_PresentKeyHAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ķ DAttention_test_attention_4d_attn_mask_bool_expanded_function_QSeqLen HAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLenJAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasShape"Concat* axis : nHAttention_test_attention_4d_attn_mask_bool_expanded_function_FloatNegInf"Constant* value* "€˙ : mGAttention_test_attention_4d_attn_mask_bool_expanded_function_ScalarZero"Constant* value* " : ķ attn_mask GAttention_test_attention_4d_attn_mask_bool_expanded_function_ScalarZero HAttention_test_attention_4d_attn_mask_bool_expanded_function_FloatNegInfJAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasShort"Where: Ÿ JAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasShortEAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBias"Identity: Ĩ EAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasPAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasCausalOrNot"Identity: ­ PAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasCausalOrNotFAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasT"Cast* to : â FAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads GAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeadsFAttention_test_attention_4d_attn_mask_bool_expanded_function_NGQACond1"Equal: – FAttention_test_attention_4d_attn_mask_bool_expanded_function_NGQACond1EAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond1"Not: â FAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads GAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeadsHAttention_test_attention_4d_attn_mask_bool_expanded_function_DivNumHeads"Div: ¨ HAttention_test_attention_4d_attn_mask_bool_expanded_function_DivNumHeadsIAttention_test_attention_4d_attn_mask_bool_expanded_function_IDivNumHeads"Cast* to : č FAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads GAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeadsNAttention_test_attention_4d_attn_mask_bool_expanded_function_RemainderNumHeads"Mod: å NAttention_test_attention_4d_attn_mask_bool_expanded_function_RemainderNumHeads CAttention_test_attention_4d_attn_mask_bool_expanded_function_Zero1DEAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond2"Equal: Û EAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond1 EAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond2DAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond"And: Ē DAttention_test_attention_4d_attn_mask_bool_expanded_function_GQACond IAttention_test_attention_4d_attn_mask_bool_expanded_function_IDivNumHeads BAttention_test_attention_4d_attn_mask_bool_expanded_function_One1DJAttention_test_attention_4d_attn_mask_bool_expanded_function_InterleaveDim"Where: eBAttention_test_attention_4d_attn_mask_bool_expanded_function_Two1D"Constant* value*: : ä GAttention_test_attention_4d_attn_mask_bool_expanded_function_PresentKey BAttention_test_attention_4d_attn_mask_bool_expanded_function_Two1DHAttention_test_attention_4d_attn_mask_bool_expanded_function_KUnsqueezed" Unsqueeze: æ IAttention_test_attention_4d_attn_mask_bool_expanded_function_PresentValue BAttention_test_attention_4d_attn_mask_bool_expanded_function_Two1DHAttention_test_attention_4d_attn_mask_bool_expanded_function_VUnsqueezed" Unsqueeze: Ō FAttention_test_attention_4d_attn_mask_bool_expanded_function_BatchSize GAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeads JAttention_test_attention_4d_attn_mask_bool_expanded_function_InterleaveDim HAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLen GAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSizeIAttention_test_attention_4d_attn_mask_bool_expanded_function_KExpandShape"Concat* axis : į HAttention_test_attention_4d_attn_mask_bool_expanded_function_KUnsqueezed IAttention_test_attention_4d_attn_mask_bool_expanded_function_KExpandShapeFAttention_test_attention_4d_attn_mask_bool_expanded_function_KExpanded"Expand: Ņ FAttention_test_attention_4d_attn_mask_bool_expanded_function_BatchSize GAttention_test_attention_4d_attn_mask_bool_expanded_function_KVNumHeads JAttention_test_attention_4d_attn_mask_bool_expanded_function_InterleaveDim HAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLen FAttention_test_attention_4d_attn_mask_bool_expanded_function_VHeadSizeIAttention_test_attention_4d_attn_mask_bool_expanded_function_VExpandShape"Concat* axis : į HAttention_test_attention_4d_attn_mask_bool_expanded_function_VUnsqueezed IAttention_test_attention_4d_attn_mask_bool_expanded_function_VExpandShapeFAttention_test_attention_4d_attn_mask_bool_expanded_function_VExpanded"Expand: ˆ FAttention_test_attention_4d_attn_mask_bool_expanded_function_BatchSize FAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads HAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLen GAttention_test_attention_4d_attn_mask_bool_expanded_function_QKHeadSizeLAttention_test_attention_4d_attn_mask_bool_expanded_function_KAttentionShape"Concat* axis : ‡ FAttention_test_attention_4d_attn_mask_bool_expanded_function_BatchSize FAttention_test_attention_4d_attn_mask_bool_expanded_function_QNumHeads HAttention_test_attention_4d_attn_mask_bool_expanded_function_NewKVSeqLen FAttention_test_attention_4d_attn_mask_bool_expanded_function_VHeadSizeLAttention_test_attention_4d_attn_mask_bool_expanded_function_VAttentionShape"Concat* axis : ī FAttention_test_attention_4d_attn_mask_bool_expanded_function_KExpanded LAttention_test_attention_4d_attn_mask_bool_expanded_function_KAttentionShapeLAttention_test_attention_4d_attn_mask_bool_expanded_function_KAttentionInput"Reshape: ī FAttention_test_attention_4d_attn_mask_bool_expanded_function_VExpanded LAttention_test_attention_4d_attn_mask_bool_expanded_function_VAttentionShapeLAttention_test_attention_4d_attn_mask_bool_expanded_function_VAttentionInput"Reshape: ˇ LAttention_test_attention_4d_attn_mask_bool_expanded_function_KAttentionInputGAttention_test_attention_4d_attn_mask_bool_expanded_function_KTranspose" Transpose* perm@@@@ : ā FAttention_test_attention_4d_attn_mask_bool_expanded_function_QReshaped IAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorFDAttention_test_attention_4d_attn_mask_bool_expanded_function_QScaled"Mul: á GAttention_test_attention_4d_attn_mask_bool_expanded_function_KTranspose IAttention_test_attention_4d_attn_mask_bool_expanded_function_ScaleFactorFDAttention_test_attention_4d_attn_mask_bool_expanded_function_KScaled"Mul: á DAttention_test_attention_4d_attn_mask_bool_expanded_function_QScaled DAttention_test_attention_4d_attn_mask_bool_expanded_function_KScaledIAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeight"MatMul: § IAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeightGAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnCast"Cast* to : ë GAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnCast FAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnBiasTQAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeightWithBias"Add: ą QAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeightWithBiasPAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeightSoftcap"Identity: ¯ PAttention_test_attention_4d_attn_mask_bool_expanded_function_QKAttnWeightSoftcapHAttention_test_attention_4d_attn_mask_bool_expanded_function_SoftmaxCast"Cast* to : Ĩ HAttention_test_attention_4d_attn_mask_bool_expanded_function_SoftmaxCastNAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnWeightSoftmax"Softmax: Ŧ NAttention_test_attention_4d_attn_mask_bool_expanded_function_AttnWeightSoftmaxGAttention_test_attention_4d_attn_mask_bool_expanded_function_SoftmaxOut"Cast* to : ë GAttention_test_attention_4d_attn_mask_bool_expanded_function_SoftmaxOut LAttention_test_attention_4d_attn_mask_bool_expanded_function_VAttentionInputHAttention_test_attention_4d_attn_mask_bool_expanded_function_YPreReshape"MatMul: Y HAttention_test_attention_4d_attn_mask_bool_expanded_function_YPreReshapeY"Identity:)test_attention_4d_attn_mask_bool_expandedZ Q     Z K     Z V     Z attn_mask    b Y     B 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F#?UQ?‹pü>5-1?]Õō>!MĀ>ūž?î˙?ņu?#đ>(ˇ?ƒ)4?ŧ¯õ>4 ŋ>čē?\Ž?ŋ!?$Ļ?!_?Ÿ…0?Ãķ>vŦŋ>Åå?ú?“%?zCö>á5û>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_expanded/000077500000000000000000000000001511334557700306215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_expanded/model.onnx000066400000000000000000000261701511334557700326330ustar00rootroot00000000000000  backend-test:ßX i QAAttention_test_attention_4d_attn_mask_expanded_function_BatchSize"Shape* start * end : y Q?Attention_test_attention_4d_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : z K@Attention_test_attention_4d_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : R QAAttention_test_attention_4d_attn_mask_expanded_function_QReshaped"Identity: R KAAttention_test_attention_4d_attn_mask_expanded_function_KReshaped"Identity: R VAAttention_test_attention_4d_attn_mask_expanded_function_VReshaped"Identity: Š AAttention_test_attention_4d_attn_mask_expanded_function_QReshapedAAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads"Shape* start * end : Ē AAttention_test_attention_4d_attn_mask_expanded_function_KReshapedBAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeads"Shape* start * end : Ē AAttention_test_attention_4d_attn_mask_expanded_function_QReshapedBAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSize"Shape* start * end : œ BAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSizeCAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSizeF"Cast* to : Š AAttention_test_attention_4d_attn_mask_expanded_function_VReshapedAAttention_test_attention_4d_attn_mask_expanded_function_VHeadSize"Shape* start * end : “ CAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSizeFDAttention_test_attention_4d_attn_mask_expanded_function_SqrtHeadSize"Sqrt: `=Attention_test_attention_4d_attn_mask_expanded_function_One1D"Constant* value*: : d>Attention_test_attention_4d_attn_mask_expanded_function_One1DF"Constant* value* "€? : a>Attention_test_attention_4d_attn_mask_expanded_function_Zero1D"Constant* value*: : Ö >Attention_test_attention_4d_attn_mask_expanded_function_One1DF DAttention_test_attention_4d_attn_mask_expanded_function_SqrtHeadSizeGAttention_test_attention_4d_attn_mask_expanded_function_CalculatedScale"Div: b>Attention_test_attention_4d_attn_mask_expanded_function_ScaleF"Constant* value*"€? : š GAttention_test_attention_4d_attn_mask_expanded_function_CalculatedScaleCAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactor"Identity: – CAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorGAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: ĸ GAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorSqrtDAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorF"Cast* to : “ AAttention_test_attention_4d_attn_mask_expanded_function_KReshapedBAttention_test_attention_4d_attn_mask_expanded_function_PresentKey"Identity: gDAttention_test_attention_4d_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: : • AAttention_test_attention_4d_attn_mask_expanded_function_VReshapedDAttention_test_attention_4d_attn_mask_expanded_function_PresentValue"Identity: ž BAttention_test_attention_4d_attn_mask_expanded_function_PresentKeyCAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ä ?Attention_test_attention_4d_attn_mask_expanded_function_QSeqLen CAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLenEAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasShape"Concat* axis : iCAttention_test_attention_4d_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : hBAttention_test_attention_4d_attn_mask_expanded_function_ScalarZero"Constant* value* " : ^ attn_maskEAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasShort"Identity: • EAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasShort@Attention_test_attention_4d_attn_mask_expanded_function_AttnBias"Identity: › @Attention_test_attention_4d_attn_mask_expanded_function_AttnBiasKAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Ŗ KAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasCausalOrNotAAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasT"Cast* to : Ķ AAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeadsAAttention_test_attention_4d_attn_mask_expanded_function_NGQACond1"Equal: Œ AAttention_test_attention_4d_attn_mask_expanded_function_NGQACond1@Attention_test_attention_4d_attn_mask_expanded_function_GQACond1"Not: Ķ AAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeadsCAttention_test_attention_4d_attn_mask_expanded_function_DivNumHeads"Div: ž CAttention_test_attention_4d_attn_mask_expanded_function_DivNumHeadsDAttention_test_attention_4d_attn_mask_expanded_function_IDivNumHeads"Cast* to : Ų AAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads BAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeadsIAttention_test_attention_4d_attn_mask_expanded_function_RemainderNumHeads"Mod: Ö IAttention_test_attention_4d_attn_mask_expanded_function_RemainderNumHeads >Attention_test_attention_4d_attn_mask_expanded_function_Zero1D@Attention_test_attention_4d_attn_mask_expanded_function_GQACond2"Equal: Ė @Attention_test_attention_4d_attn_mask_expanded_function_GQACond1 @Attention_test_attention_4d_attn_mask_expanded_function_GQACond2?Attention_test_attention_4d_attn_mask_expanded_function_GQACond"And: – ?Attention_test_attention_4d_attn_mask_expanded_function_GQACond DAttention_test_attention_4d_attn_mask_expanded_function_IDivNumHeads =Attention_test_attention_4d_attn_mask_expanded_function_One1DEAttention_test_attention_4d_attn_mask_expanded_function_InterleaveDim"Where: `=Attention_test_attention_4d_attn_mask_expanded_function_Two1D"Constant* value*: : Õ BAttention_test_attention_4d_attn_mask_expanded_function_PresentKey =Attention_test_attention_4d_attn_mask_expanded_function_Two1DCAttention_test_attention_4d_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: × DAttention_test_attention_4d_attn_mask_expanded_function_PresentValue =Attention_test_attention_4d_attn_mask_expanded_function_Two1DCAttention_test_attention_4d_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: ´ AAttention_test_attention_4d_attn_mask_expanded_function_BatchSize BAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeads EAttention_test_attention_4d_attn_mask_expanded_function_InterleaveDim CAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLen BAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSizeDAttention_test_attention_4d_attn_mask_expanded_function_KExpandShape"Concat* axis : Ø CAttention_test_attention_4d_attn_mask_expanded_function_KUnsqueezed DAttention_test_attention_4d_attn_mask_expanded_function_KExpandShapeAAttention_test_attention_4d_attn_mask_expanded_function_KExpanded"Expand: ŗ AAttention_test_attention_4d_attn_mask_expanded_function_BatchSize BAttention_test_attention_4d_attn_mask_expanded_function_KVNumHeads EAttention_test_attention_4d_attn_mask_expanded_function_InterleaveDim CAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLen AAttention_test_attention_4d_attn_mask_expanded_function_VHeadSizeDAttention_test_attention_4d_attn_mask_expanded_function_VExpandShape"Concat* axis : Ø CAttention_test_attention_4d_attn_mask_expanded_function_VUnsqueezed DAttention_test_attention_4d_attn_mask_expanded_function_VExpandShapeAAttention_test_attention_4d_attn_mask_expanded_function_VExpanded"Expand: ī AAttention_test_attention_4d_attn_mask_expanded_function_BatchSize AAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads CAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLen BAttention_test_attention_4d_attn_mask_expanded_function_QKHeadSizeGAttention_test_attention_4d_attn_mask_expanded_function_KAttentionShape"Concat* axis : î AAttention_test_attention_4d_attn_mask_expanded_function_BatchSize AAttention_test_attention_4d_attn_mask_expanded_function_QNumHeads CAttention_test_attention_4d_attn_mask_expanded_function_NewKVSeqLen AAttention_test_attention_4d_attn_mask_expanded_function_VHeadSizeGAttention_test_attention_4d_attn_mask_expanded_function_VAttentionShape"Concat* axis : ā AAttention_test_attention_4d_attn_mask_expanded_function_KExpanded GAttention_test_attention_4d_attn_mask_expanded_function_KAttentionShapeGAttention_test_attention_4d_attn_mask_expanded_function_KAttentionInput"Reshape: ā AAttention_test_attention_4d_attn_mask_expanded_function_VExpanded GAttention_test_attention_4d_attn_mask_expanded_function_VAttentionShapeGAttention_test_attention_4d_attn_mask_expanded_function_VAttentionInput"Reshape: ­ GAttention_test_attention_4d_attn_mask_expanded_function_KAttentionInputBAttention_test_attention_4d_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : Ņ AAttention_test_attention_4d_attn_mask_expanded_function_QReshaped DAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorF?Attention_test_attention_4d_attn_mask_expanded_function_QScaled"Mul: Ō BAttention_test_attention_4d_attn_mask_expanded_function_KTranspose DAttention_test_attention_4d_attn_mask_expanded_function_ScaleFactorF?Attention_test_attention_4d_attn_mask_expanded_function_KScaled"Mul: Ō ?Attention_test_attention_4d_attn_mask_expanded_function_QScaled ?Attention_test_attention_4d_attn_mask_expanded_function_KScaledDAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeight"MatMul:  DAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeightBAttention_test_attention_4d_attn_mask_expanded_function_QKAttnCast"Cast* to : Ü BAttention_test_attention_4d_attn_mask_expanded_function_QKAttnCast AAttention_test_attention_4d_attn_mask_expanded_function_AttnBiasTLAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeightWithBias"Add: § LAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeightWithBiasKAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: Ĩ KAttention_test_attention_4d_attn_mask_expanded_function_QKAttnWeightSoftcapCAttention_test_attention_4d_attn_mask_expanded_function_SoftmaxCast"Cast* to : › CAttention_test_attention_4d_attn_mask_expanded_function_SoftmaxCastIAttention_test_attention_4d_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: ĸ IAttention_test_attention_4d_attn_mask_expanded_function_AttnWeightSoftmaxBAttention_test_attention_4d_attn_mask_expanded_function_SoftmaxOut"Cast* to : Ü BAttention_test_attention_4d_attn_mask_expanded_function_SoftmaxOut GAttention_test_attention_4d_attn_mask_expanded_function_VAttentionInputCAttention_test_attention_4d_attn_mask_expanded_function_YPreReshape"MatMul: T CAttention_test_attention_4d_attn_mask_expanded_function_YPreReshapeY"Identity:$test_attention_4d_attn_mask_expandedZ Q     Z K     Z V     Z attn_mask   b Y     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_expanded/test_data_set_0/000077500000000000000000000000001511334557700336635ustar00rootroot00000000000000input_0.pb000066400000000000000000000014201511334557700355020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700355020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_attn_mask_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? 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Ē>M{?Z-_?O˛?˙l?ī— ?É]l?'tT?žáw?ãvk?2˜=o÷2>Š<Į> ŋs?d™>ĄQ$>Ũäb?Īä>‰jh?u$>˙>)?@já>Ĩœ=iK2?V}>ãC"='ˆu=}-z=0ah?i=?jįe?[.,?›h?eā›>tz?Øpš>æøđ>ZŠÁ>GÂz?ĻŲ2> î§>U+.?˙r=˛t?Žô>rh‘>œ"t>ˇ?aŧ>úŧé>ÖÉŦ>Frx?Y¤>'AÆ= ҝ>ŠM?Ęŋ(?=eË>¯Đ?R+´>Ž8?Ÿ8#?L$P?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>TŨ>ŒĄ?ÎčE? pu?¸Eđ=ũ$Û=<ö?hŌ>?b Y?ą’o?ŌÁ{?ΞĖ>KģÂ>([>ŨW/?!(?"°\?3/Į=Üū>ÉÁ?ģZw>˙->} \?QÂo=9õđ>`:í=›ę>ĐŪz? đØ>Šl[?$Cđ=áŠ>ėŊÎ>-´Ė>Ęß+?å~°>mš6?ÁĄ#?Ú^Ė>ĒŨ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī> Ī>ŋ_¤>bZõ<˛ŧę0?_îē>ų#C>Ÿ§œj6d>÷í¤=~ˇŽ=Āĩb>,ÔĖ=Iŗ‡>_y‡=Ø[†=ę4[?Û&>YG?/F?„Žé>ģ >îbL>°Ũ>Y:?×é˛> H?ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?V`B?˜Ĩē>­E?ļĀ>šÕē>F•…>Ōīũ>‚†.?‹˙>Á=?eđ=|Ž#>¸?=ہx?ķũ|;ĢŨ6>Öä?ĨĻ=øÃa?98?Uew?gô?ƒÎ™>Ŧ ?#Jn?ŸP?\Έ>5`?"lž>†Qĩ:$Ą}>īĸ>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal/test_data_set_0/output_0.pb000066400000000000000000000014201511334557700334010ustar00rootroot00000000000000BYJ€-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?cîî>}QĪ>ÛYS>Ņ;ũ>’*đ>1[?Û=ę>ėÁ?–gé>:ŋÍ>tBd>’˙>ręË>’õę>×é÷>¤Ĩ-?ČtÛ>MÜ?Æ0×>ė¸>×Áģ>Ņé>pŧ>Üâ?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė="TŽ>@ ?™ˇ>ũ–'? R›>ôäī>"VŌ>Æåˇ>ü( ?@ Į>Wđ>†¯5?ī´š>§íÛ> „?dö>å ?ßËÖ>?Î÷?ņ3Ģ>xÖ>—Ō ?m÷á>ĄQ$>Ũäb?Īä>‰jh?u$>˙>)?@já>Ĩœ=Äaá>”ˆ ?íˇo> Bî>Ô,Ū=į1J?īŧ?ļX?Ģs?qr?euy>3Ē?j;7>:T4?‡Š ?ėß)?$å>b=ņ>Š>ĘÄá><6‡>šr+?ĸßū>[ø?íéy?„Éc?UēC?jĀ2?kÆĢ>ã:>NG€=ĩw>yĄ*?ˇ-.?NøD?K¸X?ôXX> ˙=Ÿļš>yP?rß8?0•C?;˙W?Mö1?í­ˆ>ĸ7>öö>*o?@CD?ä‚?‚@?é)?(—†>€>õ?•ŦŨ>°Q?Hr=?‰R?œF'?”ņ9?És ?ĶAâ=Ô`Ī> S?&K1>jkū>@0?Õ9ō>ƒŋ?†7ĩ>Íą˙>ˇ/?AP >Ŗ(?øęá>%Ãņ>2Ë?8Hâ>N@ę>"_?oá >ôx?ž%? $ã>ã×é>ëÉ>/mĘ>ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?RgA?”?v\‘> ?YÁ>Į ?uč?߂R?ĖU?{Ÿ?Amp>”ë>x´‰>īb-?#É>]ĸ?Aô?ņ>î,ž>Až?ĩũÖ>ĻÔ+?´Ķ°>’^?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal_expanded/000077500000000000000000000000001511334557700301105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal_expanded/model.onnx000066400000000000000000000323571511334557700321260ustar00rootroot00000000000000  backend-test:Öi f Q>Attention_test_attention_4d_causal_expanded_function_BatchSize"Shape* start * end : v QAttention_test_attention_4d_causal_expanded_function_QReshaped"Identity: O K>Attention_test_attention_4d_causal_expanded_function_KReshaped"Identity: O V>Attention_test_attention_4d_causal_expanded_function_VReshaped"Identity: Ŗ >Attention_test_attention_4d_causal_expanded_function_QReshaped>Attention_test_attention_4d_causal_expanded_function_QNumHeads"Shape* start * end : ¤ >Attention_test_attention_4d_causal_expanded_function_KReshaped?Attention_test_attention_4d_causal_expanded_function_KVNumHeads"Shape* start * end : ¤ >Attention_test_attention_4d_causal_expanded_function_QReshaped?Attention_test_attention_4d_causal_expanded_function_QKHeadSize"Shape* start * end : – ?Attention_test_attention_4d_causal_expanded_function_QKHeadSize@Attention_test_attention_4d_causal_expanded_function_QKHeadSizeF"Cast* to : Ŗ >Attention_test_attention_4d_causal_expanded_function_VReshaped>Attention_test_attention_4d_causal_expanded_function_VHeadSize"Shape* start * end :  @Attention_test_attention_4d_causal_expanded_function_QKHeadSizeFAAttention_test_attention_4d_causal_expanded_function_SqrtHeadSize"Sqrt: ]:Attention_test_attention_4d_causal_expanded_function_One1D"Constant* value*: : a;Attention_test_attention_4d_causal_expanded_function_One1DF"Constant* value* "€? : ^;Attention_test_attention_4d_causal_expanded_function_Zero1D"Constant* value*: : Í ;Attention_test_attention_4d_causal_expanded_function_One1DF AAttention_test_attention_4d_causal_expanded_function_SqrtHeadSizeDAttention_test_attention_4d_causal_expanded_function_CalculatedScale"Div: _;Attention_test_attention_4d_causal_expanded_function_ScaleF"Constant* value*"€? : ” DAttention_test_attention_4d_causal_expanded_function_CalculatedScale@Attention_test_attention_4d_causal_expanded_function_ScaleFactor"Identity:  @Attention_test_attention_4d_causal_expanded_function_ScaleFactorDAttention_test_attention_4d_causal_expanded_function_ScaleFactorSqrt"Sqrt: œ DAttention_test_attention_4d_causal_expanded_function_ScaleFactorSqrtAAttention_test_attention_4d_causal_expanded_function_ScaleFactorF"Cast* to :  >Attention_test_attention_4d_causal_expanded_function_KReshaped?Attention_test_attention_4d_causal_expanded_function_PresentKey"Identity: dAAttention_test_attention_4d_causal_expanded_function_PastKVSeqLen"Constant* value*: :  >Attention_test_attention_4d_causal_expanded_function_VReshapedAAttention_test_attention_4d_causal_expanded_function_PresentValue"Identity: ¸ ?Attention_test_attention_4d_causal_expanded_function_PresentKey@Attention_test_attention_4d_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Û Attention_test_attention_4d_causal_expanded_function_ZeroNoDim"Squeeze: ŋ 8Attention_test_attention_4d_causal_expanded_function_One 9Attention_test_attention_4d_causal_expanded_function_Zero=Attention_test_attention_4d_causal_expanded_function_OneNoDim"Squeeze: Ķ BAttention_test_attention_4d_causal_expanded_function_AttnBiasShape >Attention_test_attention_4d_causal_expanded_function_ZeroNoDimCAttention_test_attention_4d_causal_expanded_function_SequenceLength"Gather: × BAttention_test_attention_4d_causal_expanded_function_AttnBiasShape =Attention_test_attention_4d_causal_expanded_function_OneNoDimHAttention_test_attention_4d_causal_expanded_function_TotalSequenceLength"Gather: Œ >Attention_test_attention_4d_causal_expanded_function_ZeroNoDim CAttention_test_attention_4d_causal_expanded_function_SequenceLength =Attention_test_attention_4d_causal_expanded_function_OneNoDim=Attention_test_attention_4d_causal_expanded_function_RangeRow"Range: Į =Attention_test_attention_4d_causal_expanded_function_RangeRow 8Attention_test_attention_4d_causal_expanded_function_One?Attention_test_attention_4d_causal_expanded_function_RangeRow2D" Unsqueeze: ‘ >Attention_test_attention_4d_causal_expanded_function_ZeroNoDim HAttention_test_attention_4d_causal_expanded_function_TotalSequenceLength =Attention_test_attention_4d_causal_expanded_function_OneNoDim=Attention_test_attention_4d_causal_expanded_function_RangeCol"Range: Č =Attention_test_attention_4d_causal_expanded_function_RangeCol 9Attention_test_attention_4d_causal_expanded_function_Zero?Attention_test_attention_4d_causal_expanded_function_RangeCol2D" Unsqueeze: Đ ?Attention_test_attention_4d_causal_expanded_function_RangeRow2D AAttention_test_attention_4d_causal_expanded_function_PastKVSeqLenCAttention_test_attention_4d_causal_expanded_function_RangeRow2DPast"Add: Đ CAttention_test_attention_4d_causal_expanded_function_RangeRow2DPast ?Attention_test_attention_4d_causal_expanded_function_RangeCol2D@Attention_test_attention_4d_causal_expanded_function_BoolMaskTri"Less: Œ @Attention_test_attention_4d_causal_expanded_function_BoolMaskTri @Attention_test_attention_4d_causal_expanded_function_FloatNegInf ?Attention_test_attention_4d_causal_expanded_function_ScalarZeroAttention_test_attention_4d_causal_expanded_function_AttnBiasT"Cast* to : Ę >Attention_test_attention_4d_causal_expanded_function_QNumHeads ?Attention_test_attention_4d_causal_expanded_function_KVNumHeads>Attention_test_attention_4d_causal_expanded_function_NGQACond1"Equal: † >Attention_test_attention_4d_causal_expanded_function_NGQACond1=Attention_test_attention_4d_causal_expanded_function_GQACond1"Not: Ę >Attention_test_attention_4d_causal_expanded_function_QNumHeads ?Attention_test_attention_4d_causal_expanded_function_KVNumHeads@Attention_test_attention_4d_causal_expanded_function_DivNumHeads"Div: ˜ @Attention_test_attention_4d_causal_expanded_function_DivNumHeadsAAttention_test_attention_4d_causal_expanded_function_IDivNumHeads"Cast* to : Đ >Attention_test_attention_4d_causal_expanded_function_QNumHeads ?Attention_test_attention_4d_causal_expanded_function_KVNumHeadsFAttention_test_attention_4d_causal_expanded_function_RemainderNumHeads"Mod: Í FAttention_test_attention_4d_causal_expanded_function_RemainderNumHeads ;Attention_test_attention_4d_causal_expanded_function_Zero1D=Attention_test_attention_4d_causal_expanded_function_GQACond2"Equal: à =Attention_test_attention_4d_causal_expanded_function_GQACond1 =Attention_test_attention_4d_causal_expanded_function_GQACond2Attention_test_attention_4d_causal_expanded_function_BatchSize ?Attention_test_attention_4d_causal_expanded_function_KVNumHeads BAttention_test_attention_4d_causal_expanded_function_InterleaveDim @Attention_test_attention_4d_causal_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_causal_expanded_function_QKHeadSizeAAttention_test_attention_4d_causal_expanded_function_KExpandShape"Concat* axis : Ī @Attention_test_attention_4d_causal_expanded_function_KUnsqueezed AAttention_test_attention_4d_causal_expanded_function_KExpandShape>Attention_test_attention_4d_causal_expanded_function_KExpanded"Expand: Ą >Attention_test_attention_4d_causal_expanded_function_BatchSize ?Attention_test_attention_4d_causal_expanded_function_KVNumHeads BAttention_test_attention_4d_causal_expanded_function_InterleaveDim @Attention_test_attention_4d_causal_expanded_function_NewKVSeqLen >Attention_test_attention_4d_causal_expanded_function_VHeadSizeAAttention_test_attention_4d_causal_expanded_function_VExpandShape"Concat* axis : Ī @Attention_test_attention_4d_causal_expanded_function_VUnsqueezed AAttention_test_attention_4d_causal_expanded_function_VExpandShape>Attention_test_attention_4d_causal_expanded_function_VExpanded"Expand: ā >Attention_test_attention_4d_causal_expanded_function_BatchSize >Attention_test_attention_4d_causal_expanded_function_QNumHeads @Attention_test_attention_4d_causal_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_causal_expanded_function_QKHeadSizeDAttention_test_attention_4d_causal_expanded_function_KAttentionShape"Concat* axis : ß >Attention_test_attention_4d_causal_expanded_function_BatchSize >Attention_test_attention_4d_causal_expanded_function_QNumHeads @Attention_test_attention_4d_causal_expanded_function_NewKVSeqLen >Attention_test_attention_4d_causal_expanded_function_VHeadSizeDAttention_test_attention_4d_causal_expanded_function_VAttentionShape"Concat* axis : × >Attention_test_attention_4d_causal_expanded_function_KExpanded DAttention_test_attention_4d_causal_expanded_function_KAttentionShapeDAttention_test_attention_4d_causal_expanded_function_KAttentionInput"Reshape: × >Attention_test_attention_4d_causal_expanded_function_VExpanded DAttention_test_attention_4d_causal_expanded_function_VAttentionShapeDAttention_test_attention_4d_causal_expanded_function_VAttentionInput"Reshape: § DAttention_test_attention_4d_causal_expanded_function_KAttentionInput?Attention_test_attention_4d_causal_expanded_function_KTranspose" Transpose* perm@@@@ : Č >Attention_test_attention_4d_causal_expanded_function_QReshaped AAttention_test_attention_4d_causal_expanded_function_ScaleFactorFAttention_test_attention_4d_causal_expanded_function_AttnBiasTIAttention_test_attention_4d_causal_expanded_function_QKAttnWeightWithBias"Add: Ą IAttention_test_attention_4d_causal_expanded_function_QKAttnWeightWithBiasHAttention_test_attention_4d_causal_expanded_function_QKAttnWeightSoftcap"Identity: Ÿ HAttention_test_attention_4d_causal_expanded_function_QKAttnWeightSoftcap@Attention_test_attention_4d_causal_expanded_function_SoftmaxCast"Cast* to : • @Attention_test_attention_4d_causal_expanded_function_SoftmaxCastFAttention_test_attention_4d_causal_expanded_function_AttnWeightSoftmax"Softmax: œ FAttention_test_attention_4d_causal_expanded_function_AttnWeightSoftmax?Attention_test_attention_4d_causal_expanded_function_SoftmaxOut"Cast* to : Ķ ?Attention_test_attention_4d_causal_expanded_function_SoftmaxOut DAttention_test_attention_4d_causal_expanded_function_VAttentionInput@Attention_test_attention_4d_causal_expanded_function_YPreReshape"MatMul: Q @Attention_test_attention_4d_causal_expanded_function_YPreReshapeY"Identity:!test_attention_4d_causal_expandedZ Q     Z K     Z V     b Y     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal_expanded/test_data_set_0/000077500000000000000000000000001511334557700331525ustar00rootroot00000000000000input_0.pb000066400000000000000000000014201511334557700347710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700347710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_causal_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? 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KRAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : d QSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QReshaped"Identity: d KSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KReshaped"Identity: d VSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VReshaped"Identity: Í SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QReshapedSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads"Shape* start * end : Î SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KReshapedTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeads"Shape* start * end : Î SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QReshapedTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSize"Shape* start * end : Ā TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSizeUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSizeF"Cast* to : Í SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VReshapedSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VHeadSize"Shape* start * end : ˇ UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSizeFVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SqrtHeadSize"Sqrt: rOAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1D"Constant* value*: : ~RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NegOne1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : vPAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DF"Constant* value* "€? : sPAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Zero1D"Constant* value*: : Œ PAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DF VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SqrtHeadSizeYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_CalculatedScale"Div: tPAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleF"Constant* value*"€? : ž YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_CalculatedScaleUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactor"Identity: ē UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorSqrt"Sqrt: Æ YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorSqrtVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorF"Cast* to : ˇ SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KReshapedTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PresentKey"Identity: yVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PastKVSeqLen"Constant* value*: : š SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VReshapedVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PresentValue"Identity: â TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PresentKeyUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : š QAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QSeqLen UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLenWAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasShape"Concat* axis : {UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_FloatNegInf"Constant* value* "€˙ : zTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScalarZero"Constant* value* " : p attn_maskWAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasShort"Identity: ƒ attn_maskVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_MaskKVSeqLen"Shape* start˙˙˙˙˙˙˙˙˙ : ‘ UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_MaskKVSeqLenYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingKVSeqLen"Sub: ” PAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Zero1D YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingKVSeqLenNAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Pads"Concat* axis : — UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_FloatNegInf WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasShortYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_FloatNegInfCast"CastLike: ŗ WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasShort NAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Pads YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_FloatNegInfCast RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NegOne1DRAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBias"Pad: ŋ RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBias]Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasCausalOrNot"Identity: Ė nonpad_kv_seqlen OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DZAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLenExpanded" Unsqueeze: ĩ RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLenTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLen0D"Squeeze: qPAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Zero0D"Constant* value*: : pOAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One0D"Constant* value*: : Ķ PAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Zero0D TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLen0D OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One0DOAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Range"Range:  OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Range ZAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVSeqLenExpandedYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMaskBool"Less: í YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMaskBool TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScalarZero UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_FloatNegInfZAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMaskFloat"Where: “ ZAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMaskFloat OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DWAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMask3D" Unsqueeze:  WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMask3D OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DWAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMask4D" Unsqueeze: œ ]Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasCausalOrNot WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PaddingMask4D[Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasCausalPad"Add: Å [Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasCausalPadSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasT"Cast* to : ‰ SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeadsSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NGQACond1"Equal: ° SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NGQACond1RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond1"Not: ‰ SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeadsUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_DivNumHeads"Div:  UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_DivNumHeadsVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_IDivNumHeads"Cast* to :  SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeads[Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_RemainderNumHeads"Mod: Œ [Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_RemainderNumHeads PAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Zero1DRAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond2"Equal: ‚ RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond1 RAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond2QAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond"And: Ū QAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_GQACond VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_IDivNumHeads OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_One1DWAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_InterleaveDim"Where: rOAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Two1D"Constant* value*: : ‹ TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PresentKey OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Two1DUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KUnsqueezed" Unsqueeze:  VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_PresentValue OAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_Two1DUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VUnsqueezed" Unsqueeze:   SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_BatchSize TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeads WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_InterleaveDim UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSizeVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KExpandShape"Concat* axis : Ž UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KUnsqueezed VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KExpandShapeSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KExpanded"Expand: Ÿ SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_BatchSize TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KVNumHeads WAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_InterleaveDim UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VHeadSizeVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VExpandShape"Concat* axis : Ž UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VUnsqueezed VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VExpandShapeSAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VExpanded"Expand: É SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_BatchSize SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKHeadSizeYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KAttentionShape"Concat* axis : Č SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_BatchSize SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QNumHeads UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_NewKVSeqLen SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VHeadSizeYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VAttentionShape"Concat* axis : – SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KExpanded YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KAttentionShapeYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KAttentionInput"Reshape: – SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VExpanded YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VAttentionShapeYAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VAttentionInput"Reshape: Ņ YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KAttentionInputTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KTranspose" Transpose* perm@@@@ : ‡ SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QReshaped VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorFQAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QScaled"Mul: ˆ TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KTranspose VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_ScaleFactorFQAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KScaled"Mul: ˆ QAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QScaled QAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_KScaledVAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeight"MatMul: Á VAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeightTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnCast"Cast* to : ’ TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnCast SAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnBiasT^Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeightWithBias"Add: Ë ^Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeightWithBias]Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeightSoftcap"Identity: É ]Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_QKAttnWeightSoftcapUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SoftmaxCast"Cast* to : ŋ UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SoftmaxCast[Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnWeightSoftmax"Softmax: Æ [Attention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_AttnWeightSoftmaxTAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SoftmaxOut"Cast* to : ’ TAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_SoftmaxOut YAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_VAttentionInputUAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_YPreReshape"MatMul: f UAttention_test_attention_4d_diff_heads_mask4d_padded_kv_expanded_function_YPreReshapeY"Identity:6test_attention_4d_diff_heads_mask4d_padded_kv_expandedZ Q     Z K     Z V      Z# attn_mask     Z nonpad_kv_seqlen  b Y      B test_data_set_0/000077500000000000000000000000001511334557700371435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_mask4d_padded_kv_expandedinput_0.pb000066400000000000000000000014201511334557700410410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_mask4d_padded_kv_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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QPAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‹ KQAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : c QRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QReshaped"Identity: c KRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KReshaped"Identity: c VRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VReshaped"Identity: Ë RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QReshapedRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads"Shape* start * end : Ė RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KReshapedSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads"Shape* start * end : Ė RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QReshapedSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSize"Shape* start * end : ž SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeF"Cast* to : Ë RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VReshapedRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VHeadSize"Shape* start * end : ĩ TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeFUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SqrtHeadSize"Sqrt: qNAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_One1D"Constant* value*: : uOAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_One1DF"Constant* value* "€? : rOAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_Zero1D"Constant* value*: : ‰ OAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_One1DF UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SqrtHeadSizeXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_CalculatedScale"Div: sOAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleF"Constant* value*"€? : ŧ XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_CalculatedScaleTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactor"Identity: ¸ TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: Ä XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorSqrtUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorF"Cast* to : ĩ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KReshapedSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PresentKey"Identity: xUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: : ˇ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VReshapedUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PresentValue"Identity: ā SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PresentKeyTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : — PAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QSeqLen TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLenVAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShape"Concat* axis : zTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : ySAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScalarZero"Constant* value* " : o attn_maskVAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShort"Identity: ˇ VAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasShortQAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBias"Identity: Ŋ QAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBias\Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Å \Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasCausalOrNotRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasT"Cast* to : † RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NGQACond1"Equal: Ž RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NGQACond1QAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond1"Not: † RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_DivNumHeads"Div: Ā TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_DivNumHeadsUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_IDivNumHeads"Cast* to : Œ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeadsZAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_RemainderNumHeads"Mod: ‰ ZAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_RemainderNumHeads OAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_Zero1DQAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond2"Equal: ˙ QAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond1 QAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond2PAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond"And: Ú PAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_GQACond UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_IDivNumHeads NAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_One1DVAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim"Where: qNAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_Two1D"Constant* value*: : ˆ SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PresentKey NAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_Two1DTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: Š UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_PresentValue NAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_Two1DTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: š RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_BatchSize SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads VAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KExpandShape"Concat* axis : ‹ TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KUnsqueezed UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KExpandShapeRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KExpanded"Expand: ™ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_BatchSize SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KVNumHeads VAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_InterleaveDim TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VHeadSizeUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VExpandShape"Concat* axis : ‹ TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VUnsqueezed UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VExpandShapeRAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VExpanded"Expand: Ä RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_BatchSize RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKHeadSizeXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KAttentionShape"Concat* axis : à RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_BatchSize RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QNumHeads TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_NewKVSeqLen RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VHeadSizeXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VAttentionShape"Concat* axis : “ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KExpanded XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KAttentionShapeXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KAttentionInput"Reshape: “ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VExpanded XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VAttentionShapeXAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VAttentionInput"Reshape: Ī XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KAttentionInputSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : „ RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QReshaped UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorFPAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QScaled"Mul: … SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KTranspose UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_ScaleFactorFPAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KScaled"Mul: … PAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QScaled PAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_KScaledUAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeight"MatMul: ŋ UAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnCast"Cast* to :  SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnCast RAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnBiasT]Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightWithBias"Add: É ]Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightWithBias\Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: Į \Attention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_QKAttnWeightSoftcapTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxCast"Cast* to : Ŋ TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxCastZAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: Ä ZAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_AttnWeightSoftmaxSAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxOut"Cast* to :  SAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_SoftmaxOut XAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_VAttentionInputTAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_YPreReshape"MatMul: e TAttention_test_attention_4d_diff_heads_sizes_attn_mask_expanded_function_YPreReshapeY"Identity:5test_attention_4d_diff_heads_sizes_attn_mask_expandedZ Q     Z K     Z V      Z attn_mask   b Y      B test_data_set_0/000077500000000000000000000000001511334557700371555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_attn_mask_expandedinput_0.pb000066400000000000000000000014201511334557700410530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_attn_mask_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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KNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ` QOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QReshaped"Identity: ` KOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KReshaped"Identity: ` VOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VReshaped"Identity: Å OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QReshapedOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads"Shape* start * end : Æ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KReshapedPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVNumHeads"Shape* start * end : Æ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QReshapedPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKHeadSize"Shape* start * end : ¸ PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKHeadSizeQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKHeadSizeF"Cast* to : Å OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VReshapedOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VHeadSize"Shape* start * end : ¯ QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKHeadSizeFRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SqrtHeadSize"Sqrt: nKAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One1D"Constant* value*: : rLAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One1DF"Constant* value* "€? : oLAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Zero1D"Constant* value*: : € LAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One1DF RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SqrtHeadSizeUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_CalculatedScale"Div: pLAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleF"Constant* value*"€? : ļ UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_CalculatedScaleQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactor"Identity: ˛ QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorSqrt"Sqrt: ž UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorSqrtRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorF"Cast* to : ¯ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KReshapedPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PresentKey"Identity: uRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PastKVSeqLen"Constant* value*: : ą OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VReshapedRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PresentValue"Identity: Ú PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PresentKeyQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Ž MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QSeqLen QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NewKVSeqLenSAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasShape"Concat* axis : wQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : vPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScalarZero"Constant* value* " : ¸ SAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasShapeNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBias"ConstantOfShape: mJAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Zero"Constant* value*: : lIAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One"Constant* value*: : ô JAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Zero JAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroNoDim"Squeeze: ō IAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One JAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_OneNoDim"Squeeze: † SAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasShape OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroNoDimTAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SequenceLength"Gather: Š SAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasShape NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_OneNoDimYAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_TotalSequenceLength"Gather: Đ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroNoDim TAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SequenceLength NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_OneNoDimNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow"Range: ú NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow IAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_OnePAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow2D" Unsqueeze: Õ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroNoDim YAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_TotalSequenceLength NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_OneNoDimNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeCol"Range: û NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeCol JAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ZeroPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeCol2D" Unsqueeze: ƒ PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow2D RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PastKVSeqLenTAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow2DPast"Add: ƒ TAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeRow2DPast PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RangeCol2DQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_BoolMaskTri"Less: Đ QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_BoolMaskTri QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_FloatNegInf PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScalarZeroMAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_MaskTri"Where:  NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBias MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_MaskTriYAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasCausalOrNot"Add: ŋ YAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasCausalOrNotOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasT"Cast* to : ũ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVNumHeadsOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NGQACond1"Equal: ¨ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NGQACond1NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond1"Not: ũ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVNumHeadsQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_DivNumHeads"Div: ē QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_DivNumHeadsRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_IDivNumHeads"Cast* to : ƒ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVNumHeadsWAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RemainderNumHeads"Mod: € WAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_RemainderNumHeads LAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Zero1DNAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond2"Equal: ö NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond1 NAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond2MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond"And: Î MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_GQACond RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_IDivNumHeads KAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_One1DSAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_InterleaveDim"Where: nKAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Two1D"Constant* value*: : ˙ PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PresentKey KAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_Two1DQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KUnsqueezed" Unsqueeze:  RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_PresentValue 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OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_BatchSize PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KVNumHeads SAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_InterleaveDim QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VHeadSizeRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VExpandShape"Concat* axis : ‚ QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VUnsqueezed RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VExpandShapeOAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VExpanded"Expand: ĩ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_BatchSize OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKHeadSizeUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KAttentionShape"Concat* axis : ´ OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_BatchSize OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_NewKVSeqLen OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VHeadSizeUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VAttentionShape"Concat* axis : Š OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KExpanded UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KAttentionShapeUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KAttentionInput"Reshape: Š OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VExpanded UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VAttentionShapeUAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VAttentionInput"Reshape: É UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KAttentionInputPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KTranspose" Transpose* perm@@@@ : û OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QReshaped RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorFMAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QScaled"Mul: ü PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KTranspose RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_ScaleFactorFMAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KScaled"Mul: ü MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QScaled MAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_KScaledRAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeight"MatMul: š RAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeightPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnCast"Cast* to : † PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnCast OAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnBiasTZAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeightWithBias"Add: à ZAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeightWithBiasYAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeightSoftcap"Identity: Á YAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_QKAttnWeightSoftcapQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SoftmaxCast"Cast* to : ˇ QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SoftmaxCastWAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnWeightSoftmax"Softmax: ž WAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_AttnWeightSoftmaxPAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SoftmaxOut"Cast* to : † PAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_SoftmaxOut UAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_VAttentionInputQAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_YPreReshape"MatMul: b QAttention_test_attention_4d_diff_heads_sizes_causal_expanded_function_YPreReshapeY"Identity:2test_attention_4d_diff_heads_sizes_causal_expandedZ Q     Z K     Z V      b Y      B test_data_set_0/000077500000000000000000000000001511334557700364445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_causal_expandedinput_0.pb000066400000000000000000000014201511334557700403420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_causal_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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Q ?€â3?†č>+•Ŋ>Á€?ÍÖĐ>Ũ?-­Ž> čĩ>ĩ'í>Đ´?x4?Dö?vú?Nęš>\?“œÃ>ŊûŽ>šhž>}­ō>ÛĶ?ü[?™†Ã>ĶŦ?7€ũ>ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?V`B?˜Ĩē>‚ú%?’r3?{ķ*>­Œ"?ŽŊŪ>0L?^?“L?čÂũ>L™>g Ō>_H?Æ[ō=k˜ß>„°?O ?š>$?ŌÜ??‘&?—ˇ>KÃÁ>¯Ö;?;v‡>eģŨ>ä3đ>4U?3P?Čß?1†?)mŗ>i>5“Ú>WÅW?ĄjQ?NžŅ=õ" >îŋ›>ÛUš=nmŲ>ŦfÜ=ŪÜŊ>›ĩ¨> ´5?ķ$á>`ų?ūV?oøŗ>k)>`>Š>\Ÿ>ečˆ>ĢÛ>ë˛?X‘Ō>^E)?XŅ ?{Ę ?sîi>ČĻ?KBî>"ŋ†>×Fũ>k†?_Ģ?pī?‘Ų? ;û>Œg>;?íŖũ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_expanded/000077500000000000000000000000001511334557700321315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_sizes_expanded/model.onnx000066400000000000000000000277461511334557700341550ustar00rootroot00000000000000  backend-test:Í_ p QHAttention_test_attention_4d_diff_heads_sizes_expanded_function_BatchSize"Shape* start * end : € QFAttention_test_attention_4d_diff_heads_sizes_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KGAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Y QHAttention_test_attention_4d_diff_heads_sizes_expanded_function_QReshaped"Identity: Y KHAttention_test_attention_4d_diff_heads_sizes_expanded_function_KReshaped"Identity: Y VHAttention_test_attention_4d_diff_heads_sizes_expanded_function_VReshaped"Identity: ˇ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QReshapedHAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads"Shape* start * end : ¸ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_KReshapedIAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeads"Shape* start * end : ¸ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QReshapedIAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSize"Shape* start * end : Ē IAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSizeJAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSizeF"Cast* to : ˇ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_VReshapedHAttention_test_attention_4d_diff_heads_sizes_expanded_function_VHeadSize"Shape* start * end : Ą JAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSizeFKAttention_test_attention_4d_diff_heads_sizes_expanded_function_SqrtHeadSize"Sqrt: gDAttention_test_attention_4d_diff_heads_sizes_expanded_function_One1D"Constant* value*: : kEAttention_test_attention_4d_diff_heads_sizes_expanded_function_One1DF"Constant* value* "€? : hEAttention_test_attention_4d_diff_heads_sizes_expanded_function_Zero1D"Constant* value*: : ë EAttention_test_attention_4d_diff_heads_sizes_expanded_function_One1DF KAttention_test_attention_4d_diff_heads_sizes_expanded_function_SqrtHeadSizeNAttention_test_attention_4d_diff_heads_sizes_expanded_function_CalculatedScale"Div: iEAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleF"Constant* value*"€? : ¨ NAttention_test_attention_4d_diff_heads_sizes_expanded_function_CalculatedScaleJAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactor"Identity: ¤ JAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorNAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorSqrt"Sqrt: ° NAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorSqrtKAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorF"Cast* to : Ą HAttention_test_attention_4d_diff_heads_sizes_expanded_function_KReshapedIAttention_test_attention_4d_diff_heads_sizes_expanded_function_PresentKey"Identity: nKAttention_test_attention_4d_diff_heads_sizes_expanded_function_PastKVSeqLen"Constant* value*: : Ŗ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_VReshapedKAttention_test_attention_4d_diff_heads_sizes_expanded_function_PresentValue"Identity: Ė IAttention_test_attention_4d_diff_heads_sizes_expanded_function_PresentKeyJAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ų FAttention_test_attention_4d_diff_heads_sizes_expanded_function_QSeqLen JAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLenLAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasShape"Concat* axis : pJAttention_test_attention_4d_diff_heads_sizes_expanded_function_FloatNegInf"Constant* value* "€˙ : oIAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScalarZero"Constant* value* " : Ē LAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasShapeGAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBias"ConstantOfShape: Š GAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasRAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasCausalOrNot"Identity: ą RAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasCausalOrNotHAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasT"Cast* to : č HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeadsHAttention_test_attention_4d_diff_heads_sizes_expanded_function_NGQACond1"Equal: š HAttention_test_attention_4d_diff_heads_sizes_expanded_function_NGQACond1GAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond1"Not: č HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeadsJAttention_test_attention_4d_diff_heads_sizes_expanded_function_DivNumHeads"Div: Ŧ JAttention_test_attention_4d_diff_heads_sizes_expanded_function_DivNumHeadsKAttention_test_attention_4d_diff_heads_sizes_expanded_function_IDivNumHeads"Cast* to : î HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeadsPAttention_test_attention_4d_diff_heads_sizes_expanded_function_RemainderNumHeads"Mod: ë PAttention_test_attention_4d_diff_heads_sizes_expanded_function_RemainderNumHeads EAttention_test_attention_4d_diff_heads_sizes_expanded_function_Zero1DGAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond2"Equal: á GAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond1 GAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond2FAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond"And: ˛ FAttention_test_attention_4d_diff_heads_sizes_expanded_function_GQACond KAttention_test_attention_4d_diff_heads_sizes_expanded_function_IDivNumHeads DAttention_test_attention_4d_diff_heads_sizes_expanded_function_One1DLAttention_test_attention_4d_diff_heads_sizes_expanded_function_InterleaveDim"Where: gDAttention_test_attention_4d_diff_heads_sizes_expanded_function_Two1D"Constant* value*: : ę IAttention_test_attention_4d_diff_heads_sizes_expanded_function_PresentKey DAttention_test_attention_4d_diff_heads_sizes_expanded_function_Two1DJAttention_test_attention_4d_diff_heads_sizes_expanded_function_KUnsqueezed" Unsqueeze: ė KAttention_test_attention_4d_diff_heads_sizes_expanded_function_PresentValue DAttention_test_attention_4d_diff_heads_sizes_expanded_function_Two1DJAttention_test_attention_4d_diff_heads_sizes_expanded_function_VUnsqueezed" Unsqueeze: Ū HAttention_test_attention_4d_diff_heads_sizes_expanded_function_BatchSize IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeads LAttention_test_attention_4d_diff_heads_sizes_expanded_function_InterleaveDim JAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLen IAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSizeKAttention_test_attention_4d_diff_heads_sizes_expanded_function_KExpandShape"Concat* axis : í JAttention_test_attention_4d_diff_heads_sizes_expanded_function_KUnsqueezed KAttention_test_attention_4d_diff_heads_sizes_expanded_function_KExpandShapeHAttention_test_attention_4d_diff_heads_sizes_expanded_function_KExpanded"Expand: Ũ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_BatchSize IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KVNumHeads LAttention_test_attention_4d_diff_heads_sizes_expanded_function_InterleaveDim JAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLen HAttention_test_attention_4d_diff_heads_sizes_expanded_function_VHeadSizeKAttention_test_attention_4d_diff_heads_sizes_expanded_function_VExpandShape"Concat* axis : í JAttention_test_attention_4d_diff_heads_sizes_expanded_function_VUnsqueezed KAttention_test_attention_4d_diff_heads_sizes_expanded_function_VExpandShapeHAttention_test_attention_4d_diff_heads_sizes_expanded_function_VExpanded"Expand: ’ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_BatchSize HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads JAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLen IAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKHeadSizeNAttention_test_attention_4d_diff_heads_sizes_expanded_function_KAttentionShape"Concat* axis : ‘ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_BatchSize HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QNumHeads JAttention_test_attention_4d_diff_heads_sizes_expanded_function_NewKVSeqLen HAttention_test_attention_4d_diff_heads_sizes_expanded_function_VHeadSizeNAttention_test_attention_4d_diff_heads_sizes_expanded_function_VAttentionShape"Concat* axis : õ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_KExpanded NAttention_test_attention_4d_diff_heads_sizes_expanded_function_KAttentionShapeNAttention_test_attention_4d_diff_heads_sizes_expanded_function_KAttentionInput"Reshape: õ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_VExpanded NAttention_test_attention_4d_diff_heads_sizes_expanded_function_VAttentionShapeNAttention_test_attention_4d_diff_heads_sizes_expanded_function_VAttentionInput"Reshape: ģ NAttention_test_attention_4d_diff_heads_sizes_expanded_function_KAttentionInputIAttention_test_attention_4d_diff_heads_sizes_expanded_function_KTranspose" Transpose* perm@@@@ : æ HAttention_test_attention_4d_diff_heads_sizes_expanded_function_QReshaped KAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorFFAttention_test_attention_4d_diff_heads_sizes_expanded_function_QScaled"Mul: į IAttention_test_attention_4d_diff_heads_sizes_expanded_function_KTranspose KAttention_test_attention_4d_diff_heads_sizes_expanded_function_ScaleFactorFFAttention_test_attention_4d_diff_heads_sizes_expanded_function_KScaled"Mul: į FAttention_test_attention_4d_diff_heads_sizes_expanded_function_QScaled FAttention_test_attention_4d_diff_heads_sizes_expanded_function_KScaledKAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeight"MatMul: Ģ KAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeightIAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnCast"Cast* to : ņ IAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnCast HAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnBiasTSAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeightWithBias"Add: ĩ SAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeightWithBiasRAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeightSoftcap"Identity: ŗ RAttention_test_attention_4d_diff_heads_sizes_expanded_function_QKAttnWeightSoftcapJAttention_test_attention_4d_diff_heads_sizes_expanded_function_SoftmaxCast"Cast* to : Š JAttention_test_attention_4d_diff_heads_sizes_expanded_function_SoftmaxCastPAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnWeightSoftmax"Softmax: ° PAttention_test_attention_4d_diff_heads_sizes_expanded_function_AttnWeightSoftmaxIAttention_test_attention_4d_diff_heads_sizes_expanded_function_SoftmaxOut"Cast* to : ņ IAttention_test_attention_4d_diff_heads_sizes_expanded_function_SoftmaxOut NAttention_test_attention_4d_diff_heads_sizes_expanded_function_VAttentionInputJAttention_test_attention_4d_diff_heads_sizes_expanded_function_YPreReshape"MatMul: [ JAttention_test_attention_4d_diff_heads_sizes_expanded_function_YPreReshapeY"Identity:+test_attention_4d_diff_heads_sizes_expandedZ Q     Z K     Z V      b Y      B 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RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_SqrtHeadSizeUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_CalculatedScale"Div: pLAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleF"Constant* value*" ×#< : ­ LAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFQAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactor"Identity: ˛ QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorSqrt"Sqrt: ž UAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorSqrtRAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorF"Cast* to : ¯ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KReshapedPAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_PresentKey"Identity: 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OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QNumHeads PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KVNumHeadsQAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_DivNumHeads"Div: ē QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_DivNumHeadsRAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_IDivNumHeads"Cast* to : ƒ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QNumHeads PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KVNumHeadsWAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_RemainderNumHeads"Mod: € WAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_RemainderNumHeads LAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_Zero1DNAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_GQACond2"Equal: ö NAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_GQACond1 NAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_GQACond2MAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_GQACond"And: Î MAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_GQACond RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_IDivNumHeads KAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_One1DSAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_InterleaveDim"Where: nKAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_Two1D"Constant* value*: : ˙ PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_PresentKey KAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_Two1DQAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KUnsqueezed" Unsqueeze:  RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_PresentValue KAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_Two1DQAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VUnsqueezed" Unsqueeze: ˆ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_BatchSize PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KVNumHeads SAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_InterleaveDim QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKHeadSizeRAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KExpandShape"Concat* axis : ‚ QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KUnsqueezed RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KExpandShapeOAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KExpanded"Expand: ‡ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_BatchSize PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KVNumHeads SAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_InterleaveDim QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VHeadSizeRAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VExpandShape"Concat* axis : ‚ QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VUnsqueezed RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VExpandShapeOAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VExpanded"Expand: ĩ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_BatchSize OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKHeadSizeUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KAttentionShape"Concat* axis : ´ OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_BatchSize OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_NewKVSeqLen OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VHeadSizeUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VAttentionShape"Concat* axis : Š OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KExpanded UAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KAttentionShapeUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KAttentionInput"Reshape: Š OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VExpanded UAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VAttentionShapeUAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_VAttentionInput"Reshape: É UAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KAttentionInputPAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : û OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QReshaped RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorFMAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QScaled"Mul: ü PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KTranspose RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_ScaleFactorFMAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KScaled"Mul: ü MAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QScaled MAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_KScaledRAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeight"MatMul: š RAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightPAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnCast"Cast* to : † PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnCast OAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_AttnBiasTZAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightWithBias"Add: à ZAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightWithBiasYAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightSoftcap"Identity: Á YAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_QKAttnWeightSoftcapQAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_SoftmaxCast"Cast* to : ˇ QAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_SoftmaxCastWAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_AttnWeightSoftmax"Softmax: ž WAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_AttnWeightSoftmaxPAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_SoftmaxOut"Cast* to : † PAttention_test_attention_4d_diff_heads_sizes_scaled_expanded_function_SoftmaxOut 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RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLenTAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasShape"Concat* axis : xRAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : wQAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_ScalarZero"Constant* value* " : ē TAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasShapeOAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBias"ConstantOfShape: š OAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasZAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasCausalOrNot"Identity: Á ZAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasCausalOrNotPAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_AttnBiasT"Cast* to : € PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsPAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NGQACond1"Equal: Ē PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NGQACond1OAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond1"Not: € PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsRAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_DivNumHeads"Div: ŧ RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_DivNumHeadsSAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_IDivNumHeads"Cast* to : † PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QNumHeads QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KVNumHeadsXAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_RemainderNumHeads"Mod: ƒ XAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_RemainderNumHeads MAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_Zero1DOAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond2"Equal: ų OAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond1 OAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond2NAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond"And: Ō NAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_GQACond SAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_IDivNumHeads LAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_One1DTAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_InterleaveDim"Where: oLAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_Two1D"Constant* value*: : ‚ QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_PresentKey LAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_Two1DRAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KUnsqueezed" Unsqueeze: „ SAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_PresentValue LAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_Two1DRAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VUnsqueezed" Unsqueeze: Ž PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_BatchSize QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KVNumHeads TAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_InterleaveDim RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeSAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KExpandShape"Concat* axis : … RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KUnsqueezed SAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KExpandShapePAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KExpanded"Expand:  PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_BatchSize QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KVNumHeads TAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_InterleaveDim RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VHeadSizeSAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VExpandShape"Concat* axis : … RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VUnsqueezed SAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VExpandShapePAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VExpanded"Expand: ē PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_BatchSize PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QNumHeads RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen QAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QKHeadSizeVAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KAttentionShape"Concat* axis : š PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_BatchSize PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_QNumHeads RAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_NewKVSeqLen PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VHeadSizeVAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VAttentionShape"Concat* axis :  PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KExpanded VAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KAttentionShapeVAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_KAttentionInput"Reshape:  PAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VExpanded VAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VAttentionShapeVAttention_test_attention_4d_diff_heads_sizes_softcap_expanded_function_VAttentionInput"Reshape: Ë 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?test_attention_4d_diff_heads_with_past_and_present_expanded/000077500000000000000000000000001511334557700353015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000354521511334557700373160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_expanded  backend-test:‘v € QXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_BatchSize"Shape* start * end :  QVAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‘ KWAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : i QXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QReshaped"Identity: i KXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KReshaped"Identity: i VXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VReshaped"Identity: × XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QReshapedXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads"Shape* start * end : Ø XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KReshapedYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end : Ø XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QReshapedYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : Ę YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : × XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VReshapedXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : Á ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSizeF[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: wTAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_One1D"Constant* value*: : {UAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : xUAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_Zero1D"Constant* value*: : › UAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_One1DF [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SqrtHeadSize^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_CalculatedScale"Div: yUAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : Č ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_CalculatedScaleZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactor"Identity: Ä ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactor^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt: Đ ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactorSqrt[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Ö past_key XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KReshapedYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentKey"Concat* axis : œ past_key[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : t YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ú past_value XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VReshaped[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentValue"Concat* axis : x [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentValue present_value"Identity: ė YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentKeyZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Š VAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QSeqLen ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen\Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : €ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : u attn_mask\Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasShort"Identity: à \Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasShortWAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBias"Identity: É WAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasbAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: Ņ bAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasCausalOrNotXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ˜ XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NGQACond1"Equal: ē XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NGQACond1WAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond1"Not: ˜ XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeadsZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_DivNumHeads"Div: Ė ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_DivNumHeads[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ž XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeads`Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_RemainderNumHeads"Mod: › `Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_RemainderNumHeads UAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_Zero1DWAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond2"Equal: ‘ WAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond1 WAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond2VAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond"And: ō VAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_GQACond [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_IDivNumHeads TAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_One1D\Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_InterleaveDim"Where: wTAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_Two1D"Constant* value*: : š YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentKey TAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_Two1DZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: œ [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_PresentValue TAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_Two1DZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ž XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_BatchSize YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeads \Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_InterleaveDim ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSize[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KExpandShape"Concat* axis :  ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KUnsqueezed [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KExpandShapeXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KExpanded"Expand: Ŋ XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_BatchSize YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KVNumHeads \Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_InterleaveDim ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VHeadSize[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VExpandShape"Concat* axis :  ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VUnsqueezed [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VExpandShapeXAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VExpanded"Expand: â XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_BatchSize XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKHeadSize^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : á XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_BatchSize XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QNumHeads ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_NewKVSeqLen XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VHeadSize^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VAttentionShape"Concat* axis : Ĩ XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KExpanded ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KAttentionShape^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KAttentionInput"Reshape: Ĩ XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VExpanded ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VAttentionShape^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VAttentionInput"Reshape: Û ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KAttentionInputYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ : – XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QReshaped [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactorFVAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QScaled"Mul: — YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KTranspose [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_ScaleFactorFVAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KScaled"Mul: — VAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QScaled VAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_KScaled[Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeight"MatMul: Ë [Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnCast"Cast* to : Ą YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnCast XAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnBiasTcAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: Õ cAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightWithBiasbAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Ķ bAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_QKAttnWeightSoftcapZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : É ZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SoftmaxCast`Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax: Đ `Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_AttnWeightSoftmaxYAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : Ą YAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_SoftmaxOut ^Attention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_VAttentionInputZAttention_test_attention_4d_diff_heads_with_past_and_present_expanded_function_YPreReshape"MatMul: k 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded/000077500000000000000000000000001511334557700365435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000374541511334557700405640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded  backend-test:“~ ‡ Q_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_BatchSize"Shape* start * end : — Q]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˜ K^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : p Q_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QReshaped"Identity: p K_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KReshaped"Identity: p V_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VReshaped"Identity: å _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QReshaped_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads"Shape* start * end : æ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeads"Shape* start * end : æ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSize"Shape* start * end : Ø `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSizeaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSizeF"Cast* to : å _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VReshaped_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VHeadSize"Shape* start * end : Ī aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSizeFbAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SqrtHeadSize"Sqrt: ~[Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_One1D"Constant* value*: : ‚\Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_One1DF"Constant* value* "€? : \Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_Zero1D"Constant* value*: : ° \Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_One1DF bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SqrtHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_CalculatedScale"Div: €\Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleF"Constant* value*"€? : Ö eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_CalculatedScaleaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactor"Identity: Ō aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactoreAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactorSqrt"Sqrt: Ū eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactorSqrtbAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactorF"Cast* to : ä past_key _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentKey"Concat* axis : Ŗ past_keybAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentKey present_key"Identity: č past_value _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VReshapedbAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentValue"Concat* axis :  bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentValue present_value"Identity: ú `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentKeyaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ž ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QSeqLen aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLencAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasShape"Concat* axis : ‡aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_FloatNegInf"Constant* value* "€˙ : †`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScalarZero"Constant* value* " : | attn_maskcAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasShort"Identity: Ņ cAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasShort^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBias"Identity: × ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasiAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasCausalOrNot"Identity: ß iAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasCausalOrNot_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasT"Cast* to : ­ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeads_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NGQACond1"Equal: Č _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NGQACond1^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond1"Not: ­ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeadsaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_DivNumHeads"Div: Ú aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_DivNumHeadsbAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_IDivNumHeads"Cast* to : ŗ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeadsgAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_RemainderNumHeads"Mod: ° gAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_RemainderNumHeads \Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_Zero1D^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond2"Equal: Ļ ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond1 ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond2]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond"And: Ž ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_GQACond bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_IDivNumHeads [Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_One1DcAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_InterleaveDim"Where: ~[Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_Two1D"Constant* value*: : ¯ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentKey [Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_Two1DaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KUnsqueezed" Unsqueeze: ą bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_PresentValue [Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_Two1DaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VUnsqueezed" Unsqueeze: č _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_BatchSize `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeads cAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_InterleaveDim aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLen `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSizebAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KExpandShape"Concat* axis : ˛ aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KUnsqueezed bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KExpandShape_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KExpanded"Expand: į _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_BatchSize `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KVNumHeads cAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_InterleaveDim aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLen _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VHeadSizebAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VExpandShape"Concat* axis : ˛ aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VUnsqueezed bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VExpandShape_Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VExpanded"Expand: … _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_BatchSize _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLen `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KAttentionShape"Concat* axis : „ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_BatchSize _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QNumHeads aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_NewKVSeqLen _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VAttentionShape"Concat* axis : ē _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KExpanded eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KAttentionShapeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KAttentionInput"Reshape: ē _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VExpanded eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VAttentionShapeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VAttentionInput"Reshape: é eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KAttentionInput`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KTranspose" Transpose* perm@@@@ : Ģ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QReshaped bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactorF]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QScaled"Mul: Ŧ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KTranspose bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_ScaleFactorF]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KScaled"Mul: Ŧ ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QScaled ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_KScaledbAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeight"MatMul: Ų bAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeight`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnCast"Cast* to : ļ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnCast _Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnBiasTjAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeightWithBias"Add: ã jAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeightWithBiasiAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeightSoftcap"Identity: á iAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_QKAttnWeightSoftcapaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SoftmaxCast"Cast* to : × aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SoftmaxCastgAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnWeightSoftmax"Softmax: Ū gAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_AttnWeightSoftmax`Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SoftmaxOut"Cast* to : ļ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_SoftmaxOut eAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_VAttentionInputaAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_YPreReshape"MatMul: r aAttention_test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded_function_YPreReshapeY"Identity:Btest_attention_4d_diff_heads_with_past_and_present_mask3d_expandedZ Q     Z K     Z V      Z# attn_mask     Z" past_key     Z$ past_value      b Y      b% present_key     b' present_value      B test_data_set_0/000077500000000000000000000000001511334557700416055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask3d_expandedinput_0.pb000066400000000000000000000014201511334557700435030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700435030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask3d_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded/000077500000000000000000000000001511334557700365445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000374541511334557700405650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded  backend-test:“~ ‡ Q_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_BatchSize"Shape* start * end : — Q]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˜ K^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : p Q_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QReshaped"Identity: p K_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KReshaped"Identity: p V_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VReshaped"Identity: å _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QReshaped_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads"Shape* start * end : æ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeads"Shape* start * end : æ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSize"Shape* start * end : Ø `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSizeaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSizeF"Cast* to : å _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VReshaped_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VHeadSize"Shape* start * end : Ī aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSizeFbAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SqrtHeadSize"Sqrt: ~[Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_One1D"Constant* value*: : ‚\Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_One1DF"Constant* value* "€? : \Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_Zero1D"Constant* value*: : ° \Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_One1DF bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SqrtHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_CalculatedScale"Div: €\Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleF"Constant* value*"€? : Ö eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_CalculatedScaleaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactor"Identity: Ō aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactoreAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactorSqrt"Sqrt: Ū eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactorSqrtbAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactorF"Cast* to : ä past_key _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KReshaped`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentKey"Concat* axis : Ŗ past_keybAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentKey present_key"Identity: č past_value _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VReshapedbAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentValue"Concat* axis :  bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentValue present_value"Identity: ú `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentKeyaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ž ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QSeqLen aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLencAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasShape"Concat* axis : ‡aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_FloatNegInf"Constant* value* "€˙ : †`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScalarZero"Constant* value* " : | attn_maskcAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasShort"Identity: Ņ cAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasShort^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBias"Identity: × ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasiAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasCausalOrNot"Identity: ß iAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasCausalOrNot_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasT"Cast* to : ­ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeads_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NGQACond1"Equal: Č _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NGQACond1^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond1"Not: ­ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeadsaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_DivNumHeads"Div: Ú aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_DivNumHeadsbAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_IDivNumHeads"Cast* to : ŗ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeadsgAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_RemainderNumHeads"Mod: ° gAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_RemainderNumHeads \Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_Zero1D^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond2"Equal: Ļ ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond1 ^Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond2]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond"And: Ž ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_GQACond bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_IDivNumHeads [Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_One1DcAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_InterleaveDim"Where: ~[Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_Two1D"Constant* value*: : ¯ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentKey [Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_Two1DaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KUnsqueezed" Unsqueeze: ą bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_PresentValue [Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_Two1DaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VUnsqueezed" Unsqueeze: č _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_BatchSize `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeads cAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_InterleaveDim aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLen `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSizebAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KExpandShape"Concat* axis : ˛ aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KUnsqueezed bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KExpandShape_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KExpanded"Expand: į _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_BatchSize `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KVNumHeads cAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_InterleaveDim aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLen _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VHeadSizebAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VExpandShape"Concat* axis : ˛ aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VUnsqueezed bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VExpandShape_Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VExpanded"Expand: … _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_BatchSize _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLen `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KAttentionShape"Concat* axis : „ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_BatchSize _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QNumHeads aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_NewKVSeqLen _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VHeadSizeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VAttentionShape"Concat* axis : ē _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KExpanded eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KAttentionShapeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KAttentionInput"Reshape: ē _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VExpanded eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VAttentionShapeeAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VAttentionInput"Reshape: é eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KAttentionInput`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KTranspose" Transpose* perm@@@@ : Ģ _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QReshaped bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactorF]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QScaled"Mul: Ŧ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KTranspose bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_ScaleFactorF]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KScaled"Mul: Ŧ ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QScaled ]Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_KScaledbAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeight"MatMul: Ų bAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeight`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnCast"Cast* to : ļ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnCast _Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnBiasTjAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeightWithBias"Add: ã jAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeightWithBiasiAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeightSoftcap"Identity: á iAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_QKAttnWeightSoftcapaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SoftmaxCast"Cast* to : × aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SoftmaxCastgAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnWeightSoftmax"Softmax: Ū gAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_AttnWeightSoftmax`Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SoftmaxOut"Cast* to : ļ `Attention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_SoftmaxOut eAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_VAttentionInputaAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_YPreReshape"MatMul: r aAttention_test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded_function_YPreReshapeY"Identity:Btest_attention_4d_diff_heads_with_past_and_present_mask4d_expandedZ Q     Z K     Z V      Z# attn_mask     Z" past_key     Z$ past_value      b Y      b% present_key     b' present_value      B test_data_set_0/000077500000000000000000000000001511334557700416065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask4d_expandedinput_0.pb000066400000000000000000000014201511334557700435040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000022201511334557700435040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_diff_heads_with_past_and_present_mask4d_expanded/test_data_set_0BKJ€ aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? 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Ņ>o;Á>‹2O?Yƒ5?9Ot?ũ0´>]Åe?’E?]ˇ>w%?qŋ“>ŦØ_?K@æ=eˆY>m;>mYÎ>–Į>?hã?ļ°ų>:AÎŲ>{(‚=W@U>_ąn?Y‘\>ŧ[?lŠM?;÷">đ?"āė=áV:?ē0#?5ÛO?äqõ>x4j?"J=}õ•>° 7?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_expanded/000077500000000000000000000000001511334557700265605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_expanded/model.onnx000066400000000000000000000231631511334557700305710ustar00rootroot00000000000000  backend-test:ÚL _ Q7Attention_test_attention_4d_expanded_function_BatchSize"Shape* start * end : o Q5Attention_test_attention_4d_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : p K6Attention_test_attention_4d_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : H Q7Attention_test_attention_4d_expanded_function_QReshaped"Identity: H K7Attention_test_attention_4d_expanded_function_KReshaped"Identity: H V7Attention_test_attention_4d_expanded_function_VReshaped"Identity: • 7Attention_test_attention_4d_expanded_function_QReshaped7Attention_test_attention_4d_expanded_function_QNumHeads"Shape* start * end : – 7Attention_test_attention_4d_expanded_function_KReshaped8Attention_test_attention_4d_expanded_function_KVNumHeads"Shape* start * end : – 7Attention_test_attention_4d_expanded_function_QReshaped8Attention_test_attention_4d_expanded_function_QKHeadSize"Shape* start * end : ˆ 8Attention_test_attention_4d_expanded_function_QKHeadSize9Attention_test_attention_4d_expanded_function_QKHeadSizeF"Cast* to : • 7Attention_test_attention_4d_expanded_function_VReshaped7Attention_test_attention_4d_expanded_function_VHeadSize"Shape* start * end :  9Attention_test_attention_4d_expanded_function_QKHeadSizeF:Attention_test_attention_4d_expanded_function_SqrtHeadSize"Sqrt: V3Attention_test_attention_4d_expanded_function_One1D"Constant* value*: : Z4Attention_test_attention_4d_expanded_function_One1DF"Constant* value* "€? : W4Attention_test_attention_4d_expanded_function_Zero1D"Constant* value*: : ¸ 4Attention_test_attention_4d_expanded_function_One1DF :Attention_test_attention_4d_expanded_function_SqrtHeadSize=Attention_test_attention_4d_expanded_function_CalculatedScale"Div: X4Attention_test_attention_4d_expanded_function_ScaleF"Constant* value*"€? : † =Attention_test_attention_4d_expanded_function_CalculatedScale9Attention_test_attention_4d_expanded_function_ScaleFactor"Identity: ‚ 9Attention_test_attention_4d_expanded_function_ScaleFactor=Attention_test_attention_4d_expanded_function_ScaleFactorSqrt"Sqrt: Ž =Attention_test_attention_4d_expanded_function_ScaleFactorSqrt:Attention_test_attention_4d_expanded_function_ScaleFactorF"Cast* to :  7Attention_test_attention_4d_expanded_function_KReshaped8Attention_test_attention_4d_expanded_function_PresentKey"Identity: ]:Attention_test_attention_4d_expanded_function_PastKVSeqLen"Constant* value*: :  7Attention_test_attention_4d_expanded_function_VReshaped:Attention_test_attention_4d_expanded_function_PresentValue"Identity: Ē 8Attention_test_attention_4d_expanded_function_PresentKey9Attention_test_attention_4d_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Æ 5Attention_test_attention_4d_expanded_function_QSeqLen 9Attention_test_attention_4d_expanded_function_NewKVSeqLen;Attention_test_attention_4d_expanded_function_AttnBiasShape"Concat* axis : _9Attention_test_attention_4d_expanded_function_FloatNegInf"Constant* value* "€˙ : ^8Attention_test_attention_4d_expanded_function_ScalarZero"Constant* value* " : ˆ ;Attention_test_attention_4d_expanded_function_AttnBiasShape6Attention_test_attention_4d_expanded_function_AttnBias"ConstantOfShape: ‡ 6Attention_test_attention_4d_expanded_function_AttnBiasAAttention_test_attention_4d_expanded_function_AttnBiasCausalOrNot"Identity:  AAttention_test_attention_4d_expanded_function_AttnBiasCausalOrNot7Attention_test_attention_4d_expanded_function_AttnBiasT"Cast* to : ĩ 7Attention_test_attention_4d_expanded_function_QNumHeads 8Attention_test_attention_4d_expanded_function_KVNumHeads7Attention_test_attention_4d_expanded_function_NGQACond1"Equal: x 7Attention_test_attention_4d_expanded_function_NGQACond16Attention_test_attention_4d_expanded_function_GQACond1"Not: ĩ 7Attention_test_attention_4d_expanded_function_QNumHeads 8Attention_test_attention_4d_expanded_function_KVNumHeads9Attention_test_attention_4d_expanded_function_DivNumHeads"Div: Š 9Attention_test_attention_4d_expanded_function_DivNumHeads:Attention_test_attention_4d_expanded_function_IDivNumHeads"Cast* to : ģ 7Attention_test_attention_4d_expanded_function_QNumHeads 8Attention_test_attention_4d_expanded_function_KVNumHeads?Attention_test_attention_4d_expanded_function_RemainderNumHeads"Mod: ¸ ?Attention_test_attention_4d_expanded_function_RemainderNumHeads 4Attention_test_attention_4d_expanded_function_Zero1D6Attention_test_attention_4d_expanded_function_GQACond2"Equal: Ž 6Attention_test_attention_4d_expanded_function_GQACond1 6Attention_test_attention_4d_expanded_function_GQACond25Attention_test_attention_4d_expanded_function_GQACond"And: î 5Attention_test_attention_4d_expanded_function_GQACond :Attention_test_attention_4d_expanded_function_IDivNumHeads 3Attention_test_attention_4d_expanded_function_One1D;Attention_test_attention_4d_expanded_function_InterleaveDim"Where: V3Attention_test_attention_4d_expanded_function_Two1D"Constant* value*: : ˇ 8Attention_test_attention_4d_expanded_function_PresentKey 3Attention_test_attention_4d_expanded_function_Two1D9Attention_test_attention_4d_expanded_function_KUnsqueezed" Unsqueeze: š :Attention_test_attention_4d_expanded_function_PresentValue 3Attention_test_attention_4d_expanded_function_Two1D9Attention_test_attention_4d_expanded_function_VUnsqueezed" Unsqueeze: ø 7Attention_test_attention_4d_expanded_function_BatchSize 8Attention_test_attention_4d_expanded_function_KVNumHeads ;Attention_test_attention_4d_expanded_function_InterleaveDim 9Attention_test_attention_4d_expanded_function_NewKVSeqLen 8Attention_test_attention_4d_expanded_function_QKHeadSize:Attention_test_attention_4d_expanded_function_KExpandShape"Concat* axis : ē 9Attention_test_attention_4d_expanded_function_KUnsqueezed :Attention_test_attention_4d_expanded_function_KExpandShape7Attention_test_attention_4d_expanded_function_KExpanded"Expand: ÷ 7Attention_test_attention_4d_expanded_function_BatchSize 8Attention_test_attention_4d_expanded_function_KVNumHeads ;Attention_test_attention_4d_expanded_function_InterleaveDim 9Attention_test_attention_4d_expanded_function_NewKVSeqLen 7Attention_test_attention_4d_expanded_function_VHeadSize:Attention_test_attention_4d_expanded_function_VExpandShape"Concat* axis : ē 9Attention_test_attention_4d_expanded_function_VUnsqueezed :Attention_test_attention_4d_expanded_function_VExpandShape7Attention_test_attention_4d_expanded_function_VExpanded"Expand: Ŋ 7Attention_test_attention_4d_expanded_function_BatchSize 7Attention_test_attention_4d_expanded_function_QNumHeads 9Attention_test_attention_4d_expanded_function_NewKVSeqLen 8Attention_test_attention_4d_expanded_function_QKHeadSize=Attention_test_attention_4d_expanded_function_KAttentionShape"Concat* axis : ŧ 7Attention_test_attention_4d_expanded_function_BatchSize 7Attention_test_attention_4d_expanded_function_QNumHeads 9Attention_test_attention_4d_expanded_function_NewKVSeqLen 7Attention_test_attention_4d_expanded_function_VHeadSize=Attention_test_attention_4d_expanded_function_VAttentionShape"Concat* axis :  7Attention_test_attention_4d_expanded_function_KExpanded =Attention_test_attention_4d_expanded_function_KAttentionShape=Attention_test_attention_4d_expanded_function_KAttentionInput"Reshape:  7Attention_test_attention_4d_expanded_function_VExpanded =Attention_test_attention_4d_expanded_function_VAttentionShape=Attention_test_attention_4d_expanded_function_VAttentionInput"Reshape: ™ =Attention_test_attention_4d_expanded_function_KAttentionInput8Attention_test_attention_4d_expanded_function_KTranspose" Transpose* perm@@@@ : ŗ 7Attention_test_attention_4d_expanded_function_QReshaped :Attention_test_attention_4d_expanded_function_ScaleFactorF5Attention_test_attention_4d_expanded_function_QScaled"Mul: ´ 8Attention_test_attention_4d_expanded_function_KTranspose :Attention_test_attention_4d_expanded_function_ScaleFactorF5Attention_test_attention_4d_expanded_function_KScaled"Mul: ´ 5Attention_test_attention_4d_expanded_function_QScaled 5Attention_test_attention_4d_expanded_function_KScaled:Attention_test_attention_4d_expanded_function_QKAttnWeight"MatMul: ‰ :Attention_test_attention_4d_expanded_function_QKAttnWeight8Attention_test_attention_4d_expanded_function_QKAttnCast"Cast* to : ž 8Attention_test_attention_4d_expanded_function_QKAttnCast 7Attention_test_attention_4d_expanded_function_AttnBiasTBAttention_test_attention_4d_expanded_function_QKAttnWeightWithBias"Add: “ BAttention_test_attention_4d_expanded_function_QKAttnWeightWithBiasAAttention_test_attention_4d_expanded_function_QKAttnWeightSoftcap"Identity: ‘ AAttention_test_attention_4d_expanded_function_QKAttnWeightSoftcap9Attention_test_attention_4d_expanded_function_SoftmaxCast"Cast* to : ‡ 9Attention_test_attention_4d_expanded_function_SoftmaxCast?Attention_test_attention_4d_expanded_function_AttnWeightSoftmax"Softmax: Ž ?Attention_test_attention_4d_expanded_function_AttnWeightSoftmax8Attention_test_attention_4d_expanded_function_SoftmaxOut"Cast* to : ž 8Attention_test_attention_4d_expanded_function_SoftmaxOut =Attention_test_attention_4d_expanded_function_VAttentionInput9Attention_test_attention_4d_expanded_function_YPreReshape"MatMul: J 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Attention_test_attention_4d_fp16_expanded_function_QKHeadSizeF?Attention_test_attention_4d_fp16_expanded_function_SqrtHeadSize"Sqrt: [8Attention_test_attention_4d_fp16_expanded_function_One1D"Constant* value*: : _9Attention_test_attention_4d_fp16_expanded_function_One1DF"Constant* value* "€? : \9Attention_test_attention_4d_fp16_expanded_function_Zero1D"Constant* value*: : Į 9Attention_test_attention_4d_fp16_expanded_function_One1DF ?Attention_test_attention_4d_fp16_expanded_function_SqrtHeadSizeBAttention_test_attention_4d_fp16_expanded_function_CalculatedScale"Div: ]9Attention_test_attention_4d_fp16_expanded_function_ScaleF"Constant* value*"€? :  BAttention_test_attention_4d_fp16_expanded_function_CalculatedScale>Attention_test_attention_4d_fp16_expanded_function_ScaleFactor"Identity: Œ >Attention_test_attention_4d_fp16_expanded_function_ScaleFactorBAttention_test_attention_4d_fp16_expanded_function_ScaleFactorSqrt"Sqrt: ˜ BAttention_test_attention_4d_fp16_expanded_function_ScaleFactorSqrt?Attention_test_attention_4d_fp16_expanded_function_ScaleFactorF"Cast* to  : ‰ Attention_test_attention_4d_fp16_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Õ :Attention_test_attention_4d_fp16_expanded_function_QSeqLen >Attention_test_attention_4d_fp16_expanded_function_NewKVSeqLen@Attention_test_attention_4d_fp16_expanded_function_AttnBiasShape"Concat* axis : d>Attention_test_attention_4d_fp16_expanded_function_FloatNegInf"Constant* value* "€˙ : c=Attention_test_attention_4d_fp16_expanded_function_ScalarZero"Constant* value* " : ’ @Attention_test_attention_4d_fp16_expanded_function_AttnBiasShape;Attention_test_attention_4d_fp16_expanded_function_AttnBias"ConstantOfShape: ‘ ;Attention_test_attention_4d_fp16_expanded_function_AttnBiasFAttention_test_attention_4d_fp16_expanded_function_AttnBiasCausalOrNot"Identity: ™ FAttention_test_attention_4d_fp16_expanded_function_AttnBiasCausalOrNotAttention_test_attention_4d_fp16_expanded_function_DivNumHeads"Div: ” >Attention_test_attention_4d_fp16_expanded_function_DivNumHeads?Attention_test_attention_4d_fp16_expanded_function_IDivNumHeads"Cast* to : Ę Attention_test_attention_4d_fp16_expanded_function_KUnsqueezed" Unsqueeze: Č ?Attention_test_attention_4d_fp16_expanded_function_PresentValue 8Attention_test_attention_4d_fp16_expanded_function_Two1D>Attention_test_attention_4d_fp16_expanded_function_VUnsqueezed" Unsqueeze: – Attention_test_attention_4d_fp16_expanded_function_NewKVSeqLen =Attention_test_attention_4d_fp16_expanded_function_QKHeadSize?Attention_test_attention_4d_fp16_expanded_function_KExpandShape"Concat* axis : É >Attention_test_attention_4d_fp16_expanded_function_KUnsqueezed ?Attention_test_attention_4d_fp16_expanded_function_KExpandShapeAttention_test_attention_4d_fp16_expanded_function_NewKVSeqLen 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?Hbã>÷ã?æõ?&J?•˛Ô>9ėŅ>{2â>ãnû>Xšî>‰ˆ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_attn_mask_expanded/000077500000000000000000000000001511334557700314515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_attn_mask_expanded/model.onnx000066400000000000000000000272641511334557700334700ustar00rootroot00000000000000  backend-test:›] m QEAttention_test_attention_4d_gqa_attn_mask_expanded_function_BatchSize"Shape* start * end : } QCAttention_test_attention_4d_gqa_attn_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ~ KDAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : V QEAttention_test_attention_4d_gqa_attn_mask_expanded_function_QReshaped"Identity: V KEAttention_test_attention_4d_gqa_attn_mask_expanded_function_KReshaped"Identity: V VEAttention_test_attention_4d_gqa_attn_mask_expanded_function_VReshaped"Identity: ą EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QReshapedEAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads"Shape* start * end : ˛ EAttention_test_attention_4d_gqa_attn_mask_expanded_function_KReshapedFAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeads"Shape* start * end : ˛ EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QReshapedFAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSize"Shape* start * end : ¤ FAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSizeGAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSizeF"Cast* to : ą EAttention_test_attention_4d_gqa_attn_mask_expanded_function_VReshapedEAttention_test_attention_4d_gqa_attn_mask_expanded_function_VHeadSize"Shape* start * end : › GAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSizeFHAttention_test_attention_4d_gqa_attn_mask_expanded_function_SqrtHeadSize"Sqrt: dAAttention_test_attention_4d_gqa_attn_mask_expanded_function_One1D"Constant* value*: : hBAttention_test_attention_4d_gqa_attn_mask_expanded_function_One1DF"Constant* value* "€? : eBAttention_test_attention_4d_gqa_attn_mask_expanded_function_Zero1D"Constant* value*: : â BAttention_test_attention_4d_gqa_attn_mask_expanded_function_One1DF HAttention_test_attention_4d_gqa_attn_mask_expanded_function_SqrtHeadSizeKAttention_test_attention_4d_gqa_attn_mask_expanded_function_CalculatedScale"Div: fBAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleF"Constant* value*"€? : ĸ KAttention_test_attention_4d_gqa_attn_mask_expanded_function_CalculatedScaleGAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactor"Identity: ž GAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorKAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorSqrt"Sqrt: Ē KAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorSqrtHAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorF"Cast* to : › EAttention_test_attention_4d_gqa_attn_mask_expanded_function_KReshapedFAttention_test_attention_4d_gqa_attn_mask_expanded_function_PresentKey"Identity: kHAttention_test_attention_4d_gqa_attn_mask_expanded_function_PastKVSeqLen"Constant* value*: :  EAttention_test_attention_4d_gqa_attn_mask_expanded_function_VReshapedHAttention_test_attention_4d_gqa_attn_mask_expanded_function_PresentValue"Identity: Æ FAttention_test_attention_4d_gqa_attn_mask_expanded_function_PresentKeyGAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : đ CAttention_test_attention_4d_gqa_attn_mask_expanded_function_QSeqLen GAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLenIAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasShape"Concat* axis : mGAttention_test_attention_4d_gqa_attn_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : lFAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScalarZero"Constant* value* " : b attn_maskIAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasShort"Identity:  IAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasShortDAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBias"Identity: Ŗ DAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasOAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasCausalOrNot"Identity: Ģ OAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasCausalOrNotEAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasT"Cast* to : ß EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeadsEAttention_test_attention_4d_gqa_attn_mask_expanded_function_NGQACond1"Equal: ” EAttention_test_attention_4d_gqa_attn_mask_expanded_function_NGQACond1DAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond1"Not: ß EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeadsGAttention_test_attention_4d_gqa_attn_mask_expanded_function_DivNumHeads"Div: Ļ GAttention_test_attention_4d_gqa_attn_mask_expanded_function_DivNumHeadsHAttention_test_attention_4d_gqa_attn_mask_expanded_function_IDivNumHeads"Cast* to : å EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeadsMAttention_test_attention_4d_gqa_attn_mask_expanded_function_RemainderNumHeads"Mod: â MAttention_test_attention_4d_gqa_attn_mask_expanded_function_RemainderNumHeads BAttention_test_attention_4d_gqa_attn_mask_expanded_function_Zero1DDAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond2"Equal: Ø DAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond1 DAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond2CAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond"And: Ļ CAttention_test_attention_4d_gqa_attn_mask_expanded_function_GQACond HAttention_test_attention_4d_gqa_attn_mask_expanded_function_IDivNumHeads AAttention_test_attention_4d_gqa_attn_mask_expanded_function_One1DIAttention_test_attention_4d_gqa_attn_mask_expanded_function_InterleaveDim"Where: dAAttention_test_attention_4d_gqa_attn_mask_expanded_function_Two1D"Constant* value*: : á FAttention_test_attention_4d_gqa_attn_mask_expanded_function_PresentKey AAttention_test_attention_4d_gqa_attn_mask_expanded_function_Two1DGAttention_test_attention_4d_gqa_attn_mask_expanded_function_KUnsqueezed" Unsqueeze: ã HAttention_test_attention_4d_gqa_attn_mask_expanded_function_PresentValue AAttention_test_attention_4d_gqa_attn_mask_expanded_function_Two1DGAttention_test_attention_4d_gqa_attn_mask_expanded_function_VUnsqueezed" Unsqueeze: Ė EAttention_test_attention_4d_gqa_attn_mask_expanded_function_BatchSize FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeads IAttention_test_attention_4d_gqa_attn_mask_expanded_function_InterleaveDim GAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLen FAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSizeHAttention_test_attention_4d_gqa_attn_mask_expanded_function_KExpandShape"Concat* axis : ä GAttention_test_attention_4d_gqa_attn_mask_expanded_function_KUnsqueezed HAttention_test_attention_4d_gqa_attn_mask_expanded_function_KExpandShapeEAttention_test_attention_4d_gqa_attn_mask_expanded_function_KExpanded"Expand: Ë EAttention_test_attention_4d_gqa_attn_mask_expanded_function_BatchSize FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KVNumHeads IAttention_test_attention_4d_gqa_attn_mask_expanded_function_InterleaveDim GAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLen EAttention_test_attention_4d_gqa_attn_mask_expanded_function_VHeadSizeHAttention_test_attention_4d_gqa_attn_mask_expanded_function_VExpandShape"Concat* axis : ä GAttention_test_attention_4d_gqa_attn_mask_expanded_function_VUnsqueezed HAttention_test_attention_4d_gqa_attn_mask_expanded_function_VExpandShapeEAttention_test_attention_4d_gqa_attn_mask_expanded_function_VExpanded"Expand: ƒ EAttention_test_attention_4d_gqa_attn_mask_expanded_function_BatchSize EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads GAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLen FAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKHeadSizeKAttention_test_attention_4d_gqa_attn_mask_expanded_function_KAttentionShape"Concat* axis : ‚ EAttention_test_attention_4d_gqa_attn_mask_expanded_function_BatchSize EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QNumHeads GAttention_test_attention_4d_gqa_attn_mask_expanded_function_NewKVSeqLen EAttention_test_attention_4d_gqa_attn_mask_expanded_function_VHeadSizeKAttention_test_attention_4d_gqa_attn_mask_expanded_function_VAttentionShape"Concat* axis : ė EAttention_test_attention_4d_gqa_attn_mask_expanded_function_KExpanded KAttention_test_attention_4d_gqa_attn_mask_expanded_function_KAttentionShapeKAttention_test_attention_4d_gqa_attn_mask_expanded_function_KAttentionInput"Reshape: ė EAttention_test_attention_4d_gqa_attn_mask_expanded_function_VExpanded KAttention_test_attention_4d_gqa_attn_mask_expanded_function_VAttentionShapeKAttention_test_attention_4d_gqa_attn_mask_expanded_function_VAttentionInput"Reshape: ĩ KAttention_test_attention_4d_gqa_attn_mask_expanded_function_KAttentionInputFAttention_test_attention_4d_gqa_attn_mask_expanded_function_KTranspose" Transpose* perm@@@@ : Ũ EAttention_test_attention_4d_gqa_attn_mask_expanded_function_QReshaped HAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorFCAttention_test_attention_4d_gqa_attn_mask_expanded_function_QScaled"Mul: Ū FAttention_test_attention_4d_gqa_attn_mask_expanded_function_KTranspose HAttention_test_attention_4d_gqa_attn_mask_expanded_function_ScaleFactorFCAttention_test_attention_4d_gqa_attn_mask_expanded_function_KScaled"Mul: Ū CAttention_test_attention_4d_gqa_attn_mask_expanded_function_QScaled CAttention_test_attention_4d_gqa_attn_mask_expanded_function_KScaledHAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeight"MatMul: Ĩ HAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeightFAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnCast"Cast* to : č FAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnCast EAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnBiasTPAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeightWithBias"Add: ¯ PAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeightWithBiasOAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeightSoftcap"Identity: ­ OAttention_test_attention_4d_gqa_attn_mask_expanded_function_QKAttnWeightSoftcapGAttention_test_attention_4d_gqa_attn_mask_expanded_function_SoftmaxCast"Cast* to : Ŗ GAttention_test_attention_4d_gqa_attn_mask_expanded_function_SoftmaxCastMAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnWeightSoftmax"Softmax: Ē MAttention_test_attention_4d_gqa_attn_mask_expanded_function_AttnWeightSoftmaxFAttention_test_attention_4d_gqa_attn_mask_expanded_function_SoftmaxOut"Cast* to : č FAttention_test_attention_4d_gqa_attn_mask_expanded_function_SoftmaxOut 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?Uuô>LŠ?n?ö>|'>iĒ(?UĖë>ŠJ6?´u?0|?¯ŒH?†Ü?ōQ‡=8āņ>cā>ÄŠO>{āØ>Ëk(?üšë>áūô>ģ,>m"˙>3{i>z×??â ?!¤?R6?58.?#y>4Ę ?˙q ?Ũ*??sƒ?MS?‹‚?Ųš?RŨ">’Í$?˛ö>, 7?^ú ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_causal_expanded/000077500000000000000000000000001511334557700307405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_causal_expanded/model.onnx000066400000000000000000000337031511334557700327520ustar00rootroot00000000000000  backend-test:Ēo j QBAttention_test_attention_4d_gqa_causal_expanded_function_BatchSize"Shape* start * end : z Q@Attention_test_attention_4d_gqa_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { KAAttention_test_attention_4d_gqa_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : S QBAttention_test_attention_4d_gqa_causal_expanded_function_QReshaped"Identity: S KBAttention_test_attention_4d_gqa_causal_expanded_function_KReshaped"Identity: S VBAttention_test_attention_4d_gqa_causal_expanded_function_VReshaped"Identity: Ģ BAttention_test_attention_4d_gqa_causal_expanded_function_QReshapedBAttention_test_attention_4d_gqa_causal_expanded_function_QNumHeads"Shape* start * end : Ŧ BAttention_test_attention_4d_gqa_causal_expanded_function_KReshapedCAttention_test_attention_4d_gqa_causal_expanded_function_KVNumHeads"Shape* start * end : Ŧ BAttention_test_attention_4d_gqa_causal_expanded_function_QReshapedCAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSize"Shape* start * end : ž CAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSizeDAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSizeF"Cast* to : Ģ BAttention_test_attention_4d_gqa_causal_expanded_function_VReshapedBAttention_test_attention_4d_gqa_causal_expanded_function_VHeadSize"Shape* start * end : • DAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSizeFEAttention_test_attention_4d_gqa_causal_expanded_function_SqrtHeadSize"Sqrt: a>Attention_test_attention_4d_gqa_causal_expanded_function_One1D"Constant* value*: : e?Attention_test_attention_4d_gqa_causal_expanded_function_One1DF"Constant* value* "€? : b?Attention_test_attention_4d_gqa_causal_expanded_function_Zero1D"Constant* value*: : Ų ?Attention_test_attention_4d_gqa_causal_expanded_function_One1DF EAttention_test_attention_4d_gqa_causal_expanded_function_SqrtHeadSizeHAttention_test_attention_4d_gqa_causal_expanded_function_CalculatedScale"Div: c?Attention_test_attention_4d_gqa_causal_expanded_function_ScaleF"Constant* value*"€? : œ HAttention_test_attention_4d_gqa_causal_expanded_function_CalculatedScaleDAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactor"Identity: ˜ DAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorHAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorSqrt"Sqrt: ¤ HAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorSqrtEAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorF"Cast* to : • BAttention_test_attention_4d_gqa_causal_expanded_function_KReshapedCAttention_test_attention_4d_gqa_causal_expanded_function_PresentKey"Identity: hEAttention_test_attention_4d_gqa_causal_expanded_function_PastKVSeqLen"Constant* value*: : — BAttention_test_attention_4d_gqa_causal_expanded_function_VReshapedEAttention_test_attention_4d_gqa_causal_expanded_function_PresentValue"Identity: Ā CAttention_test_attention_4d_gqa_causal_expanded_function_PresentKeyDAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : į @Attention_test_attention_4d_gqa_causal_expanded_function_QSeqLen DAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLenFAttention_test_attention_4d_gqa_causal_expanded_function_AttnBiasShape"Concat* axis : jDAttention_test_attention_4d_gqa_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : iCAttention_test_attention_4d_gqa_causal_expanded_function_ScalarZero"Constant* value* " : ž FAttention_test_attention_4d_gqa_causal_expanded_function_AttnBiasShapeAAttention_test_attention_4d_gqa_causal_expanded_function_AttnBias"ConstantOfShape: `=Attention_test_attention_4d_gqa_causal_expanded_function_Zero"Constant* value*: : _Attention_test_attention_4d_gqa_causal_expanded_function_One1DFAttention_test_attention_4d_gqa_causal_expanded_function_InterleaveDim"Where: a>Attention_test_attention_4d_gqa_causal_expanded_function_Two1D"Constant* value*: : Ø CAttention_test_attention_4d_gqa_causal_expanded_function_PresentKey >Attention_test_attention_4d_gqa_causal_expanded_function_Two1DDAttention_test_attention_4d_gqa_causal_expanded_function_KUnsqueezed" Unsqueeze: Ú EAttention_test_attention_4d_gqa_causal_expanded_function_PresentValue >Attention_test_attention_4d_gqa_causal_expanded_function_Two1DDAttention_test_attention_4d_gqa_causal_expanded_function_VUnsqueezed" Unsqueeze: ē BAttention_test_attention_4d_gqa_causal_expanded_function_BatchSize CAttention_test_attention_4d_gqa_causal_expanded_function_KVNumHeads FAttention_test_attention_4d_gqa_causal_expanded_function_InterleaveDim DAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSizeEAttention_test_attention_4d_gqa_causal_expanded_function_KExpandShape"Concat* axis : Û DAttention_test_attention_4d_gqa_causal_expanded_function_KUnsqueezed EAttention_test_attention_4d_gqa_causal_expanded_function_KExpandShapeBAttention_test_attention_4d_gqa_causal_expanded_function_KExpanded"Expand: š BAttention_test_attention_4d_gqa_causal_expanded_function_BatchSize CAttention_test_attention_4d_gqa_causal_expanded_function_KVNumHeads FAttention_test_attention_4d_gqa_causal_expanded_function_InterleaveDim DAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLen BAttention_test_attention_4d_gqa_causal_expanded_function_VHeadSizeEAttention_test_attention_4d_gqa_causal_expanded_function_VExpandShape"Concat* axis : Û DAttention_test_attention_4d_gqa_causal_expanded_function_VUnsqueezed EAttention_test_attention_4d_gqa_causal_expanded_function_VExpandShapeBAttention_test_attention_4d_gqa_causal_expanded_function_VExpanded"Expand: ô BAttention_test_attention_4d_gqa_causal_expanded_function_BatchSize BAttention_test_attention_4d_gqa_causal_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_causal_expanded_function_QKHeadSizeHAttention_test_attention_4d_gqa_causal_expanded_function_KAttentionShape"Concat* axis : ķ BAttention_test_attention_4d_gqa_causal_expanded_function_BatchSize BAttention_test_attention_4d_gqa_causal_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_causal_expanded_function_NewKVSeqLen BAttention_test_attention_4d_gqa_causal_expanded_function_VHeadSizeHAttention_test_attention_4d_gqa_causal_expanded_function_VAttentionShape"Concat* axis : ã BAttention_test_attention_4d_gqa_causal_expanded_function_KExpanded HAttention_test_attention_4d_gqa_causal_expanded_function_KAttentionShapeHAttention_test_attention_4d_gqa_causal_expanded_function_KAttentionInput"Reshape: ã BAttention_test_attention_4d_gqa_causal_expanded_function_VExpanded HAttention_test_attention_4d_gqa_causal_expanded_function_VAttentionShapeHAttention_test_attention_4d_gqa_causal_expanded_function_VAttentionInput"Reshape: ¯ HAttention_test_attention_4d_gqa_causal_expanded_function_KAttentionInputCAttention_test_attention_4d_gqa_causal_expanded_function_KTranspose" Transpose* perm@@@@ : Ô BAttention_test_attention_4d_gqa_causal_expanded_function_QReshaped EAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorF@Attention_test_attention_4d_gqa_causal_expanded_function_QScaled"Mul: Õ CAttention_test_attention_4d_gqa_causal_expanded_function_KTranspose EAttention_test_attention_4d_gqa_causal_expanded_function_ScaleFactorF@Attention_test_attention_4d_gqa_causal_expanded_function_KScaled"Mul: Õ @Attention_test_attention_4d_gqa_causal_expanded_function_QScaled @Attention_test_attention_4d_gqa_causal_expanded_function_KScaledEAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeight"MatMul: Ÿ EAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeightCAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnCast"Cast* to : ß CAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnCast BAttention_test_attention_4d_gqa_causal_expanded_function_AttnBiasTMAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeightWithBias"Add: Š MAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeightWithBiasLAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeightSoftcap"Identity: § LAttention_test_attention_4d_gqa_causal_expanded_function_QKAttnWeightSoftcapDAttention_test_attention_4d_gqa_causal_expanded_function_SoftmaxCast"Cast* to :  DAttention_test_attention_4d_gqa_causal_expanded_function_SoftmaxCastJAttention_test_attention_4d_gqa_causal_expanded_function_AttnWeightSoftmax"Softmax: ¤ 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=Attention_test_attention_4d_gqa_expanded_function_ScaleFactorAAttention_test_attention_4d_gqa_expanded_function_ScaleFactorSqrt"Sqrt: – AAttention_test_attention_4d_gqa_expanded_function_ScaleFactorSqrt>Attention_test_attention_4d_gqa_expanded_function_ScaleFactorF"Cast* to : ‡ ;Attention_test_attention_4d_gqa_expanded_function_KReshapedAttention_test_attention_4d_gqa_expanded_function_PastKVSeqLen"Constant* value*: : ‰ ;Attention_test_attention_4d_gqa_expanded_function_VReshaped>Attention_test_attention_4d_gqa_expanded_function_PresentValue"Identity: ˛ Attention_test_attention_4d_gqa_expanded_function_IDivNumHeads"Cast* to : Į ;Attention_test_attention_4d_gqa_expanded_function_QNumHeads Attention_test_attention_4d_gqa_expanded_function_IDivNumHeads 7Attention_test_attention_4d_gqa_expanded_function_One1D?Attention_test_attention_4d_gqa_expanded_function_InterleaveDim"Where: Z7Attention_test_attention_4d_gqa_expanded_function_Two1D"Constant* value*: : à Attention_test_attention_4d_gqa_expanded_function_PresentValue 7Attention_test_attention_4d_gqa_expanded_function_Two1D=Attention_test_attention_4d_gqa_expanded_function_VUnsqueezed" Unsqueeze:  ;Attention_test_attention_4d_gqa_expanded_function_BatchSize Attention_test_attention_4d_gqa_expanded_function_KExpandShape"Concat* axis : Æ =Attention_test_attention_4d_gqa_expanded_function_KUnsqueezed >Attention_test_attention_4d_gqa_expanded_function_KExpandShape;Attention_test_attention_4d_gqa_expanded_function_KExpanded"Expand:  ;Attention_test_attention_4d_gqa_expanded_function_BatchSize Attention_test_attention_4d_gqa_expanded_function_VExpandShape"Concat* axis : Æ =Attention_test_attention_4d_gqa_expanded_function_VUnsqueezed >Attention_test_attention_4d_gqa_expanded_function_VExpandShape;Attention_test_attention_4d_gqa_expanded_function_VExpanded"Expand: Ņ ;Attention_test_attention_4d_gqa_expanded_function_BatchSize ;Attention_test_attention_4d_gqa_expanded_function_QNumHeads =Attention_test_attention_4d_gqa_expanded_function_NewKVSeqLen Attention_test_attention_4d_gqa_expanded_function_ScaleFactorF9Attention_test_attention_4d_gqa_expanded_function_QScaled"Mul: Ā Attention_test_attention_4d_gqa_expanded_function_ScaleFactorF9Attention_test_attention_4d_gqa_expanded_function_KScaled"Mul: Ā 9Attention_test_attention_4d_gqa_expanded_function_QScaled 9Attention_test_attention_4d_gqa_expanded_function_KScaled>Attention_test_attention_4d_gqa_expanded_function_QKAttnWeight"MatMul: ‘ >Attention_test_attention_4d_gqa_expanded_function_QKAttnWeightQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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GØ>OˆĨ>ŧ?ŒŖ÷>C§ū>Ũđ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_scaled_expanded/000077500000000000000000000000001511334557700307235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_scaled_expanded/model.onnx000066400000000000000000000262071511334557700327360ustar00rootroot00000000000000  backend-test:îX j QBAttention_test_attention_4d_gqa_scaled_expanded_function_BatchSize"Shape* start * end : z Q@Attention_test_attention_4d_gqa_scaled_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : { KAAttention_test_attention_4d_gqa_scaled_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : S QBAttention_test_attention_4d_gqa_scaled_expanded_function_QReshaped"Identity: S KBAttention_test_attention_4d_gqa_scaled_expanded_function_KReshaped"Identity: S VBAttention_test_attention_4d_gqa_scaled_expanded_function_VReshaped"Identity: Ģ BAttention_test_attention_4d_gqa_scaled_expanded_function_QReshapedBAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads"Shape* start * end : Ŧ BAttention_test_attention_4d_gqa_scaled_expanded_function_KReshapedCAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeads"Shape* start * end : Ŧ BAttention_test_attention_4d_gqa_scaled_expanded_function_QReshapedCAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSize"Shape* start * end : ž CAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSizeDAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSizeF"Cast* to : Ģ BAttention_test_attention_4d_gqa_scaled_expanded_function_VReshapedBAttention_test_attention_4d_gqa_scaled_expanded_function_VHeadSize"Shape* start * end : • DAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSizeFEAttention_test_attention_4d_gqa_scaled_expanded_function_SqrtHeadSize"Sqrt: a>Attention_test_attention_4d_gqa_scaled_expanded_function_One1D"Constant* value*: : e?Attention_test_attention_4d_gqa_scaled_expanded_function_One1DF"Constant* value* "€? : b?Attention_test_attention_4d_gqa_scaled_expanded_function_Zero1D"Constant* value*: : Ų ?Attention_test_attention_4d_gqa_scaled_expanded_function_One1DF EAttention_test_attention_4d_gqa_scaled_expanded_function_SqrtHeadSizeHAttention_test_attention_4d_gqa_scaled_expanded_function_CalculatedScale"Div: c?Attention_test_attention_4d_gqa_scaled_expanded_function_ScaleF"Constant* value*" ×#< : “ ?Attention_test_attention_4d_gqa_scaled_expanded_function_ScaleFDAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactor"Identity: ˜ DAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorHAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorSqrt"Sqrt: ¤ HAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorSqrtEAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorF"Cast* to : • BAttention_test_attention_4d_gqa_scaled_expanded_function_KReshapedCAttention_test_attention_4d_gqa_scaled_expanded_function_PresentKey"Identity: hEAttention_test_attention_4d_gqa_scaled_expanded_function_PastKVSeqLen"Constant* value*: : — BAttention_test_attention_4d_gqa_scaled_expanded_function_VReshapedEAttention_test_attention_4d_gqa_scaled_expanded_function_PresentValue"Identity: Ā CAttention_test_attention_4d_gqa_scaled_expanded_function_PresentKeyDAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : į @Attention_test_attention_4d_gqa_scaled_expanded_function_QSeqLen DAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLenFAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasShape"Concat* axis : jDAttention_test_attention_4d_gqa_scaled_expanded_function_FloatNegInf"Constant* value* "€˙ : iCAttention_test_attention_4d_gqa_scaled_expanded_function_ScalarZero"Constant* value* " : ž FAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasShapeAAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBias"ConstantOfShape:  AAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasLAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasCausalOrNot"Identity: Ĩ LAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasCausalOrNotBAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasT"Cast* to : Ö BAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeadsBAttention_test_attention_4d_gqa_scaled_expanded_function_NGQACond1"Equal: Ž BAttention_test_attention_4d_gqa_scaled_expanded_function_NGQACond1AAttention_test_attention_4d_gqa_scaled_expanded_function_GQACond1"Not: Ö BAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeadsDAttention_test_attention_4d_gqa_scaled_expanded_function_DivNumHeads"Div:   DAttention_test_attention_4d_gqa_scaled_expanded_function_DivNumHeadsEAttention_test_attention_4d_gqa_scaled_expanded_function_IDivNumHeads"Cast* to : Ü BAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads CAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeadsJAttention_test_attention_4d_gqa_scaled_expanded_function_RemainderNumHeads"Mod: Ų JAttention_test_attention_4d_gqa_scaled_expanded_function_RemainderNumHeads ?Attention_test_attention_4d_gqa_scaled_expanded_function_Zero1DAAttention_test_attention_4d_gqa_scaled_expanded_function_GQACond2"Equal: Ī AAttention_test_attention_4d_gqa_scaled_expanded_function_GQACond1 AAttention_test_attention_4d_gqa_scaled_expanded_function_GQACond2@Attention_test_attention_4d_gqa_scaled_expanded_function_GQACond"And: š @Attention_test_attention_4d_gqa_scaled_expanded_function_GQACond EAttention_test_attention_4d_gqa_scaled_expanded_function_IDivNumHeads >Attention_test_attention_4d_gqa_scaled_expanded_function_One1DFAttention_test_attention_4d_gqa_scaled_expanded_function_InterleaveDim"Where: a>Attention_test_attention_4d_gqa_scaled_expanded_function_Two1D"Constant* value*: : Ø CAttention_test_attention_4d_gqa_scaled_expanded_function_PresentKey >Attention_test_attention_4d_gqa_scaled_expanded_function_Two1DDAttention_test_attention_4d_gqa_scaled_expanded_function_KUnsqueezed" Unsqueeze: Ú EAttention_test_attention_4d_gqa_scaled_expanded_function_PresentValue >Attention_test_attention_4d_gqa_scaled_expanded_function_Two1DDAttention_test_attention_4d_gqa_scaled_expanded_function_VUnsqueezed" Unsqueeze: ē BAttention_test_attention_4d_gqa_scaled_expanded_function_BatchSize CAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeads FAttention_test_attention_4d_gqa_scaled_expanded_function_InterleaveDim DAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSizeEAttention_test_attention_4d_gqa_scaled_expanded_function_KExpandShape"Concat* axis : Û DAttention_test_attention_4d_gqa_scaled_expanded_function_KUnsqueezed EAttention_test_attention_4d_gqa_scaled_expanded_function_KExpandShapeBAttention_test_attention_4d_gqa_scaled_expanded_function_KExpanded"Expand: š BAttention_test_attention_4d_gqa_scaled_expanded_function_BatchSize CAttention_test_attention_4d_gqa_scaled_expanded_function_KVNumHeads FAttention_test_attention_4d_gqa_scaled_expanded_function_InterleaveDim DAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLen BAttention_test_attention_4d_gqa_scaled_expanded_function_VHeadSizeEAttention_test_attention_4d_gqa_scaled_expanded_function_VExpandShape"Concat* axis : Û DAttention_test_attention_4d_gqa_scaled_expanded_function_VUnsqueezed EAttention_test_attention_4d_gqa_scaled_expanded_function_VExpandShapeBAttention_test_attention_4d_gqa_scaled_expanded_function_VExpanded"Expand: ô BAttention_test_attention_4d_gqa_scaled_expanded_function_BatchSize BAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_scaled_expanded_function_QKHeadSizeHAttention_test_attention_4d_gqa_scaled_expanded_function_KAttentionShape"Concat* axis : ķ BAttention_test_attention_4d_gqa_scaled_expanded_function_BatchSize BAttention_test_attention_4d_gqa_scaled_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_scaled_expanded_function_NewKVSeqLen BAttention_test_attention_4d_gqa_scaled_expanded_function_VHeadSizeHAttention_test_attention_4d_gqa_scaled_expanded_function_VAttentionShape"Concat* axis : ã BAttention_test_attention_4d_gqa_scaled_expanded_function_KExpanded HAttention_test_attention_4d_gqa_scaled_expanded_function_KAttentionShapeHAttention_test_attention_4d_gqa_scaled_expanded_function_KAttentionInput"Reshape: ã BAttention_test_attention_4d_gqa_scaled_expanded_function_VExpanded HAttention_test_attention_4d_gqa_scaled_expanded_function_VAttentionShapeHAttention_test_attention_4d_gqa_scaled_expanded_function_VAttentionInput"Reshape: ¯ HAttention_test_attention_4d_gqa_scaled_expanded_function_KAttentionInputCAttention_test_attention_4d_gqa_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : Ô BAttention_test_attention_4d_gqa_scaled_expanded_function_QReshaped EAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorF@Attention_test_attention_4d_gqa_scaled_expanded_function_QScaled"Mul: Õ CAttention_test_attention_4d_gqa_scaled_expanded_function_KTranspose EAttention_test_attention_4d_gqa_scaled_expanded_function_ScaleFactorF@Attention_test_attention_4d_gqa_scaled_expanded_function_KScaled"Mul: Õ @Attention_test_attention_4d_gqa_scaled_expanded_function_QScaled @Attention_test_attention_4d_gqa_scaled_expanded_function_KScaledEAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeight"MatMul: Ÿ EAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeightCAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnCast"Cast* to : ß CAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnCast BAttention_test_attention_4d_gqa_scaled_expanded_function_AttnBiasTMAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeightWithBias"Add: Š MAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeightWithBiasLAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeightSoftcap"Identity: § LAttention_test_attention_4d_gqa_scaled_expanded_function_QKAttnWeightSoftcapDAttention_test_attention_4d_gqa_scaled_expanded_function_SoftmaxCast"Cast* to :  DAttention_test_attention_4d_gqa_scaled_expanded_function_SoftmaxCastJAttention_test_attention_4d_gqa_scaled_expanded_function_AttnWeightSoftmax"Softmax: ¤ JAttention_test_attention_4d_gqa_scaled_expanded_function_AttnWeightSoftmaxCAttention_test_attention_4d_gqa_scaled_expanded_function_SoftmaxOut"Cast* to : ß CAttention_test_attention_4d_gqa_scaled_expanded_function_SoftmaxOut HAttention_test_attention_4d_gqa_scaled_expanded_function_VAttentionInputDAttention_test_attention_4d_gqa_scaled_expanded_function_YPreReshape"MatMul: U DAttention_test_attention_4d_gqa_scaled_expanded_function_YPreReshapeY"Identity:%test_attention_4d_gqa_scaled_expandedZ Q     Z K     Z V     b Y     B 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Hđ>iā?G1?đ? ?sæĖ>)ÂŅ>Mžđ>đc?â=ķ>Nâũ>gF/?š?íËĪ>ļīÕ>KŨé>„F?Ņ9õ>@Ž?yŽ2?j ?•îŌ>v ˙>÷†I„ˇ>ۖē>°jį>Ė ?Ķ?Į†@?cņ ?’¨>¸ų¸>­ˇ>ß>~ū>=:˙>&};?đz ?}?Ž>S>ˇ>rŲē>æë>õE?$?ãa??-Ø ?“3¨>פē>qē>ˇĖâ>&Ē˙>ÄĪü>˛}=?°û ?uĒŠ>ģ>R ģ>ę#â>$,?š™ø>å>?EW ?fg§> zš>r˜ŧ>É0Ũ>R’˙>‰S?Ę1;?œą ?œ °>žņš>ᙹ>¨¨į>ˆü?ę?Yc@?Ėr?VJĨ>{įŊ>ƒÚļ>†č>Åû>ú>?B:?j ?i˛>āš°>Åbŋ>AŋŨ>Wg?ķSū>@~??Ŗģ ?›;§>§.¸>§[ē> â>z˙> ˆ?AÍ=?Ũ ?éĢ>æÍ¸>ž9š>|”å>ė“?u`ø>(>?<Ŧ ?LŪ¨>áë˛>ī#Ŋ>>Ũâ>ēŧ?YÍ>[\?Is×>đ?­Ŧ#?b%$?iĸ>~ ?ųûÎ>hT?NĶ>­Y?6š"?„R&?ŖT>zd%?ƒhÔ>]ņ?t›Ķ>?'Ã#?™ˆ&?ąb>áŒ$?o'Ė>Žz?Ö>ˆ?x]"?Œ#%?ū">]Ŋ!?cœÚ> Ũ? •Ņ> ?ä#?‚C*?.$>*F'?Ķ>ĨZ?Ø´Ō>æ!?:Ŋ%?Š#?H>‰Š ?n/Ų>ÎU?ģŌ>ˆą?'×%?’`#?ŅW>ģ|!?"Ø>@$ ?nŅ>_°?{™'?EH?+A>Ž ?4tĐ>>ƒ?B+Ô> D?ßR&?i–?1>ŨM?įėĐ>Ŋ?ļ8É>ī4?Ÿ(?N? >Ü$?ÎÛ>āī ?m;Î>ž“?Ą&?æĻ#?ŧŧ>@Ą#?ĒØ>8\?‘äĪ>fų?Œ"'?–Ķ!?j >g#?„?›?Ō>'ją>c?Мø>Ôũ>Šd?p×˙>āG?vlŨ>G0Ļ>&?„"ü>B?)’?I^?S"?ŠšØ>6­>‘Ä?ˆú>¨?1 ?­Ÿ?.?Ķã>"SĢ>ú–û>;˙>œí?.Â?x”?ą,?P•Ũ>uËŽ>—cū>žû>ûE?˛ß?Eq?q?ûŨĶ>ķį¯>$?÷>*?ŠH?ú ũ>šÕ?y’Ø>`‰Ĩ>Ą‘?]÷>o?Áo?¤?ž?ĸÖ>Ȳ>Ŧqũ>Nõ>Mĸ?Ës?$Ė?ķ”?ÚZÚ>f‰Š>kÁ?Áķø>…Æ?^?Īá?‹?í$Ū>ˆé¯>O=ü>˜9˙>=v?ņ™?@;?‚Ō?K×>‹“ą>kW˙>›ãũ>Á•?Î2?Ú ?›Œ?įŊā>FžŽ>Pãú>›sû>Šø?ē–?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_softcap_expanded/000077500000000000000000000000001511334557700311275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_softcap_expanded/model.onnx000066400000000000000000000277261511334557700331510ustar00rootroot00000000000000  backend-test:Ŋ_ k QCAttention_test_attention_4d_gqa_softcap_expanded_function_BatchSize"Shape* start * end : { QAAttention_test_attention_4d_gqa_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : | KBAttention_test_attention_4d_gqa_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : T QCAttention_test_attention_4d_gqa_softcap_expanded_function_QReshaped"Identity: T KCAttention_test_attention_4d_gqa_softcap_expanded_function_KReshaped"Identity: T VCAttention_test_attention_4d_gqa_softcap_expanded_function_VReshaped"Identity: ­ CAttention_test_attention_4d_gqa_softcap_expanded_function_QReshapedCAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads"Shape* start * end : Ž CAttention_test_attention_4d_gqa_softcap_expanded_function_KReshapedDAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeads"Shape* start * end : Ž CAttention_test_attention_4d_gqa_softcap_expanded_function_QReshapedDAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSize"Shape* start * end :   DAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSizeEAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSizeF"Cast* to : ­ CAttention_test_attention_4d_gqa_softcap_expanded_function_VReshapedCAttention_test_attention_4d_gqa_softcap_expanded_function_VHeadSize"Shape* start * end : — EAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSizeFFAttention_test_attention_4d_gqa_softcap_expanded_function_SqrtHeadSize"Sqrt: b?Attention_test_attention_4d_gqa_softcap_expanded_function_One1D"Constant* value*: : f@Attention_test_attention_4d_gqa_softcap_expanded_function_One1DF"Constant* value* "€? : c@Attention_test_attention_4d_gqa_softcap_expanded_function_Zero1D"Constant* value*: : Ü @Attention_test_attention_4d_gqa_softcap_expanded_function_One1DF FAttention_test_attention_4d_gqa_softcap_expanded_function_SqrtHeadSizeIAttention_test_attention_4d_gqa_softcap_expanded_function_CalculatedScale"Div: d@Attention_test_attention_4d_gqa_softcap_expanded_function_ScaleF"Constant* value*"€? : ž IAttention_test_attention_4d_gqa_softcap_expanded_function_CalculatedScaleEAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactor"Identity: š EAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorIAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorSqrt"Sqrt: Ļ IAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorSqrtFAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorF"Cast* to : — CAttention_test_attention_4d_gqa_softcap_expanded_function_KReshapedDAttention_test_attention_4d_gqa_softcap_expanded_function_PresentKey"Identity: iFAttention_test_attention_4d_gqa_softcap_expanded_function_PastKVSeqLen"Constant* value*: : ™ CAttention_test_attention_4d_gqa_softcap_expanded_function_VReshapedFAttention_test_attention_4d_gqa_softcap_expanded_function_PresentValue"Identity:  DAttention_test_attention_4d_gqa_softcap_expanded_function_PresentKeyEAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ę AAttention_test_attention_4d_gqa_softcap_expanded_function_QSeqLen EAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLenGAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasShape"Concat* axis : kEAttention_test_attention_4d_gqa_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : jDAttention_test_attention_4d_gqa_softcap_expanded_function_ScalarZero"Constant* value* " :   GAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasShapeBAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBias"ConstantOfShape: Ÿ BAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasMAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasCausalOrNot"Identity: § MAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasCausalOrNotCAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasT"Cast* to : Ų CAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeadsCAttention_test_attention_4d_gqa_softcap_expanded_function_NGQACond1"Equal:  CAttention_test_attention_4d_gqa_softcap_expanded_function_NGQACond1BAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond1"Not: Ų CAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeadsEAttention_test_attention_4d_gqa_softcap_expanded_function_DivNumHeads"Div: ĸ EAttention_test_attention_4d_gqa_softcap_expanded_function_DivNumHeadsFAttention_test_attention_4d_gqa_softcap_expanded_function_IDivNumHeads"Cast* to : ß CAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads DAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeadsKAttention_test_attention_4d_gqa_softcap_expanded_function_RemainderNumHeads"Mod: Ü KAttention_test_attention_4d_gqa_softcap_expanded_function_RemainderNumHeads @Attention_test_attention_4d_gqa_softcap_expanded_function_Zero1DBAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond2"Equal: Ō BAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond1 BAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond2AAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond"And: ž AAttention_test_attention_4d_gqa_softcap_expanded_function_GQACond FAttention_test_attention_4d_gqa_softcap_expanded_function_IDivNumHeads ?Attention_test_attention_4d_gqa_softcap_expanded_function_One1DGAttention_test_attention_4d_gqa_softcap_expanded_function_InterleaveDim"Where: b?Attention_test_attention_4d_gqa_softcap_expanded_function_Two1D"Constant* value*: : Û DAttention_test_attention_4d_gqa_softcap_expanded_function_PresentKey ?Attention_test_attention_4d_gqa_softcap_expanded_function_Two1DEAttention_test_attention_4d_gqa_softcap_expanded_function_KUnsqueezed" Unsqueeze: Ũ FAttention_test_attention_4d_gqa_softcap_expanded_function_PresentValue ?Attention_test_attention_4d_gqa_softcap_expanded_function_Two1DEAttention_test_attention_4d_gqa_softcap_expanded_function_VUnsqueezed" Unsqueeze: Ā CAttention_test_attention_4d_gqa_softcap_expanded_function_BatchSize DAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeads GAttention_test_attention_4d_gqa_softcap_expanded_function_InterleaveDim EAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLen DAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSizeFAttention_test_attention_4d_gqa_softcap_expanded_function_KExpandShape"Concat* axis : Ū EAttention_test_attention_4d_gqa_softcap_expanded_function_KUnsqueezed FAttention_test_attention_4d_gqa_softcap_expanded_function_KExpandShapeCAttention_test_attention_4d_gqa_softcap_expanded_function_KExpanded"Expand: ŋ CAttention_test_attention_4d_gqa_softcap_expanded_function_BatchSize DAttention_test_attention_4d_gqa_softcap_expanded_function_KVNumHeads GAttention_test_attention_4d_gqa_softcap_expanded_function_InterleaveDim EAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_softcap_expanded_function_VHeadSizeFAttention_test_attention_4d_gqa_softcap_expanded_function_VExpandShape"Concat* axis : Ū EAttention_test_attention_4d_gqa_softcap_expanded_function_VUnsqueezed FAttention_test_attention_4d_gqa_softcap_expanded_function_VExpandShapeCAttention_test_attention_4d_gqa_softcap_expanded_function_VExpanded"Expand: ų CAttention_test_attention_4d_gqa_softcap_expanded_function_BatchSize CAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads EAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLen DAttention_test_attention_4d_gqa_softcap_expanded_function_QKHeadSizeIAttention_test_attention_4d_gqa_softcap_expanded_function_KAttentionShape"Concat* axis : ø CAttention_test_attention_4d_gqa_softcap_expanded_function_BatchSize CAttention_test_attention_4d_gqa_softcap_expanded_function_QNumHeads EAttention_test_attention_4d_gqa_softcap_expanded_function_NewKVSeqLen CAttention_test_attention_4d_gqa_softcap_expanded_function_VHeadSizeIAttention_test_attention_4d_gqa_softcap_expanded_function_VAttentionShape"Concat* axis : æ CAttention_test_attention_4d_gqa_softcap_expanded_function_KExpanded IAttention_test_attention_4d_gqa_softcap_expanded_function_KAttentionShapeIAttention_test_attention_4d_gqa_softcap_expanded_function_KAttentionInput"Reshape: æ CAttention_test_attention_4d_gqa_softcap_expanded_function_VExpanded IAttention_test_attention_4d_gqa_softcap_expanded_function_VAttentionShapeIAttention_test_attention_4d_gqa_softcap_expanded_function_VAttentionInput"Reshape: ą IAttention_test_attention_4d_gqa_softcap_expanded_function_KAttentionInputDAttention_test_attention_4d_gqa_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : × CAttention_test_attention_4d_gqa_softcap_expanded_function_QReshaped FAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorFAAttention_test_attention_4d_gqa_softcap_expanded_function_QScaled"Mul: Ø DAttention_test_attention_4d_gqa_softcap_expanded_function_KTranspose FAttention_test_attention_4d_gqa_softcap_expanded_function_ScaleFactorFAAttention_test_attention_4d_gqa_softcap_expanded_function_KScaled"Mul: Ø AAttention_test_attention_4d_gqa_softcap_expanded_function_QScaled AAttention_test_attention_4d_gqa_softcap_expanded_function_KScaledFAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeight"MatMul: Ą FAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeightDAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnCast"Cast* to : â DAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnCast CAttention_test_attention_4d_gqa_softcap_expanded_function_AttnBiasTNAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeightWithBias"Add: gAAttention_test_attention_4d_gqa_softcap_expanded_function_Softcap"Constant* value* "@ : š AAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapBAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapF"Cast* to : á NAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeightWithBias BAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapFDAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapDiv"Div: • DAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapDivEAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapTanh"Tanh: á EAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapTanh BAttention_test_attention_4d_gqa_softcap_expanded_function_SoftcapFMAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeightSoftcap"Mul: Š MAttention_test_attention_4d_gqa_softcap_expanded_function_QKAttnWeightSoftcapEAttention_test_attention_4d_gqa_softcap_expanded_function_SoftmaxCast"Cast* to : Ÿ EAttention_test_attention_4d_gqa_softcap_expanded_function_SoftmaxCastKAttention_test_attention_4d_gqa_softcap_expanded_function_AttnWeightSoftmax"Softmax: Ļ KAttention_test_attention_4d_gqa_softcap_expanded_function_AttnWeightSoftmaxDAttention_test_attention_4d_gqa_softcap_expanded_function_SoftmaxOut"Cast* to : â DAttention_test_attention_4d_gqa_softcap_expanded_function_SoftmaxOut IAttention_test_attention_4d_gqa_softcap_expanded_function_VAttentionInputEAttention_test_attention_4d_gqa_softcap_expanded_function_YPreReshape"MatMul: V EAttention_test_attention_4d_gqa_softcap_expanded_function_YPreReshapeY"Identity:&test_attention_4d_gqa_softcap_expandedZ Q     Z K     Z V     b Y     B 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Ȩ>ā°J?ã…Ũ=ŪČ>‡b>˛/?[ĪŅ=øFË> Ĩ>ąŸ?Ę%ŗ>S×4?ÁUÉ<øL"?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_with_past_and_present_expanded/000077500000000000000000000000001511334557700340545ustar00rootroot00000000000000model.onnx000066400000000000000000000334611511334557700360100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_with_past_and_present_expanded  backend-test:˜n y QQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_BatchSize"Shape* start * end : ‰ QOAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Š KPAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : b QQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QReshaped"Identity: b KQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KReshaped"Identity: b VQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VReshaped"Identity: É QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QReshapedQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads"Shape* start * end : Ę QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KReshapedRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end : Ę QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QReshapedRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : ŧ RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSizeSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : É QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VReshapedQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : ŗ SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSizeFTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: pMAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_One1D"Constant* value*: : tNAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : qNAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_Zero1D"Constant* value*: : † NAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_One1DF TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SqrtHeadSizeWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_CalculatedScale"Div: rNAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : ē WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_CalculatedScaleSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactor"Identity: ļ SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt:  WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorSqrtTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Č past_key QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KReshapedRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentKey"Concat* axis : • past_keyTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : m RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ė past_value QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VReshapedTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentValue"Concat* axis : q TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentValue present_value"Identity: Ū RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentKeySAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ” OAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QSeqLen SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLenUAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : ySAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : xRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : n attn_maskUAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasShort"Identity: ĩ UAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasShortPAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBias"Identity: ģ PAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBias[Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: à [Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasCausalOrNotQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ƒ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeadsQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NGQACond1"Equal: Ŧ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NGQACond1PAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond1"Not: ƒ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeadsSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_DivNumHeads"Div: ž SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_DivNumHeadsTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ‰ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeadsYAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_RemainderNumHeads"Mod: † YAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_RemainderNumHeads NAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_Zero1DPAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond2"Equal: ü PAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond1 PAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond2OAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond"And: Ö OAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_GQACond TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_IDivNumHeads MAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_One1DUAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_InterleaveDim"Where: pMAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_Two1D"Constant* value*: : … RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentKey MAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_Two1DSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: ‡ TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_PresentValue MAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_Two1DSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ” QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_BatchSize RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeads UAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_InterleaveDim SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLen RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSizeTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KExpandShape"Concat* axis : ˆ SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KUnsqueezed TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KExpandShapeQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KExpanded"Expand: “ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_BatchSize RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KVNumHeads UAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_InterleaveDim SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLen QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VHeadSizeTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VExpandShape"Concat* axis : ˆ SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VUnsqueezed TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VExpandShapeQAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VExpanded"Expand: ŋ QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_BatchSize QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLen RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKHeadSizeWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : ž QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_BatchSize QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QNumHeads SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_NewKVSeqLen QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VHeadSizeWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VAttentionShape"Concat* axis :  QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KExpanded WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KAttentionShapeWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KAttentionInput"Reshape:  QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VExpanded WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VAttentionShapeWAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VAttentionInput"Reshape: Í WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KAttentionInputRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ :  QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QReshaped TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorFOAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QScaled"Mul: ‚ RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KTranspose TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_ScaleFactorFOAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KScaled"Mul: ‚ OAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QScaled OAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_KScaledTAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeight"MatMul: Ŋ TAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeightRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnCast"Cast* to : Œ RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnCast QAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnBiasT\Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: Į \Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeightWithBias[Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Å [Attention_test_attention_4d_gqa_with_past_and_present_expanded_function_QKAttnWeightSoftcapSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : ģ SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SoftmaxCastYAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax:  YAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_AttnWeightSoftmaxRAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : Œ RAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_SoftmaxOut WAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_VAttentionInputSAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_YPreReshape"MatMul: d SAttention_test_attention_4d_gqa_with_past_and_present_expanded_function_YPreReshapeY"Identity:4test_attention_4d_gqa_with_past_and_present_expandedZ Q     Z K     Z V     Z attn_mask   Z" past_key     Z$ past_value     b Y     b% present_key     b' present_value     B 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8™5§9K&9test_attention_4d_gqa_with_past_and_present_fp16_expanded/000077500000000000000000000000001511334557700346315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000350061511334557700366410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_with_past_and_present_fp16_expanded  backend-test:ís ~ QVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_BatchSize"Shape* start * end : Ž QTAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KUAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : g QVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QReshaped"Identity: g KVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KReshaped"Identity: g VVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VReshaped"Identity: Ķ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QReshapedVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads"Shape* start * end : Ô VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KReshapedWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeads"Shape* start * end : Ô VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QReshapedWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSize"Shape* start * end : Æ WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSizeXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSizeF"Cast* to : Ķ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VReshapedVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VHeadSize"Shape* start * end : Ŋ XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSizeFYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SqrtHeadSize"Sqrt: uRAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_One1D"Constant* value*: : ySAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_One1DF"Constant* value* "€? : vSAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_Zero1D"Constant* value*: : • SAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_One1DF YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SqrtHeadSize\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_CalculatedScale"Div: wSAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleF"Constant* value*"€? : Ä \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_CalculatedScaleXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactor"Identity: Ā XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactor\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactorSqrt"Sqrt: Ė \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactorSqrtYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactorF"Cast* to  : Ō past_key VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KReshapedWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentKey"Concat* axis : š past_keyYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : r WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentKey present_key"Identity: Ö past_value VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VReshapedYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentValue"Concat* axis : v YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentValue present_value"Identity: č WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentKeyXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Ŗ TAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QSeqLen XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLenZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasShape"Concat* axis : ~XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_FloatNegInf"Constant* value* "€˙ : }WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScalarZero"Constant* value* " : s attn_maskZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasShort"Identity: ŋ ZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasShortUAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBias"Identity: Å UAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBias`Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasCausalOrNot"Identity: Í `Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasCausalOrNotVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasT"Cast* to  : ’ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeadsVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NGQACond1"Equal: ļ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NGQACond1UAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond1"Not: ’ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeadsXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_DivNumHeads"Div: Č XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_DivNumHeadsYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_IDivNumHeads"Cast* to : ˜ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeads^Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_RemainderNumHeads"Mod: • ^Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_RemainderNumHeads SAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_Zero1DUAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond2"Equal: ‹ UAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond1 UAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond2TAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond"And: ę TAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_GQACond YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_IDivNumHeads RAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_One1DZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_InterleaveDim"Where: uRAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_Two1D"Constant* value*: : ” WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentKey RAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_Two1DXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KUnsqueezed" Unsqueeze: – YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_PresentValue RAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_Two1DXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VUnsqueezed" Unsqueeze: ˛ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_BatchSize WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeads ZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_InterleaveDim XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLen WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSizeYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KExpandShape"Concat* axis : — XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KUnsqueezed YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KExpandShapeVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KExpanded"Expand: ą VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_BatchSize WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KVNumHeads ZAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_InterleaveDim XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLen VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VHeadSizeYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VExpandShape"Concat* axis : — XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VUnsqueezed YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VExpandShapeVAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VExpanded"Expand: Ø VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_BatchSize VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLen WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKHeadSize\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KAttentionShape"Concat* axis : × VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_BatchSize VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QNumHeads XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_NewKVSeqLen VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VHeadSize\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VAttentionShape"Concat* axis : Ÿ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KExpanded \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KAttentionShape\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KAttentionInput"Reshape: Ÿ VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VExpanded \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VAttentionShape\Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VAttentionInput"Reshape: × \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KAttentionInputWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KTranspose" Transpose* perm@@@@ :  VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QReshaped YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactorFTAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QScaled"Mul: ‘ WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KTranspose YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_ScaleFactorFTAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KScaled"Mul: ‘ TAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QScaled TAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_KScaledYAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeight"MatMul: Į YAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeightWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnCast"Cast* to  : › WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnCast VAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnBiasTaAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeightWithBias"Add: Ņ aAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeightWithBias`Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeightSoftcap"Identity: Ī `Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_QKAttnWeightSoftcapXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SoftmaxCast"Cast* to  : Å XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SoftmaxCast^Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnWeightSoftmax"Softmax: Ė ^Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_AttnWeightSoftmaxWAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SoftmaxOut"Cast* to  : › WAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_SoftmaxOut \Attention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_VAttentionInputXAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_YPreReshape"MatMul: i XAttention_test_attention_4d_gqa_with_past_and_present_fp16_expanded_function_YPreReshapeY"Identity:9test_attention_4d_gqa_with_past_and_present_fp16_expandedZ Q      Z K      Z V      Z attn_mask    Z" past_key      Z$ past_value      b Y      b% present_key      b' present_value      B test_data_set_0/000077500000000000000000000000001511334557700376735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_with_past_and_present_fp16_expandedinput_0.pb000066400000000000000000000022201511334557700415700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_gqa_with_past_and_present_fp16_expanded/test_data_set_0  BQJ€ d8š9Ō8\8Į6+97";ļ;#6U:;8‹8h;Œ,”--%Š:::ö:Ô;e:b7?:’/9–0;-8ĸ6<42:L7Œ8Ī$ņ8æ8ī8;t9Á5ū6•9ĩ+V9]9ģ2 0 5Ō587č;ˆ.¯2)1:9 4v7Ō31/@9l0J2æ5‘:7.´:&.Đ;€7Đ;×8ę9)†4ą/Ŋ4™/5Ą6,Š9ˆ8?408.œ8o;5W980ģ9Ą4Ũ1ą8&%ĸ:Īl9R4â9ŗ;ö3œ8Ŋ8”8#3Ÿ;'7Å:™9Â4ƒ:X6 ;Ļ8;Š9Í98Ļ;'9Č6Ú8ę$Ķ4H9¤4ō8Ü6V0Æ48ē8˜8:989į6,;â5ų6#;s:ĸ9j.[;ˇ9ū;Č0ō:31í8í/É:u:Ž8„6m,”9B7Į9î:Î;Ų:˙!Â5×9~1+8õ*f2ž$Y:*3‡5m;Ŗ9(E1ų8ž83y;é8I8¸8×9ū4_6ˇ2õ1Ž;ë9Ų7G34m+ķ6ũ4’9 6ŋ1Q&N,o9B7K8,;ė;ņ2N974I%:5#6ĩ8Ļ:9û:`4b:ņ1Ÿ;€9å2”;Ų94Ķ2%8’&¤2Ė6ũ5k7q4˛8é:†/$8:0ŧ9V6†8Ũ1Ŗ0Ī7ą5†;:ũ9;;W-k8­8˛;­4ĩ3k.5$p;\9H:‚4ą8,Å7Ō;;i5ą;j3˜;ˆ;e: 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Čø>K&?ßé>Á$*?\?YŽ>x…ū>d?Ėļø>ĩąī>Ôą?ÆEÆ>?f?"Ö>g?ŋÜ>Vä?aŋī>kÄ?Å+Æ>ę‡?Ģ˙Õ>ũ ?;(Ü>ōč?Žåī>…¸?ŧÆ>R‚?WÖ>O ?25Ü>F?iđī>˛?{Æ>6i?:ņÕ>Œ'?žeÜ>Ē?f4*?ĪFü>7é0?Eķ(?äŊ>žÄ°>~Ęį>&\Í>ë5*?˜3ü>öå0?,ī(?fŊ>'ư>(áį>ŒSÍ>M*?˙û>ĮØ0?ø(?TŊ>ëí°>Đį>ˇNÍ>û)*?=ü>ĸÚ0?p)?Xųŧ>8ķ°>Išį>įLÍ> :?ÎŅ6>×â>†×>™ŗŋ>°?a­¤>œËņ>Kf?āũ6>ęīâ>j$×>{Īŋ>Dą?gˤ>ō>J?7ˇ6>ėâ>n-×>ۋŋ>7ˇ?’ķ¤>¤Ūņ>:?Ǥ6>7Ôâ>3×>Ŋ[ŋ>ŋŊ?íĨ>.×ņ>íÍ2?iKõ>jxŋ> _?„F?ũY?Ké>-Į?Uē2?Ągõ>Fŋ>QZ?r?Đ>?Ū1é>ø×?üË2?G‚õ>Ŋ8ŋ>žD?*?WW?Ŗé>Œæ?oŗ2?:hõ>ˇAŋ>ÁG?Đ?čk?3Té>Ã?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_scaled_expanded/000077500000000000000000000000001511334557700300735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_scaled_expanded/model.onnx000066400000000000000000000251171511334557700321050ustar00rootroot00000000000000  backend-test:ļT f Q>Attention_test_attention_4d_scaled_expanded_function_BatchSize"Shape* start * end : v QAttention_test_attention_4d_scaled_expanded_function_QReshaped"Identity: O K>Attention_test_attention_4d_scaled_expanded_function_KReshaped"Identity: O V>Attention_test_attention_4d_scaled_expanded_function_VReshaped"Identity: Ŗ >Attention_test_attention_4d_scaled_expanded_function_QReshaped>Attention_test_attention_4d_scaled_expanded_function_QNumHeads"Shape* start * end : ¤ >Attention_test_attention_4d_scaled_expanded_function_KReshaped?Attention_test_attention_4d_scaled_expanded_function_KVNumHeads"Shape* start * end : ¤ >Attention_test_attention_4d_scaled_expanded_function_QReshaped?Attention_test_attention_4d_scaled_expanded_function_QKHeadSize"Shape* start * end : – ?Attention_test_attention_4d_scaled_expanded_function_QKHeadSize@Attention_test_attention_4d_scaled_expanded_function_QKHeadSizeF"Cast* to : Ŗ >Attention_test_attention_4d_scaled_expanded_function_VReshaped>Attention_test_attention_4d_scaled_expanded_function_VHeadSize"Shape* start * end :  @Attention_test_attention_4d_scaled_expanded_function_QKHeadSizeFAAttention_test_attention_4d_scaled_expanded_function_SqrtHeadSize"Sqrt: ]:Attention_test_attention_4d_scaled_expanded_function_One1D"Constant* value*: : a;Attention_test_attention_4d_scaled_expanded_function_One1DF"Constant* value* "€? : ^;Attention_test_attention_4d_scaled_expanded_function_Zero1D"Constant* value*: : Í ;Attention_test_attention_4d_scaled_expanded_function_One1DF AAttention_test_attention_4d_scaled_expanded_function_SqrtHeadSizeDAttention_test_attention_4d_scaled_expanded_function_CalculatedScale"Div: _;Attention_test_attention_4d_scaled_expanded_function_ScaleF"Constant* value*" ×#< : ‹ ;Attention_test_attention_4d_scaled_expanded_function_ScaleF@Attention_test_attention_4d_scaled_expanded_function_ScaleFactor"Identity:  @Attention_test_attention_4d_scaled_expanded_function_ScaleFactorDAttention_test_attention_4d_scaled_expanded_function_ScaleFactorSqrt"Sqrt: œ DAttention_test_attention_4d_scaled_expanded_function_ScaleFactorSqrtAAttention_test_attention_4d_scaled_expanded_function_ScaleFactorF"Cast* to :  >Attention_test_attention_4d_scaled_expanded_function_KReshaped?Attention_test_attention_4d_scaled_expanded_function_PresentKey"Identity: dAAttention_test_attention_4d_scaled_expanded_function_PastKVSeqLen"Constant* value*: :  >Attention_test_attention_4d_scaled_expanded_function_VReshapedAAttention_test_attention_4d_scaled_expanded_function_PresentValue"Identity: ¸ ?Attention_test_attention_4d_scaled_expanded_function_PresentKey@Attention_test_attention_4d_scaled_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Û Attention_test_attention_4d_scaled_expanded_function_AttnBiasT"Cast* to : Ę >Attention_test_attention_4d_scaled_expanded_function_QNumHeads ?Attention_test_attention_4d_scaled_expanded_function_KVNumHeads>Attention_test_attention_4d_scaled_expanded_function_NGQACond1"Equal: † >Attention_test_attention_4d_scaled_expanded_function_NGQACond1=Attention_test_attention_4d_scaled_expanded_function_GQACond1"Not: Ę >Attention_test_attention_4d_scaled_expanded_function_QNumHeads ?Attention_test_attention_4d_scaled_expanded_function_KVNumHeads@Attention_test_attention_4d_scaled_expanded_function_DivNumHeads"Div: ˜ @Attention_test_attention_4d_scaled_expanded_function_DivNumHeadsAAttention_test_attention_4d_scaled_expanded_function_IDivNumHeads"Cast* to : Đ >Attention_test_attention_4d_scaled_expanded_function_QNumHeads ?Attention_test_attention_4d_scaled_expanded_function_KVNumHeadsFAttention_test_attention_4d_scaled_expanded_function_RemainderNumHeads"Mod: Í FAttention_test_attention_4d_scaled_expanded_function_RemainderNumHeads ;Attention_test_attention_4d_scaled_expanded_function_Zero1D=Attention_test_attention_4d_scaled_expanded_function_GQACond2"Equal: à =Attention_test_attention_4d_scaled_expanded_function_GQACond1 =Attention_test_attention_4d_scaled_expanded_function_GQACond2Attention_test_attention_4d_scaled_expanded_function_BatchSize ?Attention_test_attention_4d_scaled_expanded_function_KVNumHeads BAttention_test_attention_4d_scaled_expanded_function_InterleaveDim @Attention_test_attention_4d_scaled_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_scaled_expanded_function_QKHeadSizeAAttention_test_attention_4d_scaled_expanded_function_KExpandShape"Concat* axis : Ī @Attention_test_attention_4d_scaled_expanded_function_KUnsqueezed AAttention_test_attention_4d_scaled_expanded_function_KExpandShape>Attention_test_attention_4d_scaled_expanded_function_KExpanded"Expand: Ą >Attention_test_attention_4d_scaled_expanded_function_BatchSize ?Attention_test_attention_4d_scaled_expanded_function_KVNumHeads BAttention_test_attention_4d_scaled_expanded_function_InterleaveDim @Attention_test_attention_4d_scaled_expanded_function_NewKVSeqLen >Attention_test_attention_4d_scaled_expanded_function_VHeadSizeAAttention_test_attention_4d_scaled_expanded_function_VExpandShape"Concat* axis : Ī @Attention_test_attention_4d_scaled_expanded_function_VUnsqueezed AAttention_test_attention_4d_scaled_expanded_function_VExpandShape>Attention_test_attention_4d_scaled_expanded_function_VExpanded"Expand: ā >Attention_test_attention_4d_scaled_expanded_function_BatchSize >Attention_test_attention_4d_scaled_expanded_function_QNumHeads @Attention_test_attention_4d_scaled_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_scaled_expanded_function_QKHeadSizeDAttention_test_attention_4d_scaled_expanded_function_KAttentionShape"Concat* axis : ß >Attention_test_attention_4d_scaled_expanded_function_BatchSize >Attention_test_attention_4d_scaled_expanded_function_QNumHeads @Attention_test_attention_4d_scaled_expanded_function_NewKVSeqLen >Attention_test_attention_4d_scaled_expanded_function_VHeadSizeDAttention_test_attention_4d_scaled_expanded_function_VAttentionShape"Concat* axis : × >Attention_test_attention_4d_scaled_expanded_function_KExpanded DAttention_test_attention_4d_scaled_expanded_function_KAttentionShapeDAttention_test_attention_4d_scaled_expanded_function_KAttentionInput"Reshape: × >Attention_test_attention_4d_scaled_expanded_function_VExpanded DAttention_test_attention_4d_scaled_expanded_function_VAttentionShapeDAttention_test_attention_4d_scaled_expanded_function_VAttentionInput"Reshape: § DAttention_test_attention_4d_scaled_expanded_function_KAttentionInput?Attention_test_attention_4d_scaled_expanded_function_KTranspose" Transpose* perm@@@@ : Č >Attention_test_attention_4d_scaled_expanded_function_QReshaped AAttention_test_attention_4d_scaled_expanded_function_ScaleFactorFAttention_test_attention_4d_scaled_expanded_function_AttnBiasTIAttention_test_attention_4d_scaled_expanded_function_QKAttnWeightWithBias"Add: Ą IAttention_test_attention_4d_scaled_expanded_function_QKAttnWeightWithBiasHAttention_test_attention_4d_scaled_expanded_function_QKAttnWeightSoftcap"Identity: Ÿ HAttention_test_attention_4d_scaled_expanded_function_QKAttnWeightSoftcap@Attention_test_attention_4d_scaled_expanded_function_SoftmaxCast"Cast* to : • @Attention_test_attention_4d_scaled_expanded_function_SoftmaxCastFAttention_test_attention_4d_scaled_expanded_function_AttnWeightSoftmax"Softmax: œ FAttention_test_attention_4d_scaled_expanded_function_AttnWeightSoftmax?Attention_test_attention_4d_scaled_expanded_function_SoftmaxOut"Cast* to : Ķ ?Attention_test_attention_4d_scaled_expanded_function_SoftmaxOut 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M?ũŋ¨>ŠĨ?Հ?@= ?_Ģ?Į}å>ė-,?Á ?´c¯>­ˆ?š-?¨F?”Ķ?Ŗã>€Đ,?÷Æ ?Qr¯>Î ?žĀ?ų9?ÕÂ?“:ė>Œ+?š?ȌŦ>ŧ#?cD?…î˙>Äí>ąUũ>iWÃ>äā?ËpĖ>hs?ø0â>ī?S{ī>üũ?Ũ’ŋ>2Û ?dĮ>ãė?ŗå>rŽ?ߗô>°ˇū>ŧîŊ>Š ?’ÕÉ>Į? įæ>˜%?ä{ö>…Ķü>F}ē> ņ?TÅ>Į^?U(í>Ze?K%?=ō>øÃ/?¤L%?”@Ä>Z ē>ŗî>‡Ī>’Ÿ%?Ö>đ>Ég/? â$?ą Ä>„÷š>=+ņ>†kÎ>ąf#?ë>Ž.?z)&?zéÄ>w™Ŋ>™î>XÛÍ>$?ÔGí>,.?Pė(?Ę Ã>F”ŋ>×č> ŗÍ>ŗä ?îæ=>šĐč>ēsĐ>XÆÉ>%Ø?¤>ĩž÷>lE?ß×A>‡~ë>eaÆ>]Ė>ûž?Ū-§> ũ>Á?ą%9>Žë>ƒĮ>xÄ>>m ? čĢ>Ĩ<ø>d‹ ?ËŨ6>˙ é>ĩœÃ>už>ķi ?]¯>ˇ÷>¸3?‰'đ>ŠŲÅ>ŋą ?;U ?-q ?čkų>Ö˙ū>Á?1?Mķ>ģ@Ā>gd?ÖŲ?øã?Ë9ī>ņ?vĻ3?Ŋīõ>°ŋ>Ō*?|š?ąj ?Æũ> Ę?aŖ0?€Nķ>ëĨŋ>˜Đ?ūõ?J§"?ŗæō>Ļŗũ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_softcap_expanded/000077500000000000000000000000001511334557700302775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_softcap_expanded/model.onnx000066400000000000000000000265721511334557700323170ustar00rootroot00000000000000  backend-test:áZ g Q?Attention_test_attention_4d_softcap_expanded_function_BatchSize"Shape* start * end : w Q=Attention_test_attention_4d_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : x K>Attention_test_attention_4d_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : P Q?Attention_test_attention_4d_softcap_expanded_function_QReshaped"Identity: P K?Attention_test_attention_4d_softcap_expanded_function_KReshaped"Identity: P V?Attention_test_attention_4d_softcap_expanded_function_VReshaped"Identity: Ĩ ?Attention_test_attention_4d_softcap_expanded_function_QReshaped?Attention_test_attention_4d_softcap_expanded_function_QNumHeads"Shape* start * end : Ļ ?Attention_test_attention_4d_softcap_expanded_function_KReshaped@Attention_test_attention_4d_softcap_expanded_function_KVNumHeads"Shape* start * end : Ļ ?Attention_test_attention_4d_softcap_expanded_function_QReshaped@Attention_test_attention_4d_softcap_expanded_function_QKHeadSize"Shape* start * end : ˜ @Attention_test_attention_4d_softcap_expanded_function_QKHeadSizeAAttention_test_attention_4d_softcap_expanded_function_QKHeadSizeF"Cast* to : Ĩ ?Attention_test_attention_4d_softcap_expanded_function_VReshaped?Attention_test_attention_4d_softcap_expanded_function_VHeadSize"Shape* start * end :  AAttention_test_attention_4d_softcap_expanded_function_QKHeadSizeFBAttention_test_attention_4d_softcap_expanded_function_SqrtHeadSize"Sqrt: ^;Attention_test_attention_4d_softcap_expanded_function_One1D"Constant* value*: : bAttention_test_attention_4d_softcap_expanded_function_AttnBias"ConstantOfShape: — >Attention_test_attention_4d_softcap_expanded_function_AttnBiasIAttention_test_attention_4d_softcap_expanded_function_AttnBiasCausalOrNot"Identity: Ÿ IAttention_test_attention_4d_softcap_expanded_function_AttnBiasCausalOrNot?Attention_test_attention_4d_softcap_expanded_function_AttnBiasT"Cast* to : Í ?Attention_test_attention_4d_softcap_expanded_function_QNumHeads @Attention_test_attention_4d_softcap_expanded_function_KVNumHeads?Attention_test_attention_4d_softcap_expanded_function_NGQACond1"Equal: ˆ ?Attention_test_attention_4d_softcap_expanded_function_NGQACond1>Attention_test_attention_4d_softcap_expanded_function_GQACond1"Not: Í ?Attention_test_attention_4d_softcap_expanded_function_QNumHeads @Attention_test_attention_4d_softcap_expanded_function_KVNumHeadsAAttention_test_attention_4d_softcap_expanded_function_DivNumHeads"Div: š AAttention_test_attention_4d_softcap_expanded_function_DivNumHeadsBAttention_test_attention_4d_softcap_expanded_function_IDivNumHeads"Cast* to : Ķ ?Attention_test_attention_4d_softcap_expanded_function_QNumHeads @Attention_test_attention_4d_softcap_expanded_function_KVNumHeadsGAttention_test_attention_4d_softcap_expanded_function_RemainderNumHeads"Mod: Đ GAttention_test_attention_4d_softcap_expanded_function_RemainderNumHeads Attention_test_attention_4d_softcap_expanded_function_GQACond2"Equal: Æ >Attention_test_attention_4d_softcap_expanded_function_GQACond1 >Attention_test_attention_4d_softcap_expanded_function_GQACond2=Attention_test_attention_4d_softcap_expanded_function_GQACond"And: Ž =Attention_test_attention_4d_softcap_expanded_function_GQACond BAttention_test_attention_4d_softcap_expanded_function_IDivNumHeads ;Attention_test_attention_4d_softcap_expanded_function_One1DCAttention_test_attention_4d_softcap_expanded_function_InterleaveDim"Where: ^;Attention_test_attention_4d_softcap_expanded_function_Two1D"Constant* value*: : Ī @Attention_test_attention_4d_softcap_expanded_function_PresentKey ;Attention_test_attention_4d_softcap_expanded_function_Two1DAAttention_test_attention_4d_softcap_expanded_function_KUnsqueezed" Unsqueeze: Ņ BAttention_test_attention_4d_softcap_expanded_function_PresentValue ;Attention_test_attention_4d_softcap_expanded_function_Two1DAAttention_test_attention_4d_softcap_expanded_function_VUnsqueezed" Unsqueeze: ¨ ?Attention_test_attention_4d_softcap_expanded_function_BatchSize @Attention_test_attention_4d_softcap_expanded_function_KVNumHeads CAttention_test_attention_4d_softcap_expanded_function_InterleaveDim AAttention_test_attention_4d_softcap_expanded_function_NewKVSeqLen @Attention_test_attention_4d_softcap_expanded_function_QKHeadSizeBAttention_test_attention_4d_softcap_expanded_function_KExpandShape"Concat* axis : Ō AAttention_test_attention_4d_softcap_expanded_function_KUnsqueezed BAttention_test_attention_4d_softcap_expanded_function_KExpandShape?Attention_test_attention_4d_softcap_expanded_function_KExpanded"Expand: § ?Attention_test_attention_4d_softcap_expanded_function_BatchSize @Attention_test_attention_4d_softcap_expanded_function_KVNumHeads CAttention_test_attention_4d_softcap_expanded_function_InterleaveDim AAttention_test_attention_4d_softcap_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_softcap_expanded_function_VHeadSizeBAttention_test_attention_4d_softcap_expanded_function_VExpandShape"Concat* axis : Ō AAttention_test_attention_4d_softcap_expanded_function_VUnsqueezed BAttention_test_attention_4d_softcap_expanded_function_VExpandShape?Attention_test_attention_4d_softcap_expanded_function_VExpanded"Expand: å ?Attention_test_attention_4d_softcap_expanded_function_BatchSize ?Attention_test_attention_4d_softcap_expanded_function_QNumHeads AAttention_test_attention_4d_softcap_expanded_function_NewKVSeqLen @Attention_test_attention_4d_softcap_expanded_function_QKHeadSizeEAttention_test_attention_4d_softcap_expanded_function_KAttentionShape"Concat* axis : ä ?Attention_test_attention_4d_softcap_expanded_function_BatchSize ?Attention_test_attention_4d_softcap_expanded_function_QNumHeads AAttention_test_attention_4d_softcap_expanded_function_NewKVSeqLen ?Attention_test_attention_4d_softcap_expanded_function_VHeadSizeEAttention_test_attention_4d_softcap_expanded_function_VAttentionShape"Concat* axis : Ú ?Attention_test_attention_4d_softcap_expanded_function_KExpanded EAttention_test_attention_4d_softcap_expanded_function_KAttentionShapeEAttention_test_attention_4d_softcap_expanded_function_KAttentionInput"Reshape: Ú ?Attention_test_attention_4d_softcap_expanded_function_VExpanded EAttention_test_attention_4d_softcap_expanded_function_VAttentionShapeEAttention_test_attention_4d_softcap_expanded_function_VAttentionInput"Reshape: Š EAttention_test_attention_4d_softcap_expanded_function_KAttentionInput@Attention_test_attention_4d_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : Ë ?Attention_test_attention_4d_softcap_expanded_function_QReshaped BAttention_test_attention_4d_softcap_expanded_function_ScaleFactorF=Attention_test_attention_4d_softcap_expanded_function_QScaled"Mul: Ė @Attention_test_attention_4d_softcap_expanded_function_KTranspose BAttention_test_attention_4d_softcap_expanded_function_ScaleFactorF=Attention_test_attention_4d_softcap_expanded_function_KScaled"Mul: Ė =Attention_test_attention_4d_softcap_expanded_function_QScaled =Attention_test_attention_4d_softcap_expanded_function_KScaledBAttention_test_attention_4d_softcap_expanded_function_QKAttnWeight"MatMul: ™ BAttention_test_attention_4d_softcap_expanded_function_QKAttnWeight@Attention_test_attention_4d_softcap_expanded_function_QKAttnCast"Cast* to : Ö @Attention_test_attention_4d_softcap_expanded_function_QKAttnCast ?Attention_test_attention_4d_softcap_expanded_function_AttnBiasTJAttention_test_attention_4d_softcap_expanded_function_QKAttnWeightWithBias"Add: c=Attention_test_attention_4d_softcap_expanded_function_Softcap"Constant* value* "@ : ’ =Attention_test_attention_4d_softcap_expanded_function_Softcap>Attention_test_attention_4d_softcap_expanded_function_SoftcapF"Cast* to : Õ JAttention_test_attention_4d_softcap_expanded_function_QKAttnWeightWithBias >Attention_test_attention_4d_softcap_expanded_function_SoftcapF@Attention_test_attention_4d_softcap_expanded_function_SoftcapDiv"Div:  @Attention_test_attention_4d_softcap_expanded_function_SoftcapDivAAttention_test_attention_4d_softcap_expanded_function_SoftcapTanh"Tanh: Õ AAttention_test_attention_4d_softcap_expanded_function_SoftcapTanh >Attention_test_attention_4d_softcap_expanded_function_SoftcapFIAttention_test_attention_4d_softcap_expanded_function_QKAttnWeightSoftcap"Mul: Ą IAttention_test_attention_4d_softcap_expanded_function_QKAttnWeightSoftcapAAttention_test_attention_4d_softcap_expanded_function_SoftmaxCast"Cast* to : — AAttention_test_attention_4d_softcap_expanded_function_SoftmaxCastGAttention_test_attention_4d_softcap_expanded_function_AttnWeightSoftmax"Softmax: ž GAttention_test_attention_4d_softcap_expanded_function_AttnWeightSoftmax@Attention_test_attention_4d_softcap_expanded_function_SoftmaxOut"Cast* to : Ö @Attention_test_attention_4d_softcap_expanded_function_SoftmaxOut EAttention_test_attention_4d_softcap_expanded_function_VAttentionInputAAttention_test_attention_4d_softcap_expanded_function_YPreReshape"MatMul: R AAttention_test_attention_4d_softcap_expanded_function_YPreReshapeY"Identity:"test_attention_4d_softcap_expandedZ Q     Z K     Z V     b Y     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_softcap_expanded/test_data_set_0/000077500000000000000000000000001511334557700333415ustar00rootroot00000000000000input_0.pb000066400000000000000000000014201511334557700351600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_softcap_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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`?ôB@?Á]m?å-í<Le?ĮūČ>Ũ`?E×0?ãÂ|?V`B?˜Ĩē>­E?ļĀ>šÕē>F•…>Ōīũ>‚†.?‹˙>Á=?eđ=|Ž#>¸?=ہx?ķũ|;ĢŨ6>Öä?ĨĻ=øÃa?98?Uew?gô?ƒÎ™>Ŧ ?#Jn?ŸP?\Έ>5`?"lž>†Qĩ:$Ą}>īĸ>ר[?íĀę>ō ã>™Ŧ>ta?Fíq?†ė}?;äĀ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_expanded/000077500000000000000000000000001511334557700332245ustar00rootroot00000000000000model.onnx000066400000000000000000000323551511334557700351610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_expanded  backend-test:Ôi u QMAttention_test_attention_4d_with_past_and_present_expanded_function_BatchSize"Shape* start * end : … QKAttention_test_attention_4d_with_past_and_present_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : † KLAttention_test_attention_4d_with_past_and_present_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ^ QMAttention_test_attention_4d_with_past_and_present_expanded_function_QReshaped"Identity: ^ KMAttention_test_attention_4d_with_past_and_present_expanded_function_KReshaped"Identity: ^ VMAttention_test_attention_4d_with_past_and_present_expanded_function_VReshaped"Identity: Á MAttention_test_attention_4d_with_past_and_present_expanded_function_QReshapedMAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads"Shape* start * end :  MAttention_test_attention_4d_with_past_and_present_expanded_function_KReshapedNAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeads"Shape* start * end :  MAttention_test_attention_4d_with_past_and_present_expanded_function_QReshapedNAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSize"Shape* start * end : ´ NAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSizeOAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSizeF"Cast* to : Á MAttention_test_attention_4d_with_past_and_present_expanded_function_VReshapedMAttention_test_attention_4d_with_past_and_present_expanded_function_VHeadSize"Shape* start * end : Ģ OAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSizeFPAttention_test_attention_4d_with_past_and_present_expanded_function_SqrtHeadSize"Sqrt: lIAttention_test_attention_4d_with_past_and_present_expanded_function_One1D"Constant* value*: : pJAttention_test_attention_4d_with_past_and_present_expanded_function_One1DF"Constant* value* "€? : mJAttention_test_attention_4d_with_past_and_present_expanded_function_Zero1D"Constant* value*: : ú JAttention_test_attention_4d_with_past_and_present_expanded_function_One1DF PAttention_test_attention_4d_with_past_and_present_expanded_function_SqrtHeadSizeSAttention_test_attention_4d_with_past_and_present_expanded_function_CalculatedScale"Div: nJAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleF"Constant* value*"€? : ˛ SAttention_test_attention_4d_with_past_and_present_expanded_function_CalculatedScaleOAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactor"Identity: Ž OAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorSAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorSqrt"Sqrt: ē SAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorSqrtPAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorF"Cast* to : Ā past_key MAttention_test_attention_4d_with_past_and_present_expanded_function_KReshapedNAttention_test_attention_4d_with_past_and_present_expanded_function_PresentKey"Concat* axis : ‘ past_keyPAttention_test_attention_4d_with_past_and_present_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : i NAttention_test_attention_4d_with_past_and_present_expanded_function_PresentKey present_key"Identity: Ä past_value MAttention_test_attention_4d_with_past_and_present_expanded_function_VReshapedPAttention_test_attention_4d_with_past_and_present_expanded_function_PresentValue"Concat* axis : m PAttention_test_attention_4d_with_past_and_present_expanded_function_PresentValue present_value"Identity: Ö NAttention_test_attention_4d_with_past_and_present_expanded_function_PresentKeyOAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ˆ KAttention_test_attention_4d_with_past_and_present_expanded_function_QSeqLen OAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLenQAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasShape"Concat* axis : uOAttention_test_attention_4d_with_past_and_present_expanded_function_FloatNegInf"Constant* value* "€˙ : tNAttention_test_attention_4d_with_past_and_present_expanded_function_ScalarZero"Constant* value* " : j attn_maskQAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasShort"Identity: ­ QAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasShortLAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBias"Identity: ŗ LAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasWAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasCausalOrNot"Identity: ģ WAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasCausalOrNotMAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasT"Cast* to : ÷ MAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeadsMAttention_test_attention_4d_with_past_and_present_expanded_function_NGQACond1"Equal: ¤ MAttention_test_attention_4d_with_past_and_present_expanded_function_NGQACond1LAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond1"Not: ÷ MAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeadsOAttention_test_attention_4d_with_past_and_present_expanded_function_DivNumHeads"Div: ļ OAttention_test_attention_4d_with_past_and_present_expanded_function_DivNumHeadsPAttention_test_attention_4d_with_past_and_present_expanded_function_IDivNumHeads"Cast* to : ũ MAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads NAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeadsUAttention_test_attention_4d_with_past_and_present_expanded_function_RemainderNumHeads"Mod: ú UAttention_test_attention_4d_with_past_and_present_expanded_function_RemainderNumHeads JAttention_test_attention_4d_with_past_and_present_expanded_function_Zero1DLAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond2"Equal: đ LAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond1 LAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond2KAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond"And: Æ KAttention_test_attention_4d_with_past_and_present_expanded_function_GQACond PAttention_test_attention_4d_with_past_and_present_expanded_function_IDivNumHeads IAttention_test_attention_4d_with_past_and_present_expanded_function_One1DQAttention_test_attention_4d_with_past_and_present_expanded_function_InterleaveDim"Where: lIAttention_test_attention_4d_with_past_and_present_expanded_function_Two1D"Constant* value*: : ų NAttention_test_attention_4d_with_past_and_present_expanded_function_PresentKey IAttention_test_attention_4d_with_past_and_present_expanded_function_Two1DOAttention_test_attention_4d_with_past_and_present_expanded_function_KUnsqueezed" Unsqueeze: û PAttention_test_attention_4d_with_past_and_present_expanded_function_PresentValue IAttention_test_attention_4d_with_past_and_present_expanded_function_Two1DOAttention_test_attention_4d_with_past_and_present_expanded_function_VUnsqueezed" Unsqueeze: ü MAttention_test_attention_4d_with_past_and_present_expanded_function_BatchSize NAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeads QAttention_test_attention_4d_with_past_and_present_expanded_function_InterleaveDim OAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSizePAttention_test_attention_4d_with_past_and_present_expanded_function_KExpandShape"Concat* axis : ü OAttention_test_attention_4d_with_past_and_present_expanded_function_KUnsqueezed PAttention_test_attention_4d_with_past_and_present_expanded_function_KExpandShapeMAttention_test_attention_4d_with_past_and_present_expanded_function_KExpanded"Expand: û MAttention_test_attention_4d_with_past_and_present_expanded_function_BatchSize NAttention_test_attention_4d_with_past_and_present_expanded_function_KVNumHeads QAttention_test_attention_4d_with_past_and_present_expanded_function_InterleaveDim OAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLen MAttention_test_attention_4d_with_past_and_present_expanded_function_VHeadSizePAttention_test_attention_4d_with_past_and_present_expanded_function_VExpandShape"Concat* axis : ü OAttention_test_attention_4d_with_past_and_present_expanded_function_VUnsqueezed PAttention_test_attention_4d_with_past_and_present_expanded_function_VExpandShapeMAttention_test_attention_4d_with_past_and_present_expanded_function_VExpanded"Expand: Ģ MAttention_test_attention_4d_with_past_and_present_expanded_function_BatchSize MAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads OAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_past_and_present_expanded_function_QKHeadSizeSAttention_test_attention_4d_with_past_and_present_expanded_function_KAttentionShape"Concat* axis : Ē MAttention_test_attention_4d_with_past_and_present_expanded_function_BatchSize MAttention_test_attention_4d_with_past_and_present_expanded_function_QNumHeads OAttention_test_attention_4d_with_past_and_present_expanded_function_NewKVSeqLen MAttention_test_attention_4d_with_past_and_present_expanded_function_VHeadSizeSAttention_test_attention_4d_with_past_and_present_expanded_function_VAttentionShape"Concat* axis : „ MAttention_test_attention_4d_with_past_and_present_expanded_function_KExpanded SAttention_test_attention_4d_with_past_and_present_expanded_function_KAttentionShapeSAttention_test_attention_4d_with_past_and_present_expanded_function_KAttentionInput"Reshape: „ MAttention_test_attention_4d_with_past_and_present_expanded_function_VExpanded SAttention_test_attention_4d_with_past_and_present_expanded_function_VAttentionShapeSAttention_test_attention_4d_with_past_and_present_expanded_function_VAttentionInput"Reshape: Å SAttention_test_attention_4d_with_past_and_present_expanded_function_KAttentionInputNAttention_test_attention_4d_with_past_and_present_expanded_function_KTranspose" Transpose* perm@@@@ : õ MAttention_test_attention_4d_with_past_and_present_expanded_function_QReshaped PAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorFKAttention_test_attention_4d_with_past_and_present_expanded_function_QScaled"Mul: ö NAttention_test_attention_4d_with_past_and_present_expanded_function_KTranspose PAttention_test_attention_4d_with_past_and_present_expanded_function_ScaleFactorFKAttention_test_attention_4d_with_past_and_present_expanded_function_KScaled"Mul: ö KAttention_test_attention_4d_with_past_and_present_expanded_function_QScaled KAttention_test_attention_4d_with_past_and_present_expanded_function_KScaledPAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeight"MatMul: ĩ PAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeightNAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnCast"Cast* to : € NAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnCast MAttention_test_attention_4d_with_past_and_present_expanded_function_AttnBiasTXAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeightWithBias"Add: ŋ XAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeightWithBiasWAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeightSoftcap"Identity: Ŋ WAttention_test_attention_4d_with_past_and_present_expanded_function_QKAttnWeightSoftcapOAttention_test_attention_4d_with_past_and_present_expanded_function_SoftmaxCast"Cast* to : ŗ OAttention_test_attention_4d_with_past_and_present_expanded_function_SoftmaxCastUAttention_test_attention_4d_with_past_and_present_expanded_function_AttnWeightSoftmax"Softmax: ē UAttention_test_attention_4d_with_past_and_present_expanded_function_AttnWeightSoftmaxNAttention_test_attention_4d_with_past_and_present_expanded_function_SoftmaxOut"Cast* to : € NAttention_test_attention_4d_with_past_and_present_expanded_function_SoftmaxOut SAttention_test_attention_4d_with_past_and_present_expanded_function_VAttentionInputOAttention_test_attention_4d_with_past_and_present_expanded_function_YPreReshape"MatMul: ` 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…‚?õ?ŅqŒ?dNĒ?]gÖ?lņˆ?€˙€˙test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded/000077500000000000000000000000001511334557700411475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000540701511334557700431610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded  backend-test:ž° “ QkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BatchSize"Shape* start * end : Ŗ QiAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ¤ KjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : | QkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QReshaped"Identity: | KkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KReshaped"Identity: | VkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VReshaped"Identity: ũ kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QReshapedkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads"Shape* start * end : ū kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KReshapedlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeads"Shape* start * end : ū kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QReshapedlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSize"Shape* start * end : đ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSizemAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSizeF"Cast* to : ũ kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VReshapedkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VHeadSize"Shape* start * end : į mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSizeFnAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SqrtHeadSize"Sqrt: ŠgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One1D"Constant* value*: : ŽhAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One1DF"Constant* value* "€? : ‹hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Zero1D"Constant* value*: : Ô hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One1DF nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SqrtHeadSizeqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_CalculatedScale"Div: ŒhAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleF"Constant* value*"€? : î qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_CalculatedScalemAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactor"Identity: ę mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorSqrt"Sqrt: ö qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorSqrtnAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorF"Cast* to : ü past_key kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KReshapedlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentKey"Concat* axis : ¯ past_keynAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‡ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentKey present_key"Identity: € past_value kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VReshapednAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentValue"Concat* axis : ‹ nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentValue present_value"Identity: ’ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentKeymAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : â iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QSeqLen mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLenoAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasShape"Concat* axis : “mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_FloatNegInf"Constant* value* "€˙ : ’lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScalarZero"Constant* value* " : ˆ attn_maskoAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasShort"Identity: é oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasShortjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBias"Identity: ‰fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Zero"Constant* value*: : ˆeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One"Constant* value*: : Č fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Zero fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZerokAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZeroNoDim"Squeeze: Æ eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZerojAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_OneNoDim"Squeeze: Ú oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasShape kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZeroNoDimpAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SequenceLength"Gather: Ū oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasShape jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_OneNoDimuAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_TotalSequenceLength"Gather: Ā kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZeroNoDim pAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SequenceLength jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_OneNoDimjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow"Range: Î jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_OnelAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow2D" Unsqueeze: Å kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZeroNoDim uAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_TotalSequenceLength jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_OneNoDimjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeCol"Range: Ī jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeCol fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ZerolAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeCol2D" Unsqueeze: × lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow2D nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PastKVSeqLenpAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow2DPast"Add: × pAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeRow2DPast lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RangeCol2DmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BoolMaskTri"Less: Ā mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BoolMaskTri mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_FloatNegInf lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScalarZeroiAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_MaskTri"Where: Õ jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBias iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_MaskTriuAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasCausalOrNot"Add: ÷ uAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasCausalOrNotkAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasT"Cast* to : Ņ kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeadskAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NGQACond1"Equal: ā kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NGQACond1jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond1"Not: Ņ kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeadsmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_DivNumHeads"Div: ō mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_DivNumHeadsnAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_IDivNumHeads"Cast* to : × kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeadssAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RemainderNumHeads"Mod: Ô sAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_RemainderNumHeads hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Zero1DjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond2"Equal: Ę jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond1 jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond2iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond"And: ž iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_GQACond nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_IDivNumHeads gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_One1DoAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_InterleaveDim"Where: ŠgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Two1D"Constant* value*: : Ķ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentKey gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Two1DmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KUnsqueezed" Unsqueeze: Õ nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_PresentValue gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_Two1DmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VUnsqueezed" Unsqueeze: ° kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BatchSize lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeads oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_InterleaveDim mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLen lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSizenAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KExpandShape"Concat* axis : Ö mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KUnsqueezed nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KExpandShapekAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KExpanded"Expand: ¯ kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BatchSize lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KVNumHeads oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_InterleaveDim mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLen kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VHeadSizenAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VExpandShape"Concat* axis : Ö mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VUnsqueezed nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VExpandShapekAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VExpanded"Expand: Á kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BatchSize kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLen lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKHeadSizeqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KAttentionShape"Concat* axis : Ā kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_BatchSize kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QNumHeads mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_NewKVSeqLen kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VHeadSizeqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VAttentionShape"Concat* axis : Ū kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KExpanded qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KAttentionShapeqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KAttentionInput"Reshape: Ū kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VExpanded qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VAttentionShapeqAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VAttentionInput"Reshape:  qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KAttentionInputlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KTranspose" Transpose* perm@@@@ : Ī kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QReshaped nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorFiAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QScaled"Mul: Đ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KTranspose nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_ScaleFactorFiAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KScaled"Mul: Đ iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QScaled iAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_KScalednAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeight"MatMul: ņ nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnCast"Cast* to : Ú lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnCast kAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnBiasTvAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightWithBias"Add: û vAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightWithBiasuAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightSoftcap"Identity: ų uAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightSoftcapmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SoftmaxCast"Cast* to : ī mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SoftmaxCastsAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnWeightSoftmax"Softmax: ö sAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_AttnWeightSoftmaxlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SoftmaxOut"Cast* to : – vAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_QKAttnWeightWithBiasqk_matmul_output"Identity: Ú lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_SoftmaxOut qAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_VAttentionInputmAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_YPreReshape"MatMul: ~ mAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expanded_function_YPreReshapeY"Identity:Ntest_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal_expandedZ Q     Z K     Z V     Z# attn_mask     Z" past_key     Z$ past_value     b Y     b% present_key     b' present_value     b* qk_matmul_output     B 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…‚?õ?ŅqŒ?dNĒ?]gÖ?lņˆ?€˙€˙test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded/000077500000000000000000000000001511334557700376175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000413061511334557700416270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded  backend-test:Ŧ… Œ QdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_BatchSize"Shape* start * end : œ QbAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KcAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : u QdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QReshaped"Identity: u KdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KReshaped"Identity: u VdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VReshaped"Identity: ī dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QReshapeddAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads"Shape* start * end : đ dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KReshapedeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeads"Shape* start * end : đ dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QReshapedeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSize"Shape* start * end : â eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSizefAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSizeF"Cast* to : ī dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VReshapeddAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VHeadSize"Shape* start * end : Ų fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSizeFgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SqrtHeadSize"Sqrt: ƒ`Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_One1D"Constant* value*: : ‡aAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_One1DF"Constant* value* "€? : „aAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_Zero1D"Constant* value*: : ŋ aAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_One1DF gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SqrtHeadSizejAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_CalculatedScale"Div: …aAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleF"Constant* value*"€? : ā jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_CalculatedScalefAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactor"Identity: Ü fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorjAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorSqrt"Sqrt: č jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorSqrtgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorF"Cast* to : î past_key dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KReshapedeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentKey"Concat* axis : ¨ past_keygAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : € eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentKey present_key"Identity: ō past_value dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VReshapedgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentValue"Concat* axis : „ gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentValue present_value"Identity: „ eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentKeyfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : Í bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QSeqLen fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLenhAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasShape"Concat* axis : ŒfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_FloatNegInf"Constant* value* "€˙ : ‹eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScalarZero"Constant* value* " :  attn_maskhAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasShort"Identity: Û hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasShortcAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBias"Identity: á cAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasnAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasCausalOrNot"Identity: é nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasCausalOrNotdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasT"Cast* to : ŧ dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeadsdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NGQACond1"Equal: Ō dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NGQACond1cAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond1"Not: ŧ dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeadsfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_DivNumHeads"Div: ä fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_DivNumHeadsgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_IDivNumHeads"Cast* to :  dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeadslAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_RemainderNumHeads"Mod: ŋ lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_RemainderNumHeads aAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_Zero1DcAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond2"Equal: ĩ cAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond1 cAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond2bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond"And: ĸ bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_GQACond gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_IDivNumHeads `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_One1DhAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_InterleaveDim"Where: ƒ`Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_Two1D"Constant* value*: : ž eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentKey `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_Two1DfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KUnsqueezed" Unsqueeze: Ā gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_PresentValue `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_Two1DfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VUnsqueezed" Unsqueeze: † dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_BatchSize eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeads hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_InterleaveDim fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLen eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSizegAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KExpandShape"Concat* axis : Á fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KUnsqueezed gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KExpandShapedAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KExpanded"Expand: … dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_BatchSize eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KVNumHeads hAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_InterleaveDim fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLen dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VHeadSizegAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VExpandShape"Concat* axis : Á fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VUnsqueezed gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VExpandShapedAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VExpanded"Expand: ž dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_BatchSize dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLen eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKHeadSizejAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KAttentionShape"Concat* axis :  dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_BatchSize dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QNumHeads fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_NewKVSeqLen dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VHeadSizejAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VAttentionShape"Concat* axis : É dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KExpanded jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KAttentionShapejAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KAttentionInput"Reshape: É dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VExpanded jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VAttentionShapejAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VAttentionInput"Reshape: ķ jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KAttentionInputeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KTranspose" Transpose* perm@@@@ : ē dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QReshaped gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorFbAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QScaled"Mul: ģ eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KTranspose gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_ScaleFactorFbAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KScaled"Mul: ģ bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QScaled bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_KScaledgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeight"MatMul: ã gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeighteAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnCast"Cast* to : Å eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnCast dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnBiasToAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeightWithBias"Add: í oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeightWithBiasnAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeightSoftcap"Identity: ë nAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeightSoftcapfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SoftmaxCast"Cast* to : á fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SoftmaxCastlAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnWeightSoftmax"Softmax: č lAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_AttnWeightSoftmaxeAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SoftmaxOut"Cast* to :  oAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_QKAttnWeightWithBiasqk_matmul_output"Identity: Å eAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_SoftmaxOut jAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_VAttentionInputfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_YPreReshape"MatMul: w fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded_function_YPreReshapeY"Identity:Gtest_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expandedZ Q     Z K     Z V     Z# attn_mask     Z" past_key     Z$ past_value     b Y     b% present_key     b' present_value     b* qk_matmul_output     B test_data_set_0/000077500000000000000000000000001511334557700426615ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expandedinput_0.pb000066400000000000000000000014201511334557700445570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ 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ĄŖ?¸ĪÉ?à R?ėKv?ą˜?¯×Ą?bĻÖ?ļYž?@Pv?ŖR°?ž^¯?Sŗš?tĮ?8-û?*ÍC?Ō!G?î˜?|ۃ?vôā?Šl˜?ė`Œ?D<Ŧ?ĮÉŗ?7k¯?ļĄ?ÕŗŽ?Đ¯ú>ҟ?g,Š?BTj??]E?i&Æ?Â?s{ ?d?`¤?ų[2?gČ]?M°í?dJ?`ž?Uę@č#¤?„Åō?wcŽ?ņšĪ?×G?pÖū?ÖŌØ?ˇˇģ?´]ƒ?,cŠ?ĘŨę?.ėd?á;‡? ‘?A°ã?ėšß?Kž•?ė?™ŗ:?tō@Lĸ?˜Ŋ?Ü‚?yz@"į?J¤Õ?=žú?ĶĪĶ?9rŌ?Ģŋ?˜Qą?û<¨?˜5[?Šų?xŦ›?ĄÃT?7āB?H!š?ãŠ~?B6U?P¤Ĩ?‡Č?#?s‚ŧ?ŲōČ?D{G?ÛĄÄ?ߑŖ?yGŨ?\2’?šq?úæĘ?32€?{ĸ*‰?Šzô> F?kЍ?'ô°?§‚B?=?Šd?ŠŖ?Üxˇ?Ö,?8č\?ūfÕ?’t]?Ŋ˜2?YoĻ?‡7ˆ?vÆˇ?\Â?+h ?…° ?…šŽ?2đ™?Ôå?Ē?<č…?0“?€#Ų?Z+?+š?@nõĩ?YŠ?pļƒ?-Ōu?o‰›?¸š?˙ ­?)íÛ?NFĀ?›HŪ? …‚?õ?ŅqŒ?dNĒ?]gÖ?lņˆ?OÛ*?‹ĶË?test_attention_4d_with_past_and_present_qk_matmul_bias_expanded/000077500000000000000000000000001511334557700362155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000370451511334557700402320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_past_and_present_qk_matmul_bias_expanded  backend-test:Œ| „ Q\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_BatchSize"Shape* start * end : ” QZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : • K[Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : m Q\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QReshaped"Identity: m K\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KReshaped"Identity: m V\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VReshaped"Identity: ß \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QReshaped\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads"Shape* start * end : ā \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KReshaped]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeads"Shape* start * end : ā \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QReshaped]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSize"Shape* start * end : Ō ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSize^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSizeF"Cast* to : ß \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VReshaped\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VHeadSize"Shape* start * end : É ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSizeF_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SqrtHeadSize"Sqrt: {XAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_One1D"Constant* value*: : YAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_One1DF"Constant* value* "€? : |YAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_Zero1D"Constant* value*: : § YAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_One1DF _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SqrtHeadSizebAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_CalculatedScale"Div: }YAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleF"Constant* value*"€? : Đ bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_CalculatedScale^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactor"Identity: Ė ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorbAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorSqrt"Sqrt: Ø bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorSqrt_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorF"Cast* to : Ū past_key \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KReshaped]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentKey"Concat* axis :   past_key_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PastKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : x ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentKey present_key"Identity: â past_value \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VReshaped_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentValue"Concat* axis : | _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentValue present_value"Identity: ô ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentKey^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ĩ ZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QSeqLen ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen`Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasShape"Concat* axis : „^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_FloatNegInf"Constant* value* "€˙ : ƒ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScalarZero"Constant* value* " : y attn_mask`Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasShort"Identity: Ë `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasShort[Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBias"Identity: Ņ [Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasCausalOrNot"Identity: Ų fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasCausalOrNot\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasT"Cast* to : ¤ \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeads\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NGQACond1"Equal:  \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NGQACond1[Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond1"Not: ¤ \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeads^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_DivNumHeads"Div: Ô ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_DivNumHeads_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_IDivNumHeads"Cast* to : Ē \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeadsdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_RemainderNumHeads"Mod: § dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_RemainderNumHeads YAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_Zero1D[Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond2"Equal:  [Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond1 [Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond2ZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond"And: ‚ ZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_GQACond _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_IDivNumHeads XAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_One1D`Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_InterleaveDim"Where: {XAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_Two1D"Constant* value*: : Ļ ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentKey XAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_Two1D^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KUnsqueezed" Unsqueeze: ¨ _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_PresentValue XAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_Two1D^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VUnsqueezed" Unsqueeze: Ö \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_BatchSize ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeads `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_InterleaveDim ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSize_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KExpandShape"Concat* axis : Š ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KUnsqueezed _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KExpandShape\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KExpanded"Expand: Õ \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_BatchSize ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KVNumHeads `Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_InterleaveDim ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VHeadSize_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VExpandShape"Concat* axis : Š ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VUnsqueezed _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VExpandShape\Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VExpanded"Expand: ö \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_BatchSize \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKHeadSizebAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KAttentionShape"Concat* axis : õ \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_BatchSize \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QNumHeads ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_NewKVSeqLen \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VHeadSizebAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VAttentionShape"Concat* axis : ą \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KExpanded bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KAttentionShapebAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KAttentionInput"Reshape: ą \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VExpanded bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VAttentionShapebAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VAttentionInput"Reshape: ã bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KAttentionInput]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KTranspose" Transpose* perm@@@@ : ĸ \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QReshaped _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorFZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QScaled"Mul: Ŗ ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KTranspose _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_ScaleFactorFZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KScaled"Mul: Ŗ ZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QScaled ZAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_KScaled_Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeight"MatMul: Ķ _Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeight]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnCast"Cast* to : ­ ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnCast \Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnBiasTgAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeightWithBias"Add: Ũ gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeightWithBiasfAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeightSoftcap"Identity: Û fAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeightSoftcap^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SoftmaxCast"Cast* to : Ņ ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SoftmaxCastdAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnWeightSoftmax"Softmax: Ø dAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_AttnWeightSoftmax]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SoftmaxOut"Cast* to : ‡ gAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_QKAttnWeightWithBiasqk_matmul_output"Identity: ­ ]Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_SoftmaxOut bAttention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_VAttentionInput^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_YPreReshape"MatMul: o ^Attention_test_attention_4d_with_past_and_present_qk_matmul_bias_expanded_function_YPreReshapeY"Identity:?test_attention_4d_with_past_and_present_qk_matmul_bias_expandedZ Q     Z K     Z V     Z attn_mask   Z" past_key     Z$ past_value     b Y     b% present_key     b' present_value     b* qk_matmul_output     B 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Ë@?˙ÉŊ?(q?wąV?Éîg?¸Eˆ?¨•?gu?ųė‘?Ėŝ?Dž?‹Ļņ>0?#ˇW?t~?Š?>Ē?hÆ>Ŧ§?ŗI?’^Ũ?îĘ?SĻË?ĀŨ—?„x~?|Ã?–ŗ?N#o?V‘?,ށ?ū‡›?¨2Ī?IąK?Ŋ¨?­Ė?­­?Č?,¯ ?pÁ‚?ĶmX?Œ:ˇ?k d?CĢ›?I¸?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_bias_expanded/000077500000000000000000000000001511334557700326635ustar00rootroot00000000000000model.onnx000066400000000000000000000312641511334557700346160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_bias_expanded  backend-test:›e s QKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_BatchSize"Shape* start * end : ƒ QIAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : „ KJAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : \ QKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QReshaped"Identity: \ KKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KReshaped"Identity: \ VKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VReshaped"Identity: Ŋ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QReshapedKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads"Shape* start * end : ž KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KReshapedLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeads"Shape* start * end : ž KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QReshapedLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSize"Shape* start * end : ° LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSizeMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSizeF"Cast* to : Ŋ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VReshapedKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VHeadSize"Shape* start * end : § MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSizeFNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SqrtHeadSize"Sqrt: jGAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_One1D"Constant* value*: : nHAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_One1DF"Constant* value* "€? : kHAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_Zero1D"Constant* value*: : ô HAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_One1DF NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SqrtHeadSizeQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_CalculatedScale"Div: lHAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleF"Constant* value*"€? : Ž QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_CalculatedScaleMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactor"Identity: Ē MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorSqrt"Sqrt: ļ QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorSqrtNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorF"Cast* to : § KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KReshapedLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PresentKey"Identity: qNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PastKVSeqLen"Constant* value*: : Š KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VReshapedNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PresentValue"Identity: Ō LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PresentKeyMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‚ IAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QSeqLen MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLenOAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasShape"Concat* axis : sMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_FloatNegInf"Constant* value* "€˙ : rLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScalarZero"Constant* value* " : h attn_maskOAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasShort"Identity: Š OAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasShortJAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBias"Identity: ¯ JAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasUAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasCausalOrNot"Identity: ˇ UAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasCausalOrNotKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasT"Cast* to : ņ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeadsKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NGQACond1"Equal:   KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NGQACond1JAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond1"Not: ņ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeadsMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_DivNumHeads"Div: ˛ MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_DivNumHeadsNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_IDivNumHeads"Cast* to : ÷ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeadsSAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_RemainderNumHeads"Mod: ô SAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_RemainderNumHeads HAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_Zero1DJAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond2"Equal: ę JAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond1 JAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond2IAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond"And: ž IAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_GQACond NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_IDivNumHeads GAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_One1DOAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_InterleaveDim"Where: jGAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_Two1D"Constant* value*: : ķ LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PresentKey GAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_Two1DMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KUnsqueezed" Unsqueeze: õ NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_PresentValue GAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_Two1DMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VUnsqueezed" Unsqueeze: đ KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_BatchSize LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeads OAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_InterleaveDim MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLen LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSizeNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KExpandShape"Concat* axis : ö MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KUnsqueezed NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KExpandShapeKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KExpanded"Expand: ī KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_BatchSize LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KVNumHeads OAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_InterleaveDim MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLen KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VHeadSizeNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VExpandShape"Concat* axis : ö MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VUnsqueezed NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VExpandShapeKAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VExpanded"Expand: Ą KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_BatchSize KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLen LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKHeadSizeQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KAttentionShape"Concat* axis :   KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_BatchSize KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QNumHeads MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_NewKVSeqLen KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VHeadSizeQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VAttentionShape"Concat* axis : ū KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KExpanded QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KAttentionShapeQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KAttentionInput"Reshape: ū KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VExpanded QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VAttentionShapeQAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VAttentionInput"Reshape: Á QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KAttentionInputLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KTranspose" Transpose* perm@@@@ : ī KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QReshaped NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorFIAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QScaled"Mul: đ LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KTranspose NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_ScaleFactorFIAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KScaled"Mul: đ IAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QScaled IAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_KScaledNAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeight"MatMul: ą NAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnCast"Cast* to : ú LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnCast KAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnBiasTVAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightWithBias"Add: ģ VAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightWithBiasUAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightSoftcap"Identity: š UAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightSoftcapMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SoftmaxCast"Cast* to : ¯ MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SoftmaxCastSAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnWeightSoftmax"Softmax: ļ SAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_AttnWeightSoftmaxLAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SoftmaxOut"Cast* to : v VAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_QKAttnWeightWithBiasqk_matmul_output"Identity: ú LAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_SoftmaxOut QAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_VAttentionInputMAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_YPreReshape"MatMul: ^ MAttention_test_attention_4d_with_qk_matmul_bias_expanded_function_YPreReshapeY"Identity:.test_attention_4d_with_qk_matmul_bias_expandedZ Q     Z K     Z V     Z attn_mask   b Y     b* qk_matmul_output     B test_data_set_0/000077500000000000000000000000001511334557700356465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_bias_expandedinput_0.pb000066400000000000000000000014201511334557700375440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_bias_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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Ë@?˙ÉŊ?(q?wąV?Éîg?¸Eˆ?¨•?gu?ųė‘?Ėŝ?Dž?‹Ļņ>0?#ˇW?t~?Š?>Ē?hÆ>Ŧ§?ŗI?’^Ũ?îĘ?SĻË?ĀŨ—?„x~?|Ã?–ŗ?N#o?V‘?,ށ?ū‡›?¨2Ī?IąK?Ŋ¨?­Ė?­­?Č?,¯ ?pÁ‚?ĶmX?Œ:ˇ?k d?CĢ›?I¸?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_expanded/000077500000000000000000000000001511334557700316655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_expanded/model.onnx000066400000000000000000000275371511334557700337070ustar00rootroot00000000000000  backend-test:Æ^ n QFAttention_test_attention_4d_with_qk_matmul_expanded_function_BatchSize"Shape* start * end : ~ QDAttention_test_attention_4d_with_qk_matmul_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ :  KEAttention_test_attention_4d_with_qk_matmul_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : W QFAttention_test_attention_4d_with_qk_matmul_expanded_function_QReshaped"Identity: W KFAttention_test_attention_4d_with_qk_matmul_expanded_function_KReshaped"Identity: W VFAttention_test_attention_4d_with_qk_matmul_expanded_function_VReshaped"Identity: ŗ FAttention_test_attention_4d_with_qk_matmul_expanded_function_QReshapedFAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads"Shape* start * end : ´ FAttention_test_attention_4d_with_qk_matmul_expanded_function_KReshapedGAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeads"Shape* start * end : ´ FAttention_test_attention_4d_with_qk_matmul_expanded_function_QReshapedGAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSize"Shape* start * end : Ļ GAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSizeHAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSizeF"Cast* to : ŗ FAttention_test_attention_4d_with_qk_matmul_expanded_function_VReshapedFAttention_test_attention_4d_with_qk_matmul_expanded_function_VHeadSize"Shape* start * end :  HAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSizeFIAttention_test_attention_4d_with_qk_matmul_expanded_function_SqrtHeadSize"Sqrt: eBAttention_test_attention_4d_with_qk_matmul_expanded_function_One1D"Constant* value*: : iCAttention_test_attention_4d_with_qk_matmul_expanded_function_One1DF"Constant* value* "€? : fCAttention_test_attention_4d_with_qk_matmul_expanded_function_Zero1D"Constant* value*: : å CAttention_test_attention_4d_with_qk_matmul_expanded_function_One1DF IAttention_test_attention_4d_with_qk_matmul_expanded_function_SqrtHeadSizeLAttention_test_attention_4d_with_qk_matmul_expanded_function_CalculatedScale"Div: gCAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleF"Constant* value*"€? : ¤ LAttention_test_attention_4d_with_qk_matmul_expanded_function_CalculatedScaleHAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactor"Identity:   HAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorLAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorSqrt"Sqrt: Ŧ LAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorSqrtIAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorF"Cast* to :  FAttention_test_attention_4d_with_qk_matmul_expanded_function_KReshapedGAttention_test_attention_4d_with_qk_matmul_expanded_function_PresentKey"Identity: lIAttention_test_attention_4d_with_qk_matmul_expanded_function_PastKVSeqLen"Constant* value*: : Ÿ FAttention_test_attention_4d_with_qk_matmul_expanded_function_VReshapedIAttention_test_attention_4d_with_qk_matmul_expanded_function_PresentValue"Identity: Č GAttention_test_attention_4d_with_qk_matmul_expanded_function_PresentKeyHAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ķ DAttention_test_attention_4d_with_qk_matmul_expanded_function_QSeqLen HAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLenJAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasShape"Concat* axis : nHAttention_test_attention_4d_with_qk_matmul_expanded_function_FloatNegInf"Constant* value* "€˙ : mGAttention_test_attention_4d_with_qk_matmul_expanded_function_ScalarZero"Constant* value* " : Ļ JAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasShapeEAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBias"ConstantOfShape: Ĩ EAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasPAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasCausalOrNot"Identity: ­ PAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasCausalOrNotFAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasT"Cast* to : â FAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads GAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeadsFAttention_test_attention_4d_with_qk_matmul_expanded_function_NGQACond1"Equal: – FAttention_test_attention_4d_with_qk_matmul_expanded_function_NGQACond1EAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond1"Not: â FAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads GAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeadsHAttention_test_attention_4d_with_qk_matmul_expanded_function_DivNumHeads"Div: ¨ HAttention_test_attention_4d_with_qk_matmul_expanded_function_DivNumHeadsIAttention_test_attention_4d_with_qk_matmul_expanded_function_IDivNumHeads"Cast* to : č FAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads GAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeadsNAttention_test_attention_4d_with_qk_matmul_expanded_function_RemainderNumHeads"Mod: å NAttention_test_attention_4d_with_qk_matmul_expanded_function_RemainderNumHeads CAttention_test_attention_4d_with_qk_matmul_expanded_function_Zero1DEAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond2"Equal: Û EAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond1 EAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond2DAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond"And: Ē DAttention_test_attention_4d_with_qk_matmul_expanded_function_GQACond IAttention_test_attention_4d_with_qk_matmul_expanded_function_IDivNumHeads BAttention_test_attention_4d_with_qk_matmul_expanded_function_One1DJAttention_test_attention_4d_with_qk_matmul_expanded_function_InterleaveDim"Where: eBAttention_test_attention_4d_with_qk_matmul_expanded_function_Two1D"Constant* value*: : ä GAttention_test_attention_4d_with_qk_matmul_expanded_function_PresentKey BAttention_test_attention_4d_with_qk_matmul_expanded_function_Two1DHAttention_test_attention_4d_with_qk_matmul_expanded_function_KUnsqueezed" Unsqueeze: æ IAttention_test_attention_4d_with_qk_matmul_expanded_function_PresentValue BAttention_test_attention_4d_with_qk_matmul_expanded_function_Two1DHAttention_test_attention_4d_with_qk_matmul_expanded_function_VUnsqueezed" Unsqueeze: Ō FAttention_test_attention_4d_with_qk_matmul_expanded_function_BatchSize GAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeads JAttention_test_attention_4d_with_qk_matmul_expanded_function_InterleaveDim HAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLen GAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSizeIAttention_test_attention_4d_with_qk_matmul_expanded_function_KExpandShape"Concat* axis : į HAttention_test_attention_4d_with_qk_matmul_expanded_function_KUnsqueezed IAttention_test_attention_4d_with_qk_matmul_expanded_function_KExpandShapeFAttention_test_attention_4d_with_qk_matmul_expanded_function_KExpanded"Expand: Ņ FAttention_test_attention_4d_with_qk_matmul_expanded_function_BatchSize GAttention_test_attention_4d_with_qk_matmul_expanded_function_KVNumHeads JAttention_test_attention_4d_with_qk_matmul_expanded_function_InterleaveDim HAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLen FAttention_test_attention_4d_with_qk_matmul_expanded_function_VHeadSizeIAttention_test_attention_4d_with_qk_matmul_expanded_function_VExpandShape"Concat* axis : į HAttention_test_attention_4d_with_qk_matmul_expanded_function_VUnsqueezed IAttention_test_attention_4d_with_qk_matmul_expanded_function_VExpandShapeFAttention_test_attention_4d_with_qk_matmul_expanded_function_VExpanded"Expand: ˆ FAttention_test_attention_4d_with_qk_matmul_expanded_function_BatchSize FAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads HAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLen GAttention_test_attention_4d_with_qk_matmul_expanded_function_QKHeadSizeLAttention_test_attention_4d_with_qk_matmul_expanded_function_KAttentionShape"Concat* axis : ‡ FAttention_test_attention_4d_with_qk_matmul_expanded_function_BatchSize FAttention_test_attention_4d_with_qk_matmul_expanded_function_QNumHeads HAttention_test_attention_4d_with_qk_matmul_expanded_function_NewKVSeqLen FAttention_test_attention_4d_with_qk_matmul_expanded_function_VHeadSizeLAttention_test_attention_4d_with_qk_matmul_expanded_function_VAttentionShape"Concat* axis : ī FAttention_test_attention_4d_with_qk_matmul_expanded_function_KExpanded LAttention_test_attention_4d_with_qk_matmul_expanded_function_KAttentionShapeLAttention_test_attention_4d_with_qk_matmul_expanded_function_KAttentionInput"Reshape: ī FAttention_test_attention_4d_with_qk_matmul_expanded_function_VExpanded LAttention_test_attention_4d_with_qk_matmul_expanded_function_VAttentionShapeLAttention_test_attention_4d_with_qk_matmul_expanded_function_VAttentionInput"Reshape: ˇ LAttention_test_attention_4d_with_qk_matmul_expanded_function_KAttentionInputGAttention_test_attention_4d_with_qk_matmul_expanded_function_KTranspose" Transpose* perm@@@@ : ā FAttention_test_attention_4d_with_qk_matmul_expanded_function_QReshaped IAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorFDAttention_test_attention_4d_with_qk_matmul_expanded_function_QScaled"Mul: á GAttention_test_attention_4d_with_qk_matmul_expanded_function_KTranspose IAttention_test_attention_4d_with_qk_matmul_expanded_function_ScaleFactorFDAttention_test_attention_4d_with_qk_matmul_expanded_function_KScaled"Mul: á DAttention_test_attention_4d_with_qk_matmul_expanded_function_QScaled DAttention_test_attention_4d_with_qk_matmul_expanded_function_KScaledIAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeight"MatMul: § IAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeightGAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnCast"Cast* to : ë GAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnCast FAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnBiasTQAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeightWithBias"Add: ą QAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeightWithBiasPAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeightSoftcap"Identity: ¯ PAttention_test_attention_4d_with_qk_matmul_expanded_function_QKAttnWeightSoftcapHAttention_test_attention_4d_with_qk_matmul_expanded_function_SoftmaxCast"Cast* to : Ĩ HAttention_test_attention_4d_with_qk_matmul_expanded_function_SoftmaxCastNAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnWeightSoftmax"Softmax: Ŧ NAttention_test_attention_4d_with_qk_matmul_expanded_function_AttnWeightSoftmaxGAttention_test_attention_4d_with_qk_matmul_expanded_function_SoftmaxOut"Cast* to : i 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ĖK?–k?ût4??Wäi?āXa?ž?Š*¤?a?Øzr?‘ “?zS??đܲ?*o¨?2YŠ??ˆ?Pfk?š¨¤?Lčš?‘$_?Ģ€ƒ?/=o?tڊ?]SĢ?â•A?’w“?­›Š?Kŋ–?ãá?\sŽ?Ēęp?´eL?C@?âV?WķŠ?>č?onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softcap_expanded/000077500000000000000000000000001511334557700334045ustar00rootroot00000000000000model.onnx000066400000000000000000000335761511334557700353470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softcap_expanded  backend-test:ån v QNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_BatchSize"Shape* start * end : † QLAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‡ KMAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : _ QNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QReshaped"Identity: _ KNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KReshaped"Identity: _ VNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VReshaped"Identity: à NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QReshapedNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads"Shape* start * end : Ä NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KReshapedOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeads"Shape* start * end : Ä NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QReshapedOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSize"Shape* start * end : ļ OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSizePAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSizeF"Cast* to : à NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VReshapedNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VHeadSize"Shape* start * end : ­ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSizeFQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SqrtHeadSize"Sqrt: mJAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_One1D"Constant* value*: : qKAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_One1DF"Constant* value* "€? : nKAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Zero1D"Constant* value*: : ũ KAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_One1DF QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SqrtHeadSizeTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_CalculatedScale"Div: oKAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleF"Constant* value*"€? : ´ TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_CalculatedScalePAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactor"Identity: ° PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorSqrt"Sqrt: ŧ TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorSqrtQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorF"Cast* to : ­ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KReshapedOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PresentKey"Identity: tQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PastKVSeqLen"Constant* value*: : ¯ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VReshapedQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PresentValue"Identity: Ø OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PresentKeyPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‹ LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QSeqLen PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLenRAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasShape"Concat* axis : vPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_FloatNegInf"Constant* value* "€˙ : uOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScalarZero"Constant* value* " : k attn_maskRAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasShort"Identity: ¯ RAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasShortMAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBias"Identity: ĩ MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasXAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasCausalOrNot"Identity: Ŋ XAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasCausalOrNotNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasT"Cast* to : ú NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeadsNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NGQACond1"Equal: Ļ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NGQACond1MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond1"Not: ú NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeadsPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_DivNumHeads"Div: ¸ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_DivNumHeadsQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_IDivNumHeads"Cast* to : € NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeadsVAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_RemainderNumHeads"Mod: ũ VAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_RemainderNumHeads KAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Zero1DMAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond2"Equal: ķ MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond1 MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond2LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond"And: Ę LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_GQACond QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_IDivNumHeads JAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_One1DRAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_InterleaveDim"Where: mJAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Two1D"Constant* value*: : ü OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PresentKey JAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Two1DPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KUnsqueezed" Unsqueeze: ū QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_PresentValue JAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Two1DPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VUnsqueezed" Unsqueeze: ‚ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_BatchSize OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeads RAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_InterleaveDim PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLen OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSizeQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KExpandShape"Concat* axis : ˙ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KUnsqueezed QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KExpandShapeNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KExpanded"Expand:  NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_BatchSize OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KVNumHeads RAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_InterleaveDim PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VHeadSizeQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VExpandShape"Concat* axis : ˙ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VUnsqueezed QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VExpandShapeNAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VExpanded"Expand: ° NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_BatchSize NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLen OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKHeadSizeTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KAttentionShape"Concat* axis : ¯ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_BatchSize NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QNumHeads PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VHeadSizeTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VAttentionShape"Concat* axis : ‡ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KExpanded TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KAttentionShapeTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KAttentionInput"Reshape: ‡ NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VExpanded TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VAttentionShapeTAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VAttentionInput"Reshape: Į TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KAttentionInputOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KTranspose" Transpose* perm@@@@ : ø NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QReshaped QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorFLAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QScaled"Mul: ų OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KTranspose QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_ScaleFactorFLAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KScaled"Mul: ų LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QScaled LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_KScaledQAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeight"MatMul: ˇ QAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnCast"Cast* to : ƒ OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnCast NAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnBiasTYAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightWithBias"Add: rLAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_Softcap"Constant* value* "@ : ° LAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapMAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapF"Cast* to : ‚ YAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightWithBias MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapFOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapDiv"Div: Ģ OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapDivPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapTanh"Tanh: ‚ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapTanh MAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftcapFXAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcap"Mul: ŋ XAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcapPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftmaxCast"Cast* to : ĩ PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftmaxCastVAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnWeightSoftmax"Softmax: ŧ VAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_AttnWeightSoftmaxOAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftmaxOut"Cast* to : x XAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_QKAttnWeightSoftcapqk_matmul_output"Identity: ƒ OAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_SoftmaxOut TAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_VAttentionInputPAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_YPreReshape"MatMul: a PAttention_test_attention_4d_with_qk_matmul_softcap_expanded_function_YPreReshapeY"Identity:1test_attention_4d_with_qk_matmul_softcap_expandedZ Q     Z K     Z V     Z attn_mask   b Y     b* qk_matmul_output     B test_data_set_0/000077500000000000000000000000001511334557700363675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softcap_expandedinput_0.pb000066400000000000000000000014201511334557700402650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softcap_expanded/test_data_set_0BQJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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ģú>{#?Sę>„šđ>output_1.pb000066400000000000000000000011371511334557700366460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softmax/test_data_set_0Bqk_matmul_outputJĀÚs>Ģj0>Śb>æM>.ā>CwŊ=.K>(Ģ= >đ0>K^>‹0>­5™>€í>hŽ=éí$>ČaÜ=˙üK>ĄZ1>JÚe>H¯ >YQ>ĶŅo>ū1>RS>īÅ1>ÔŅ=‰pX>_Öô=)>Dc>¸c>É{@>žQņ=Đu3>›rk>ĘS^>™J>Ĩ į=sČ'>)†>å0>>!>yĐf>Č­!>=ā>vi}>{ />Ąå>ĸČ=ÕŊÎ=åuy>8>ŧ>ņ >8$>QøA>"bõ=;fO>î‚c>e d>}ĒÔ=#đ>ĩ(>/C>Ė>‘>„B>Ã:>ČRG>—“X>(jF>Ÿ‚>;ú==Ū>Nå>ozá=6a >ŅI>‹9A>t<‚>eųŠ="#>ûm^>Ü9> 'õ=ķq,>4v˙=ĩs>92>Š >l€>)Į>>w%’>é<>¸QE>ČĮÎ=ŅĶØ=Ŗ÷>˛‚>^UÂ=@ĸ>ĨO¸=ĒÅ*>•áx>‚>!j!> Ŧ>…Å'>Cķ=„z÷=LŨ!>Sá>9īu>¨ŋ= Öp>¨>_<^>É?>OA>Ķ>a Õ=Œ5>‘5G>vų= ‹>Æû>P1%>ĒVw>n‘â=Á >>ŸÚ{>ՏE>žšˇ=6d3>Ā1>ļÚö=wČ]> '>^Ō2>Ÿ_>onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softmax_expanded/000077500000000000000000000000001511334557700334265ustar00rootroot00000000000000model.onnx000066400000000000000000000321411511334557700353540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_attention_4d_with_qk_matmul_softmax_expanded  backend-test:Čh v QNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_BatchSize"Shape* start * end : † QLAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‡ KMAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : _ QNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QReshaped"Identity: _ KNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KReshaped"Identity: _ VNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VReshaped"Identity: à NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QReshapedNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads"Shape* start * end : Ä NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KReshapedOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeads"Shape* start * end : Ä NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QReshapedOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSize"Shape* start * end : ļ OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSizePAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSizeF"Cast* to : à NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VReshapedNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VHeadSize"Shape* start * end : ­ PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSizeFQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SqrtHeadSize"Sqrt: mJAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_One1D"Constant* value*: : qKAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_One1DF"Constant* value* "€? : nKAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_Zero1D"Constant* value*: : ũ KAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_One1DF QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SqrtHeadSizeTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_CalculatedScale"Div: oKAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleF"Constant* value*"€? : ´ TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_CalculatedScalePAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactor"Identity: ° PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorSqrt"Sqrt: ŧ TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorSqrtQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorF"Cast* to : ­ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KReshapedOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PresentKey"Identity: tQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PastKVSeqLen"Constant* value*: : ¯ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VReshapedQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PresentValue"Identity: Ø OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PresentKeyPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLen"Shape* startū˙˙˙˙˙˙˙˙ * end˙˙˙˙˙˙˙˙˙ : ‹ LAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QSeqLen PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLenRAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasShape"Concat* axis : vPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_FloatNegInf"Constant* value* "€˙ : uOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScalarZero"Constant* value* " : k attn_maskRAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasShort"Identity: ¯ RAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasShortMAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBias"Identity: ĩ MAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasXAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasCausalOrNot"Identity: Ŋ XAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasCausalOrNotNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasT"Cast* to : ú NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeadsNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NGQACond1"Equal: Ļ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NGQACond1MAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond1"Not: ú NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeadsPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_DivNumHeads"Div: ¸ PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_DivNumHeadsQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_IDivNumHeads"Cast* to : € NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeadsVAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_RemainderNumHeads"Mod: ũ VAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_RemainderNumHeads KAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_Zero1DMAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond2"Equal: ķ MAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond1 MAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond2LAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond"And: Ę LAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_GQACond QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_IDivNumHeads JAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_One1DRAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_InterleaveDim"Where: mJAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_Two1D"Constant* value*: : ü OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PresentKey JAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_Two1DPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KUnsqueezed" Unsqueeze: ū QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_PresentValue JAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_Two1DPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VUnsqueezed" Unsqueeze: ‚ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_BatchSize OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeads RAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_InterleaveDim PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLen OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSizeQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KExpandShape"Concat* axis : ˙ PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KUnsqueezed QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KExpandShapeNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KExpanded"Expand:  NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_BatchSize OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KVNumHeads RAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_InterleaveDim PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VHeadSizeQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VExpandShape"Concat* axis : ˙ PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VUnsqueezed QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VExpandShapeNAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VExpanded"Expand: ° NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_BatchSize NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLen OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKHeadSizeTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KAttentionShape"Concat* axis : ¯ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_BatchSize NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QNumHeads PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_NewKVSeqLen NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VHeadSizeTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VAttentionShape"Concat* axis : ‡ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KExpanded TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KAttentionShapeTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KAttentionInput"Reshape: ‡ NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VExpanded TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VAttentionShapeTAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VAttentionInput"Reshape: Į TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KAttentionInputOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KTranspose" Transpose* perm@@@@ : ø NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QReshaped QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorFLAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QScaled"Mul: ų OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KTranspose QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_ScaleFactorFLAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KScaled"Mul: ų LAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QScaled LAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_KScaledQAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeight"MatMul: ˇ QAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeightOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnCast"Cast* to : ƒ OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnCast NAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnBiasTYAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeightWithBias"Add: Á YAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeightWithBiasXAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeightSoftcap"Identity: ŋ XAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_QKAttnWeightSoftcapPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SoftmaxCast"Cast* to : ĩ PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SoftmaxCastVAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnWeightSoftmax"Softmax: ŧ VAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnWeightSoftmaxOAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SoftmaxOut"Cast* to : v VAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_AttnWeightSoftmaxqk_matmul_output"Identity: ƒ OAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_SoftmaxOut TAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_VAttentionInputPAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_YPreReshape"MatMul: a PAttention_test_attention_4d_with_qk_matmul_softmax_expanded_function_YPreReshapeY"Identity:1test_attention_4d_with_qk_matmul_softmax_expandedZ Q     Z K     Z V     Z attn_mask   b Y     b* qk_matmul_output     B 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™õžuØ?N2?output_2.pb000066400000000000000000000000361511334557700357710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_batchnorm_example_training_mode/test_data_set_0B output_varJ „`v?’/c?Pų >onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli/000077500000000000000000000000001511334557700243075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli/model.onnx000066400000000000000000000001351511334557700263120ustar00rootroot00000000000000  backend-test:E  xy" Bernoullitest_bernoulliZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli/test_data_set_0/000077500000000000000000000000001511334557700273515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli/test_data_set_0/input_0.pb000066400000000000000000000001311511334557700312450ustar00rootroot00000000000000  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?HéØ>QY%?n ā>onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double/test_data_set_0/output_0.pb000066400000000000000000000001311511334557700330000ustar00rootroot00000000000000  ByJPđ?đ?đ?đ?đ?đ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double_expanded/000077500000000000000000000000001511334557700275115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double_expanded/model.onnx000066400000000000000000000006761511334557700315260ustar00rootroot00000000000000  backend-test:Ĩ  x:Bernoulli_test_bernoulli_double_expanded_function_X_random"RandomUniformLike* low * high€? * dtype : ‡ :Bernoulli_test_bernoulli_double_expanded_function_X_random x;Bernoulli_test_bernoulli_double_expanded_function_X_greater"Greater: S ;Bernoulli_test_bernoulli_double_expanded_function_X_greatery"Cast* to  :test_bernoulli_double_expandedZ x   b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double_expanded/test_data_set_0/000077500000000000000000000000001511334557700325535ustar00rootroot00000000000000input_0.pb000066400000000000000000000000611511334557700343720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double_expanded/test_data_set_0 BxJ(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>output_0.pb000066400000000000000000000001311511334557700345710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_double_expanded/test_data_set_0  ByJPđ?đ?đ?đ?đ?đ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_expanded/000077500000000000000000000000001511334557700261575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_expanded/model.onnx000066400000000000000000000006311511334557700301630ustar00rootroot00000000000000  backend-test:€ z x3Bernoulli_test_bernoulli_expanded_function_X_random"RandomUniformLike* low * high€? * dtype  : y 3Bernoulli_test_bernoulli_expanded_function_X_random x4Bernoulli_test_bernoulli_expanded_function_X_greater"Greater: L 4Bernoulli_test_bernoulli_expanded_function_X_greatery"Cast* to  :test_bernoulli_expandedZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_expanded/test_data_set_0/000077500000000000000000000000001511334557700312215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_expanded/test_data_set_0/input_0.pb000066400000000000000000000001311511334557700331150ustar00rootroot00000000000000  BxJP¨_Váá?3‡`ĪÔâæ?K‰nkÖIã?õ•cÛŽoá?öũ)Û?[›‰!*Ģä?ŌYaĘmÜ?÷:ˆg‰ė?63ISÖî?BŌKNŠØ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_expanded/test_data_set_0/output_0.pb000066400000000000000000000001311511334557700333160ustar00rootroot00000000000000  ByJPđ?đ?đ?đ?đ?đ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed/000077500000000000000000000000001511334557700253075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed/model.onnx000066400000000000000000000001621511334557700273120ustar00rootroot00000000000000  backend-test:Z ! xy" Bernoulli* seed test_bernoulli_seedZ x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed/test_data_set_0/000077500000000000000000000000001511334557700303515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed/test_data_set_0/input_0.pb000066400000000000000000000000611511334557700322470ustar00rootroot00000000000000 BxJ(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed/test_data_set_0/output_0.pb000066400000000000000000000000611511334557700324500ustar00rootroot00000000000000 ByJ(€?€?€?€?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed_expanded/000077500000000000000000000000001511334557700271575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed_expanded/model.onnx000066400000000000000000000007041511334557700311640ustar00rootroot00000000000000  backend-test:Ģ  x8Bernoulli_test_bernoulli_seed_expanded_function_X_random"RandomUniformLike* low * high€? * seed * dtype : ƒ 8Bernoulli_test_bernoulli_seed_expanded_function_X_random x9Bernoulli_test_bernoulli_seed_expanded_function_X_greater"Greater: Q 9Bernoulli_test_bernoulli_seed_expanded_function_X_greatery"Cast* to :test_bernoulli_seed_expandedZ x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed_expanded/test_data_set_0/000077500000000000000000000000001511334557700322215ustar00rootroot00000000000000input_0.pb000066400000000000000000000000611511334557700340400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed_expanded/test_data_set_0 BxJ(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>output_0.pb000066400000000000000000000000611511334557700342410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bernoulli_seed_expanded/test_data_set_0 ByJ(€?€?€?€?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/000077500000000000000000000000001511334557700263505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/model.onnx000066400000000000000000000002211511334557700303470ustar00rootroot00000000000000 backend-test:y ) x yz"BitShift* direction"LEFT test_bitshift_left_uint16Z x  Z y  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/test_data_set_0/000077500000000000000000000000001511334557700314125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/test_data_set_0/input_0.pb000066400000000000000000000000171511334557700333110ustar00rootroot00000000000000BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/test_data_set_0/input_1.pb000066400000000000000000000000171511334557700333120ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint16/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700335120ustar00rootroot00000000000000BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/000077500000000000000000000000001511334557700263465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/model.onnx000066400000000000000000000002211511334557700303450ustar00rootroot00000000000000 backend-test:y ) x yz"BitShift* direction"LEFT test_bitshift_left_uint32Z x   Z y   b z   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/test_data_set_0/000077500000000000000000000000001511334557700314105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700333060ustar00rootroot00000000000000 BxJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/test_data_set_0/input_1.pb000066400000000000000000000000251511334557700333070ustar00rootroot00000000000000 ByJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint32/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700335070ustar00rootroot00000000000000 BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/000077500000000000000000000000001511334557700263535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/model.onnx000066400000000000000000000002211511334557700303520ustar00rootroot00000000000000 backend-test:y ) x yz"BitShift* direction"LEFT test_bitshift_left_uint64Z x   Z y   b z   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/test_data_set_0/000077500000000000000000000000001511334557700314155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700333110ustar00rootroot00000000000000 BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700333120ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint64/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700335120ustar00rootroot00000000000000 BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/000077500000000000000000000000001511334557700262715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/model.onnx000066400000000000000000000002201511334557700302670ustar00rootroot00000000000000 backend-test:x ) x yz"BitShift* direction"LEFT test_bitshift_left_uint8Z x  Z y  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/test_data_set_0/000077500000000000000000000000001511334557700313335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/test_data_set_0/input_0.pb000066400000000000000000000000141511334557700332270ustar00rootroot00000000000000BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/test_data_set_0/input_1.pb000066400000000000000000000000141511334557700332300ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_left_uint8/test_data_set_0/output_0.pb000066400000000000000000000000141511334557700334300ustar00rootroot00000000000000BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/000077500000000000000000000000001511334557700265335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/model.onnx000066400000000000000000000002231511334557700305340ustar00rootroot00000000000000 backend-test:{ * x yz"BitShift* direction"RIGHT test_bitshift_right_uint16Z x  Z y  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/test_data_set_0/000077500000000000000000000000001511334557700315755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/test_data_set_0/input_0.pb000066400000000000000000000000171511334557700334740ustar00rootroot00000000000000BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/test_data_set_0/input_1.pb000066400000000000000000000000171511334557700334750ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint16/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700336750ustar00rootroot00000000000000BzJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/000077500000000000000000000000001511334557700265315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/model.onnx000066400000000000000000000002231511334557700305320ustar00rootroot00000000000000 backend-test:{ * x yz"BitShift* direction"RIGHT test_bitshift_right_uint32Z x   Z y   b z   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/test_data_set_0/000077500000000000000000000000001511334557700315735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700334710ustar00rootroot00000000000000 BxJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/test_data_set_0/input_1.pb000066400000000000000000000000251511334557700334720ustar00rootroot00000000000000 ByJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint32/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700336720ustar00rootroot00000000000000 BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/000077500000000000000000000000001511334557700265365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/model.onnx000066400000000000000000000002231511334557700305370ustar00rootroot00000000000000 backend-test:{ * x yz"BitShift* direction"RIGHT test_bitshift_right_uint64Z x   Z y   b z   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/test_data_set_0/000077500000000000000000000000001511334557700316005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700334740ustar00rootroot00000000000000 BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700334750ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint64/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700336750ustar00rootroot00000000000000 BzJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/000077500000000000000000000000001511334557700264545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/model.onnx000066400000000000000000000002221511334557700304540ustar00rootroot00000000000000 backend-test:z * x yz"BitShift* direction"RIGHT test_bitshift_right_uint8Z x  Z y  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/test_data_set_0/000077500000000000000000000000001511334557700315165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/test_data_set_0/input_0.pb000066400000000000000000000000141511334557700334120ustar00rootroot00000000000000BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/test_data_set_0/input_1.pb000066400000000000000000000000141511334557700334130ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitshift_right_uint8/test_data_set_0/output_0.pb000066400000000000000000000000141511334557700336130ustar00rootroot00000000000000BzJonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_and_i16_3d/000077500000000000000000000000001511334557700256515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_and_i16_3d/model.onnx000066400000000000000000000002461511334557700276570ustar00rootroot00000000000000 backend-test:  x y bitwiseand" BitwiseAndtest_bitwise_and_i16_3dZ x    Z y    b bitwiseand    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_and_i16_3d/test_data_set_0/000077500000000000000000000000001511334557700307135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_and_i16_3d/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700326110ustar00rootroot00000000000000BxJx%t댯H”˙ū‰CË*…žOKĀ͐ĐĖ?Gží*üų† ũ˛dUūÔe1’ ÔI‹{ü ę͜_ŽŲ2BDę×ü×ßéËņÂ÷åŪ-`GVéW‰}?=KŽ9%Š€w¸€sИĮˇëC‘øv<3_4Uš2zá>€°4nZ?ŦCo\h~¯yonnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_not_3d/000077500000000000000000000000001511334557700252305ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_not_3d/model.onnx000066400000000000000000000002071511334557700272330ustar00rootroot00000000000000 backend-test:o  x bitwise_not" BitwiseNottest_bitwise_not_3dZ x    b! bitwise_not    B 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_or_i16_4d/test_data_set_0/000077500000000000000000000000001511334557700305725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_bitwise_or_i16_4d/test_data_set_0/input_0.pb000066400000000000000000000005701511334557700324750ustar00rootroot00000000000000BxJčĨk Č KĪ@LĮm|™2”~åT |j˛ÄWWiqw^āÖ i ‡ŋŊ–šŧQˆX ķ/jųHžĮFąšƒD˜qĢ˚´ĐíķŠRĀD™ībWš薉Cgū—ũä%šĶ& "Š—oיGsÜĘ>Ž X7Áņ̓x}M†´UF‚Ė[K‡ÍHËĖĢ”ž¤į‡­Dšp|RāŠ—üŅųJ\ AŪŧiŌķál\įâŠ6āiŌVFBĮg0ļ*”ö0–e qōá5Ԋā7Ŋ¸YgāyS™q TôĢM8;˜{ BGĩÅ$åx•¨Í[1ņ¯M(N­×j­CôÂNŽŋËŖ5]ĄTͰļ }ˇRŸœ^Ęí㠈ÔM˛Ī)ĀėS˜ņj”Ŧ[s“{kÖcĩ/îŽ X7Áņ̓x}M†´UF‚Ė[K‡ÍHËĖĢ”ž¤į‡­Dšp|RāŠ—üŅųJ\ AŪŧiŌķál\įâŠ6āiŌVFBĮg0ļ*”ö0–e qōá5Ԋā7Ŋ¸YgāyS™q TôĢM8;˜{ BGĩÅ$åx•¨Í[1ņ¯M(N­×j­CôÂNŽŋËŖ5]ĄTͰļ }ˇRŸœ^Ęí㠈ÔM˛Ī)ĀėS˜ņj”Ŧ[s“{kÖcĩ/îŽ X7Áņ̓x}M†´UF‚Ė[K‡ÍHËĖĢ”ž¤į‡­Dšp|RāŠ—üŅųJ\ AŪŧiŌķál\įâŠ6āiŌVFBĮg0ļ*”ö0–e qōá5Ԋā7Ŋ¸YgāyS™q TôĢM8;˜{ BGĩÅ$åx•¨Í[1ņ¯M(N­×j­CôÂNŽŋËŖ5]ĄTͰļ }ˇRŸœ^Ęí㠈ÔM˛Ī)ĀėS˜ņj”Ŧ[s“{kÖcĩ/î§ØJëâĐÖņb׎F†ŧŗŗæ}‹ÂDDJH¯lûXBĖ™dPÂ<›SĪSŖ×VT˙›"¸†qonnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow/000077500000000000000000000000001511334557700253145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow/model.onnx000066400000000000000000000001431511334557700273160ustar00rootroot00000000000000 backend-test:K  xy"BlackmanWindowtest_blackmanwindowZ x b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow/test_data_set_0/000077500000000000000000000000001511334557700303565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow/test_data_set_0/input_0.pb000066400000000000000000000000131511334557700322510ustar00rootroot00000000000000BxJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow/test_data_set_0/output_0.pb000066400000000000000000000000611511334557700324550ustar00rootroot00000000000000 ByJ(€Ŗ?ļ$=ą–M>i?!gY?€?!gY?f?­–M>>ļ$=onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_expanded/000077500000000000000000000000001511334557700271645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_expanded/model.onnx000066400000000000000000000102241511334557700311670ustar00rootroot00000000000000 backend-test:û ]7BlackmanWindow_test_blackmanwindow_expanded_function_A0"Constant* value* "= ×>B : ]7BlackmanWindow_test_blackmanwindow_expanded_function_A1"Constant* value* "?B : ]7BlackmanWindow_test_blackmanwindow_expanded_function_A2"Constant* value* " ×Ŗ=B : _9BlackmanWindow_test_blackmanwindow_expanded_function_Zero"Constant* value* "B : ^8BlackmanWindow_test_blackmanwindow_expanded_function_One"Constant* value* "€?B : ^8BlackmanWindow_test_blackmanwindow_expanded_function_Two"Constant* value* "@B : ^8BlackmanWindow_test_blackmanwindow_expanded_function_Tau"Constant* value* "ÛÉ@B : ] 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FBlackmanWindow_test_blackmanwindow_expanded_function_Symmetric_Size_FP CBlackmanWindow_test_blackmanwindow_expanded_function_IsSymmetric_FPHBlackmanWindow_test_blackmanwindow_expanded_function_Symmetric_Component"Mul: Ø GBlackmanWindow_test_blackmanwindow_expanded_function_Periodic_Component HBlackmanWindow_test_blackmanwindow_expanded_function_Symmetric_Componenti?!gY?€?!gY?f?­–M>>ļ$=onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric/000077500000000000000000000000001511334557700274105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric/model.onnx000066400000000000000000000001761511334557700314200ustar00rootroot00000000000000 backend-test:f ' xy"BlackmanWindow* periodic test_blackmanwindow_symmetricZ x b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric/test_data_set_0/000077500000000000000000000000001511334557700324525ustar00rootroot00000000000000input_0.pb000066400000000000000000000000131511334557700342660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric/test_data_set_0BxJ output_0.pb000066400000000000000000000000611511334557700344720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric/test_data_set_0 ByJ(€ŖŽ\P=Ĩ„>ŽG!?@}s??}s?ŽG!? „>Ļ\P=onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric_expanded/000077500000000000000000000000001511334557700312605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric_expanded/model.onnx000066400000000000000000000113161511334557700332660ustar00rootroot00000000000000 backend-test:ĩ% gABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A0"Constant* value* "= ×>B : gABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A1"Constant* value* "?B : gABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A2"Constant* value* " ×Ŗ=B : iCBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Zero"Constant* value* "B : hBBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_One"Constant* value* "€?B : hBBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Two"Constant* value* "@B : hBBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Tau"Constant* value* "ÛÉ@B : g xOBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Size_FP"Cast* to : î OBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Size_FP BBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_OnePBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Symmetric_Size_FP"Sub: iIBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsPeriodic"Constant* value_int : Ŧ IBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsPeriodicLBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsPeriodic_FP"Cast* to : č BBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_One LBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsPeriodic_FPMBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsSymmetric_FP"Sub: ų OBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Size_FP LBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsPeriodic_FPQBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Component"Mul: ü PBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Symmetric_Size_FP MBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_IsSymmetric_FPRBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Symmetric_Component"Mul: ö QBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Component RBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Symmetric_ComponentFBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Size_FP"Add: ä BBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Tau FBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Size_FPOBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_AngularIncrement"Div: Š CBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Zero OBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Periodic_Size_FP BBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_OneDBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Range"Range: ë DBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Range OBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_AngularIncrementKBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_RangeAngular"Mul: č KBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_RangeAngular BBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_TwoNBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_TwoRangeAngular"Mul: Ē NBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_TwoRangeAngularQBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_CosTwoRangeAngular"Cos: ę ABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A2 QBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_CosTwoRangeAngularKBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A2_Component"Mul: ¤ KBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_RangeAngularNBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_CosRangeAngular"Cos: į ABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A1 NBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_CosRangeAngularKBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A1_Component"Mul: Ũ ABlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A0 KBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A1_ComponentDBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Temp0"Sub: ā DBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Temp0 KBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_A2_ComponentDBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Temp1"Add: \ DBlackmanWindow_test_blackmanwindow_symmetric_expanded_function_Temp1y"Cast* to :&test_blackmanwindow_symmetric_expandedZ x b y   B test_data_set_0/000077500000000000000000000000001511334557700342435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric_expandedinput_0.pb000066400000000000000000000000131511334557700361360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric_expanded/test_data_set_0BxJ output_0.pb000066400000000000000000000000611511334557700363420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_blackmanwindow_symmetric_expanded/test_data_set_0 ByJ(€ŖŽ\P=Ĩ„>ŽG!?@}s??}s?ŽG!? „>Ļ\P=onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_BFLOAT16_to_FLOAT/000077500000000000000000000000001511334557700260735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_BFLOAT16_to_FLOAT/model.onnx000066400000000000000000000002121511334557700300720ustar00rootroot00000000000000  backend-test:r inputoutput"Cast* to test_cast_BFLOAT16_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_BFLOAT16_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700311355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_BFLOAT16_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000471511334557700330370ustar00rootroot00000000000000BinputJõ>ö>?R?ņ>Q?X>9?Ā€€€˙output_0.pb000066400000000000000000000001001511334557700331470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_BFLOAT16_to_FLOAT/test_data_set_0BoutputJ0õ>ö>?R?ņ>Q?X>9?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_DOUBLE_to_FLOAT/000077500000000000000000000000001511334557700257275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_DOUBLE_to_FLOAT/model.onnx000066400000000000000000000002101511334557700277240ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to test_cast_DOUBLE_to_FLOATZ input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_DOUBLE_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700307715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_DOUBLE_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000001571511334557700326750ustar00rootroot00000000000000 BinputJ`ˇĻŪ?ÖŊŪ?@Öúß? 6ę?€™Ū?€ ę? 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"į?øđđđ˙output_0.pb000066400000000000000000000000501511334557700331560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_DOUBLE_to_FLOAT16/test_data_set_0 BoutputJĒ7¯7˙7Ž:†7ˆ:ŋ2É9~||üonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_DOUBLE/000077500000000000000000000000001511334557700260765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_DOUBLE/model.onnx000066400000000000000000000002121511334557700300750ustar00rootroot00000000000000  backend-test:r inputoutput"Cast* to  test_cast_FLOAT16_to_DOUBLEZ input    b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_DOUBLE/test_data_set_0/000077500000000000000000000000001511334557700311405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_DOUBLE/test_data_set_0/input_0.pb000066400000000000000000000000471511334557700330420ustar00rootroot00000000000000 BinputJĒ7¯7˙7Ž:†7ˆ:ŋ2É9~||üoutput_0.pb000066400000000000000000000001601511334557700331600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_DOUBLE/test_data_set_0 BoutputJ`¨Ū?ŧŪ?üß?8ę?Ū? ę?üĘ?$į?øđđđ˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT/000077500000000000000000000000001511334557700257715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT/model.onnx000066400000000000000000000002111511334557700277670ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to test_cast_FLOAT16_to_FLOATZ input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700310335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000471511334557700327350ustar00rootroot00000000000000 BinputJĒ7¯7˙7Ž:†7ˆ:ŋ2É9~||üonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT/test_data_set_0/output_0.pb000066400000000000000000000001001511334557700331240ustar00rootroot00000000000000BoutputJ0@õ>āõ>ā˙>ĀQ?Āđ>Q?āW> 9?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT4E2M1/000077500000000000000000000000001511334557700264425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT4E2M1/model.onnx000066400000000000000000000002161511334557700304450ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to test_cast_FLOAT16_to_FLOAT4E2M1Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT4E2M1/test_data_set_0/000077500000000000000000000000001511334557700315045ustar00rootroot00000000000000input_0.pb000066400000000000000000000000551511334557700333260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT4E2M1/test_data_set_0 BinputJŽ743<ÃȀH|~||üÄ!€output_0.pb000066400000000000000000000000301511334557700335200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT4E2M1/test_data_set_0BoutputJâx÷onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FN/000077500000000000000000000000001511334557700266765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FN/model.onnx000066400000000000000000000002201511334557700306740ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to !test_cast_FLOAT16_to_FLOAT8E4M3FNZ input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FN/test_data_set_0/000077500000000000000000000000001511334557700317405ustar00rootroot00000000000000input_0.pb000066400000000000000000000000551511334557700335620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FN/test_data_set_0 BinputJĒ7¯7˙7Ž:†7É9|~||ü€üoutput_0.pb000066400000000000000000000000371511334557700337630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FN/test_data_set_0BoutputJ//05/4~~~ū€ūonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FNUZ/000077500000000000000000000000001511334557700271555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FNUZ/model.onnx000066400000000000000000000002221511334557700311550ustar00rootroot00000000000000  backend-test:z inputoutput"Cast* to #test_cast_FLOAT16_to_FLOAT8E4M3FNUZZ input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FNUZ/test_data_set_0/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000input_0.pb000066400000000000000000000000551511334557700340410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FNUZ/test_data_set_0 BinputJĒ7¯7˙7Ž:†7É9|~||ü€üoutput_0.pb000066400000000000000000000000371511334557700342420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E4M3FNUZ/test_data_set_0BoutputJ778=7<€˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2/000077500000000000000000000000001511334557700264525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2/model.onnx000066400000000000000000000002161511334557700304550ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to test_cast_FLOAT16_to_FLOAT8E5M2Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2/test_data_set_0/000077500000000000000000000000001511334557700315145ustar00rootroot00000000000000input_0.pb000066400000000000000000000000551511334557700333360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2/test_data_set_0 BinputJĒ7¯7˙7Ž:†7É9|~||ü€üoutput_0.pb000066400000000000000000000000371511334557700335370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2/test_data_set_0BoutputJ888;8:{~{{û€ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2FNUZ/000077500000000000000000000000001511334557700271555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2FNUZ/model.onnx000066400000000000000000000002221511334557700311550ustar00rootroot00000000000000  backend-test:z inputoutput"Cast* to #test_cast_FLOAT16_to_FLOAT8E5M2FNUZZ input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2FNUZ/test_data_set_0/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000input_0.pb000066400000000000000000000000551511334557700340410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2FNUZ/test_data_set_0 BinputJĒ7¯7˙7Ž:†7É9|~||ü€üoutput_0.pb000066400000000000000000000000371511334557700342420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_FLOAT8E5M2FNUZ/test_data_set_0BoutputJ<<€˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT2/000077500000000000000000000000001511334557700256405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT2/model.onnx000066400000000000000000000002101511334557700276350ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to test_cast_FLOAT16_to_INT2Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT2/test_data_set_0/000077500000000000000000000000001511334557700307025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT2/test_data_set_0/input_0.pb000066400000000000000000000000351511334557700326010ustar00rootroot00000000000000 BinputJÂĀŧ<@Bonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT2/test_data_set_0/output_0.pb000066400000000000000000000000221511334557700327760ustar00rootroot00000000000000BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT4/000077500000000000000000000000001511334557700256425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT4/model.onnx000066400000000000000000000002101511334557700276370ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to test_cast_FLOAT16_to_INT4Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT4/test_data_set_0/000077500000000000000000000000001511334557700307045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT4/test_data_set_0/input_0.pb000066400000000000000000000001011511334557700325750ustar00rootroot00000000000000 BinputJ2€ČČĮÆÅÄÂĀŧ<@BDEFGH€HI€IJ€JK€Konnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_INT4/test_data_set_0/output_0.pb000066400000000000000000000000351511334557700330040ustar00rootroot00000000000000BoutputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT2/000077500000000000000000000000001511334557700257655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT2/model.onnx000066400000000000000000000002111511334557700277630ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to test_cast_FLOAT16_to_UINT2Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT2/test_data_set_0/000077500000000000000000000000001511334557700310275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT2/test_data_set_0/input_0.pb000066400000000000000000000000351511334557700327260ustar00rootroot00000000000000 BinputJÂĀŧ<@Bonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT2/test_data_set_0/output_0.pb000066400000000000000000000000221511334557700331230ustar00rootroot00000000000000BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT4/000077500000000000000000000000001511334557700257675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT4/model.onnx000066400000000000000000000002111511334557700277650ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to test_cast_FLOAT16_to_UINT4Z input    b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT4/test_data_set_0/000077500000000000000000000000001511334557700310315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT4/test_data_set_0/input_0.pb000066400000000000000000000001011511334557700327220ustar00rootroot00000000000000 BinputJ2€ČČĮÆÅÄÂĀŧ<@BDEFGH€HI€IJ€JK€Konnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT16_to_UINT4/test_data_set_0/output_0.pb000066400000000000000000000000351511334557700331310ustar00rootroot00000000000000BoutputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT/000077500000000000000000000000001511334557700262735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT/model.onnx000066400000000000000000000002141511334557700302740ustar00rootroot00000000000000  backend-test:t inputoutput"Cast* to test_cast_FLOAT4E2M1_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700313355ustar00rootroot00000000000000input_0.pb000066400000000000000000000000271511334557700331560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT/test_data_set_0BinputJâx÷output_0.pb000066400000000000000000000001141511334557700333540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT/test_data_set_0BoutputJ<?€?€ĀĀĀĀ@Ā@€Ā@Ā@Ā€€onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT16/000077500000000000000000000000001511334557700264425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT16/model.onnx000066400000000000000000000002161511334557700304450ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to  test_cast_FLOAT4E2M1_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700315045ustar00rootroot00000000000000input_0.pb000066400000000000000000000000271511334557700333250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT16/test_data_set_0BinputJâx÷output_0.pb000066400000000000000000000000561511334557700335300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT4E2M1_to_FLOAT16/test_data_set_0 BoutputJ8<ÄÆFF€FFÆÄ€onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT/000077500000000000000000000000001511334557700270065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT/model.onnx000066400000000000000000000002201511334557700310040ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to !test_cast_FLOAT8E4M3FNUZ_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700320505ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700336710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT/test_data_set_0BinputJ778=7<€˙˙output_0.pb000066400000000000000000000001141511334557700340670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT/test_data_set_0BoutputJ<đ>đ>?P?đ>@?pCĀ˙pCpCpÃpÃonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT16/000077500000000000000000000000001511334557700271555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT16/model.onnx000066400000000000000000000002221511334557700311550ustar00rootroot00000000000000  backend-test:z inputoutput"Cast* to  #test_cast_FLOAT8E4M3FNUZ_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700340400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT16/test_data_set_0BinputJ778=7<€˙˙output_0.pb000066400000000000000000000000561511334557700342430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FNUZ_to_FLOAT16/test_data_set_0 BoutputJ€7€78€:€7:€[ū€[€[€Û€Ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT/000077500000000000000000000000001511334557700265275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT/model.onnx000066400000000000000000000002161511334557700305320ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to test_cast_FLOAT8E4M3FN_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700315715ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700334120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT/test_data_set_0BinputJ//05/4~~~ū€ūoutput_0.pb000066400000000000000000000001141511334557700336100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT/test_data_set_0BoutputJ<đ>đ>?P?đ>@?āCĀāCāCāÀāÃonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT16/000077500000000000000000000000001511334557700266765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT16/model.onnx000066400000000000000000000002201511334557700306740ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to  !test_cast_FLOAT8E4M3FN_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700317405ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700335610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT16/test_data_set_0BinputJ//05/4~~~ū€ūoutput_0.pb000066400000000000000000000000561511334557700337640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E4M3FN_to_FLOAT16/test_data_set_0 BoutputJ€7€78€:€7:_~__߀ßonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT/000077500000000000000000000000001511334557700270065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT/model.onnx000066400000000000000000000002201511334557700310040ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to !test_cast_FLOAT8E5M2FNUZ_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700320505ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700336710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT/test_data_set_0BinputJ<<€˙˙output_0.pb000066400000000000000000000001141511334557700340670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT/test_data_set_0BoutputJ<???`??@?`GĀ˙`G`G`Į`Įonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT16/000077500000000000000000000000001511334557700271555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT16/model.onnx000066400000000000000000000002221511334557700311550ustar00rootroot00000000000000  backend-test:z inputoutput"Cast* to  #test_cast_FLOAT8E5M2FNUZ_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700340400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT16/test_data_set_0BinputJ<<€˙˙output_0.pb000066400000000000000000000000561511334557700342430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2FNUZ_to_FLOAT16/test_data_set_0 BoutputJ888;8:{ū{{ûûonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT/000077500000000000000000000000001511334557700263035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT/model.onnx000066400000000000000000000002141511334557700303040ustar00rootroot00000000000000  backend-test:t inputoutput"Cast* to test_cast_FLOAT8E5M2_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700313455ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700331660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT/test_data_set_0BinputJ888;8:{~{{û€ûoutput_0.pb000066400000000000000000000001141511334557700333640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT/test_data_set_0BoutputJ<???`??@?`GĀ`G`G`Į€`Įonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT16/000077500000000000000000000000001511334557700264525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT16/model.onnx000066400000000000000000000002161511334557700304550ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to  test_cast_FLOAT8E5M2_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700315145ustar00rootroot00000000000000input_0.pb000066400000000000000000000000361511334557700333350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT16/test_data_set_0BinputJ888;8:{~{{û€ûoutput_0.pb000066400000000000000000000000561511334557700335400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT8E5M2_to_FLOAT16/test_data_set_0 BoutputJ888;8:{~{{û€ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_BFLOAT16/000077500000000000000000000000001511334557700260735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_BFLOAT16/model.onnx000066400000000000000000000002121511334557700300720ustar00rootroot00000000000000  backend-test:r inputoutput"Cast* to test_cast_FLOAT_to_BFLOAT16Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_BFLOAT16/test_data_set_0/000077500000000000000000000000001511334557700311355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_BFLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000771511334557700330420ustar00rootroot00000000000000BinputJ0¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ> Q?ŅîW>99?Ā€€€˙output_0.pb000066400000000000000000000000501511334557700331530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_BFLOAT16/test_data_set_0BoutputJõ>ö>?R?ņ>Q?X>9?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_DOUBLE/000077500000000000000000000000001511334557700257275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_DOUBLE/model.onnx000066400000000000000000000002101511334557700277240ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to  test_cast_FLOAT_to_DOUBLEZ input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_DOUBLE/test_data_set_0/000077500000000000000000000000001511334557700307715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_DOUBLE/test_data_set_0/input_0.pb000066400000000000000000000000771511334557700326760ustar00rootroot00000000000000BinputJ0¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ> Q?ŅîW>99?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_DOUBLE/test_data_set_0/output_0.pb000066400000000000000000000001601511334557700330700ustar00rootroot00000000000000 BoutputJ`ˇĻŪ?ÖŊŪ?@Öúß? 6ę?€™Ū?€ ę? ÚũĘ? "į?øđđđ˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT16/000077500000000000000000000000001511334557700257715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT16/model.onnx000066400000000000000000000002111511334557700277670ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to  test_cast_FLOAT_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700310335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000771511334557700327400ustar00rootroot00000000000000BinputJ0¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ> Q?ŅîW>99?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT16/test_data_set_0/output_0.pb000066400000000000000000000000501511334557700331300ustar00rootroot00000000000000 BoutputJĒ7¯7˙7Ž:†7ˆ:ŋ2É9~||üonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT4E2M1/000077500000000000000000000000001511334557700262735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT4E2M1/model.onnx000066400000000000000000000002141511334557700302740ustar00rootroot00000000000000  backend-test:t inputoutput"Cast* to test_cast_FLOAT_to_FLOAT4E2M1Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT4E2M1/test_data_set_0/000077500000000000000000000000001511334557700313355ustar00rootroot00000000000000input_0.pb000066400000000000000000000001131511334557700331520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT4E2M1/test_data_set_0BinputJ<Âõ>€>ff†?`ĀÁA$tI•ŋÖ3Ā€€€˙€Ā ×#<€output_0.pb000066400000000000000000000000301511334557700333510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT4E2M1/test_data_set_0BoutputJâx÷onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FN/000077500000000000000000000000001511334557700265275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FN/model.onnx000066400000000000000000000002161511334557700305320ustar00rootroot00000000000000  backend-test:v inputoutput"Cast* to test_cast_FLOAT_to_FLOAT8E4M3FNZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FN/test_data_set_0/000077500000000000000000000000001511334557700315715ustar00rootroot00000000000000input_0.pb000066400000000000000000000001131511334557700334060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FN/test_data_set_0BinputJ<¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ>99?$tI•ŋÖ3Ā€€€˙•ŋÖŗ•ŋÖ3$tÉoutput_0.pb000066400000000000000000000000371511334557700336140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FN/test_data_set_0BoutputJ//05/4~~~ū€ūonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FNUZ/000077500000000000000000000000001511334557700270065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FNUZ/model.onnx000066400000000000000000000002201511334557700310040ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to !test_cast_FLOAT_to_FLOAT8E4M3FNUZZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FNUZ/test_data_set_0/000077500000000000000000000000001511334557700320505ustar00rootroot00000000000000input_0.pb000066400000000000000000000001131511334557700336650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FNUZ/test_data_set_0BinputJ<¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ>99?$tI•ŋÖ3Ā€€€˙•ŋÖŗ•ŋÖ3$tÉoutput_0.pb000066400000000000000000000000371511334557700340730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E4M3FNUZ/test_data_set_0BoutputJ778=7<€˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2/000077500000000000000000000000001511334557700263035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2/model.onnx000066400000000000000000000002141511334557700303040ustar00rootroot00000000000000  backend-test:t inputoutput"Cast* to test_cast_FLOAT_to_FLOAT8E5M2Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2/test_data_set_0/000077500000000000000000000000001511334557700313455ustar00rootroot00000000000000input_0.pb000066400000000000000000000001131511334557700331620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2/test_data_set_0BinputJ<¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ>99?$tI•ŋÖ3Ā€€€˙•ŋÖŗ•ŋÖ3$tÉoutput_0.pb000066400000000000000000000000371511334557700333700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2/test_data_set_0BoutputJ888;8:{~{{û€ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2FNUZ/000077500000000000000000000000001511334557700270065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2FNUZ/model.onnx000066400000000000000000000002201511334557700310040ustar00rootroot00000000000000  backend-test:x inputoutput"Cast* to !test_cast_FLOAT_to_FLOAT8E5M2FNUZZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2FNUZ/test_data_set_0/000077500000000000000000000000001511334557700320505ustar00rootroot00000000000000input_0.pb000066400000000000000000000001131511334557700336650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2FNUZ/test_data_set_0BinputJ<¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ>99?$tI•ŋÖ3Ā€€€˙•ŋÖŗ•ŋÖ3$tÉoutput_0.pb000066400000000000000000000000371511334557700340730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_FLOAT8E5M2FNUZ/test_data_set_0BoutputJ<<€˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT2/000077500000000000000000000000001511334557700254715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT2/model.onnx000066400000000000000000000002061511334557700274730ustar00rootroot00000000000000  backend-test:n inputoutput"Cast* to test_cast_FLOAT_to_INT2Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT2/test_data_set_0/000077500000000000000000000000001511334557700305335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT2/test_data_set_0/input_0.pb000066400000000000000000000000531511334557700324320ustar00rootroot00000000000000BinputJ@ĀĀ€ŋ€?@@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT2/test_data_set_0/output_0.pb000066400000000000000000000000221511334557700326270ustar00rootroot00000000000000BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT4/000077500000000000000000000000001511334557700254735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT4/model.onnx000066400000000000000000000002061511334557700274750ustar00rootroot00000000000000  backend-test:n inputoutput"Cast* to test_cast_FLOAT_to_INT4Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT4/test_data_set_0/000077500000000000000000000000001511334557700305355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT4/test_data_set_0/input_0.pb000066400000000000000000000001631511334557700324360ustar00rootroot00000000000000BinputJdÁÁāĀĀ €Ā@ĀĀ€ŋ€?@@@€@ @Ā@ā@AA A0A@APA`ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_INT4/test_data_set_0/output_0.pb000066400000000000000000000000351511334557700326350ustar00rootroot00000000000000BoutputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT2/000077500000000000000000000000001511334557700256165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT2/model.onnx000066400000000000000000000002071511334557700276210ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_FLOAT_to_UINT2Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT2/test_data_set_0/000077500000000000000000000000001511334557700306605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT2/test_data_set_0/input_0.pb000066400000000000000000000000531511334557700325570ustar00rootroot00000000000000BinputJ@ĀĀ€ŋ€?@@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT2/test_data_set_0/output_0.pb000066400000000000000000000000221511334557700327540ustar00rootroot00000000000000BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT4/000077500000000000000000000000001511334557700256205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT4/model.onnx000066400000000000000000000002071511334557700276230ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_FLOAT_to_UINT4Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT4/test_data_set_0/000077500000000000000000000000001511334557700306625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT4/test_data_set_0/input_0.pb000066400000000000000000000001631511334557700325630ustar00rootroot00000000000000BinputJdÁÁāĀĀ €Ā@ĀĀ€ŋ€?@@@€@ @Ā@ā@AA A0A@APA`ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_FLOAT_to_UINT4/test_data_set_0/output_0.pb000066400000000000000000000000351511334557700327620ustar00rootroot00000000000000BoutputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT/000077500000000000000000000000001511334557700254715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT/model.onnx000066400000000000000000000002061511334557700274730ustar00rootroot00000000000000  backend-test:n inputoutput"Cast* to test_cast_INT2_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700305335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700324250ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT/test_data_set_0/output_0.pb000066400000000000000000000000541511334557700326340ustar00rootroot00000000000000BoutputJ€?Ā€ŋ€?Ā€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT16/000077500000000000000000000000001511334557700256405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT16/model.onnx000066400000000000000000000002101511334557700276350ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to  test_cast_INT2_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700307025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700325740ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_FLOAT16/test_data_set_0/output_0.pb000066400000000000000000000000361511334557700330030ustar00rootroot00000000000000 BoutputJ<Āŧ<Āŧonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_INT8/000077500000000000000000000000001511334557700253465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_INT8/model.onnx000066400000000000000000000002051511334557700273470ustar00rootroot00000000000000  backend-test:m inputoutput"Cast* to test_cast_INT2_to_INT8Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_INT8/test_data_set_0/000077500000000000000000000000001511334557700304105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_INT8/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700323020ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT2_to_INT8/test_data_set_0/output_0.pb000066400000000000000000000000271511334557700325110ustar00rootroot00000000000000BoutputJū˙ū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT/000077500000000000000000000000001511334557700254735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT/model.onnx000066400000000000000000000002061511334557700274750ustar00rootroot00000000000000  backend-test:n inputoutput"Cast* to test_cast_INT4_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700305355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700324330ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT/test_data_set_0/output_0.pb000066400000000000000000000001641511334557700326400ustar00rootroot00000000000000BoutputJdā@ÁāĀĀ €Ā@ĀĀ€ŋ€?@@@€@ @Ā@ā@ÁāĀĀ €Ā@ĀĀ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT16/000077500000000000000000000000001511334557700256425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT16/model.onnx000066400000000000000000000002101511334557700276370ustar00rootroot00000000000000  backend-test:p inputoutput"Cast* to  test_cast_INT4_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700307045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700326020ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_FLOAT16/test_data_set_0/output_0.pb000066400000000000000000000001021511334557700327770ustar00rootroot00000000000000 BoutputJ2GČĮÆÅÄÂĀŧ<@BDEFGČĮÆÅÄÂĀŧonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_INT8/000077500000000000000000000000001511334557700253505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_INT8/model.onnx000066400000000000000000000002051511334557700273510ustar00rootroot00000000000000  backend-test:m inputoutput"Cast* to test_cast_INT4_to_INT8Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_INT8/test_data_set_0/000077500000000000000000000000001511334557700304125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_INT8/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700323100ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_INT4_to_INT8/test_data_set_0/output_0.pb000066400000000000000000000000511511334557700325100ustar00rootroot00000000000000BoutputJøųúûüũū˙øųúûüũū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT/000077500000000000000000000000001511334557700256165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT/model.onnx000066400000000000000000000002071511334557700276210ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_UINT2_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700306605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700325520ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT/test_data_set_0/output_0.pb000066400000000000000000000000541511334557700327610ustar00rootroot00000000000000BoutputJ€?@@@€?@@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT16/000077500000000000000000000000001511334557700257655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT16/model.onnx000066400000000000000000000002111511334557700277630ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to  test_cast_UINT2_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700310275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700327210ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_FLOAT16/test_data_set_0/output_0.pb000066400000000000000000000000361511334557700331300ustar00rootroot00000000000000 BoutputJ<@B<@Bonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_UINT8/000077500000000000000000000000001511334557700256205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_UINT8/model.onnx000066400000000000000000000002071511334557700276230ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_UINT2_to_UINT8Z input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_UINT8/test_data_set_0/000077500000000000000000000000001511334557700306625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_UINT8/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700325540ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT2_to_UINT8/test_data_set_0/output_0.pb000066400000000000000000000000271511334557700327630ustar00rootroot00000000000000BoutputJonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT/000077500000000000000000000000001511334557700256205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT/model.onnx000066400000000000000000000002071511334557700276230ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_UINT4_to_FLOATZ input   b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700306625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700325600ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT/test_data_set_0/output_0.pb000066400000000000000000000001641511334557700327650ustar00rootroot00000000000000BoutputJdā@AA A0A@APA`ApA€?@@@€@ @Ā@ā@AA A0A@APA`ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT16/000077500000000000000000000000001511334557700257675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT16/model.onnx000066400000000000000000000002111511334557700277650ustar00rootroot00000000000000  backend-test:q inputoutput"Cast* to  test_cast_UINT4_to_FLOAT16Z input   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700310315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT16/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700327270ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_FLOAT16/test_data_set_0/output_0.pb000066400000000000000000000001021511334557700331240ustar00rootroot00000000000000 BoutputJ2GH€HI€IJ€JK€K<@BDEFGH€HI€IJ€JK€Konnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_UINT8/000077500000000000000000000000001511334557700256225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_UINT4_to_UINT8/model.onnx000066400000000000000000000002071511334557700276250ustar00rootroot00000000000000  backend-test:o inputoutput"Cast* to test_cast_UINT4_to_UINT8Z input   b output   B 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test_data_set_0/000077500000000000000000000000001511334557700336725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_no_saturate_FLOAT_to_FLOAT8E5M2input_0.pb000066400000000000000000000001131511334557700355660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_no_saturate_FLOAT_to_FLOAT8E5M2/test_data_set_0BinputJ<¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ>99?$tI•ŋÖ3Ā€€€˙•ŋÖŗ•ŋÖ3$tÉoutput_0.pb000066400000000000000000000000371511334557700357740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_no_saturate_FLOAT_to_FLOAT8E5M2/test_data_set_0BoutputJ888;8:|~||ü€üonnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_no_saturate_FLOAT_to_FLOAT8E5M2FNUZ/000077500000000000000000000000001511334557700314125ustar00rootroot00000000000000model.onnx000066400000000000000000000002561511334557700333420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_cast_no_saturate_FLOAT_to_FLOAT8E5M2FNUZ  backend-test:• 1 inputoutput"Cast* saturate * to -test_cast_no_saturate_FLOAT_to_FLOAT8E5M2FNUZZ input   b output   B 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BinputJÂĀŧ<@Binput_1.pb000066400000000000000000000000121511334557700352330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_INT2_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000221511334557700354340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_INT2_expanded/test_data_set_0BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_INT4/000077500000000000000000000000001511334557700265075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_INT4/model.onnx000066400000000000000000000002401511334557700305070ustar00rootroot00000000000000  backend-test:‡  input likeoutput"CastLiketest_castlike_FLOAT16_to_INT4Z input    Z like  b output   B 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_UINT4/test_data_set_0/000077500000000000000000000000001511334557700316765ustar00rootroot00000000000000input_0.pb000066400000000000000000000001011511334557700335100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_UINT4/test_data_set_0 BinputJ2€ČČĮÆÅÄÂĀŧ<@BDEFGH€HI€IJ€JK€Kinput_1.pb000066400000000000000000000000121511334557700335120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_UINT4/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000351511334557700337170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT16_to_UINT4/test_data_set_0BoutputJ 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BoutputJ888;8:{~{{û€ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expanded/000077500000000000000000000000001511334557700311675ustar00rootroot00000000000000model.onnx000066400000000000000000000003031511334557700331100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expanded  backend-test:Ē 3 inputoutput"Cast* to  * saturate :,test_castlike_FLOAT8E5M2_to_FLOAT16_expandedZ input   Z like   b output    B test_data_set_0/000077500000000000000000000000001511334557700341525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expandedinput_0.pb000066400000000000000000000000361511334557700360520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expanded/test_data_set_0BinputJ888;8:{~{{û€ûinput_1.pb000066400000000000000000000000121511334557700360450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expanded/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000000561511334557700362550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT16_expanded/test_data_set_0 BoutputJ888;8:{~{{û€ûonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expanded/000077500000000000000000000000001511334557700310205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expanded/model.onnx000066400000000000000000000003011511334557700330160ustar00rootroot00000000000000  backend-test:¨ 3 inputoutput"Cast* to * saturate :*test_castlike_FLOAT8E5M2_to_FLOAT_expandedZ input   Z like  b output   B test_data_set_0/000077500000000000000000000000001511334557700340035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expandedinput_0.pb000066400000000000000000000000361511334557700357030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expanded/test_data_set_0BinputJ888;8:{~{{û€ûinput_1.pb000066400000000000000000000000121511334557700356760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000001141511334557700361010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT8E5M2_to_FLOAT_expanded/test_data_set_0BoutputJ<???`??@?`GĀ`G`G`Į€`Įonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16/000077500000000000000000000000001511334557700267405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16/model.onnx000066400000000000000000000002421511334557700307420ustar00rootroot00000000000000 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Q?ŅîW>99?Ā€€€˙input_1.pb000066400000000000000000000000121511334557700336160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000501511334557700340200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16/test_data_set_0BoutputJõ>ö>?R?ņ>Q?X>9?Ā€€€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16_expanded/000077500000000000000000000000001511334557700306105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_BFLOAT16_expanded/model.onnx000066400000000000000000000002771511334557700326220ustar00rootroot00000000000000  backend-test:Ļ 3 inputoutput"Cast* to * saturate :(test_castlike_FLOAT_to_BFLOAT16_expandedZ input   Z like  b output   B 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_DOUBLE/test_data_set_0/000077500000000000000000000000001511334557700316365ustar00rootroot00000000000000input_0.pb000066400000000000000000000000771511334557700334640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_DOUBLE/test_data_set_0BinputJ0¸5õ>°îõ>˛Ö˙>å°Q?ĖĖđ> Q?ŅîW>99?Ā€€€˙input_1.pb000066400000000000000000000000121511334557700334520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_DOUBLE/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000001601511334557700336560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_DOUBLE/test_data_set_0 BoutputJ`ˇĻŪ?ÖŊŪ?@Öúß? 6ę?€™Ū?€ ę? 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT2_expanded/test_data_set_0/000077500000000000000000000000001511334557700332505ustar00rootroot00000000000000input_0.pb000066400000000000000000000000531511334557700350700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT2_expanded/test_data_set_0BinputJ@ĀĀ€ŋ€?@@@input_1.pb000066400000000000000000000000121511334557700350640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT2_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000221511334557700352650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT2_expanded/test_data_set_0BoutputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT4/000077500000000000000000000000001511334557700263405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_FLOAT_to_INT4/model.onnx000066400000000000000000000002361511334557700303450ustar00rootroot00000000000000 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_FLOAT_expanded/test_data_set_0/000077500000000000000000000000001511334557700332505ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_FLOAT_expanded/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700350640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_FLOAT_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000541511334557700352720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_FLOAT_expanded/test_data_set_0BoutputJ€?Ā€ŋ€?Ā€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/000077500000000000000000000000001511334557700262135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/model.onnx000066400000000000000000000002351511334557700302170ustar00rootroot00000000000000  backend-test:„  input likeoutput"CastLiketest_castlike_INT2_to_INT8Z input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/test_data_set_0/000077500000000000000000000000001511334557700312555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700331470ustar00rootroot00000000000000BinputJ99onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/test_data_set_0/input_1.pb000066400000000000000000000000121511334557700331500ustar00rootroot00000000000000Blikeonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8/test_data_set_0/output_0.pb000066400000000000000000000000271511334557700333560ustar00rootroot00000000000000BoutputJū˙ū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/000077500000000000000000000000001511334557700300635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/model.onnx000066400000000000000000000002721511334557700320700ustar00rootroot00000000000000  backend-test:Ą 3 inputoutput"Cast* to * saturate :#test_castlike_INT2_to_INT8_expandedZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/test_data_set_0/000077500000000000000000000000001511334557700331255ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700347400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700347410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000271511334557700351470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT2_to_INT8_expanded/test_data_set_0BoutputJū˙ū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/000077500000000000000000000000001511334557700263405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/model.onnx000066400000000000000000000002361511334557700303450ustar00rootroot00000000000000  backend-test:…  input likeoutput"CastLiketest_castlike_INT4_to_FLOATZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700314025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700333000ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/test_data_set_0/input_1.pb000066400000000000000000000000121511334557700332750ustar00rootroot00000000000000Blikeoutput_0.pb000066400000000000000000000001641511334557700334260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT/test_data_set_0BoutputJdā@ÁāĀĀ €Ā@ĀĀ€ŋ€?@@@€@ @Ā@ā@ÁāĀĀ €Ā@ĀĀ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/000077500000000000000000000000001511334557700265075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/model.onnx000066400000000000000000000002401511334557700305070ustar00rootroot00000000000000  backend-test:‡  input likeoutput"CastLiketest_castlike_INT4_to_FLOAT16Z input   Z like   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700315515ustar00rootroot00000000000000input_0.pb000066400000000000000000000000341511334557700333700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700333650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000001021511334557700335650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16/test_data_set_0 BoutputJ2GČĮÆÅÄÂĀŧ<@BDEFGČĮÆÅÄÂĀŧonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expanded/000077500000000000000000000000001511334557700303575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expanded/model.onnx000066400000000000000000000002751511334557700323670ustar00rootroot00000000000000  backend-test:¤ 3 inputoutput"Cast* to  * saturate :&test_castlike_INT4_to_FLOAT16_expandedZ input   Z like   b output    B test_data_set_0/000077500000000000000000000000001511334557700333425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expandedinput_0.pb000066400000000000000000000000341511334557700352400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expanded/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700352350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expanded/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000001021511334557700354350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT16_expanded/test_data_set_0 BoutputJ2GČĮÆÅÄÂĀŧ<@BDEFGČĮÆÅÄÂĀŧonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/000077500000000000000000000000001511334557700302105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/model.onnx000066400000000000000000000002731511334557700322160ustar00rootroot00000000000000  backend-test:ĸ 3 inputoutput"Cast* to * saturate :$test_castlike_INT4_to_FLOAT_expandedZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/test_data_set_0/000077500000000000000000000000001511334557700332525ustar00rootroot00000000000000input_0.pb000066400000000000000000000000341511334557700350710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700350660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000001641511334557700352760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_FLOAT_expanded/test_data_set_0BoutputJdā@ÁāĀĀ €Ā@ĀĀ€ŋ€?@@@€@ @Ā@ā@ÁāĀĀ €Ā@ĀĀ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/000077500000000000000000000000001511334557700262155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/model.onnx000066400000000000000000000002351511334557700302210ustar00rootroot00000000000000  backend-test:„  input likeoutput"CastLiketest_castlike_INT4_to_INT8Z input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/test_data_set_0/000077500000000000000000000000001511334557700312575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/test_data_set_0/input_0.pb000066400000000000000000000000341511334557700331550ustar00rootroot00000000000000BinputJ ‡ŠËí!Ce‡ŠËíonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/test_data_set_0/input_1.pb000066400000000000000000000000121511334557700331520ustar00rootroot00000000000000Blikeonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8/test_data_set_0/output_0.pb000066400000000000000000000000511511334557700333550ustar00rootroot00000000000000BoutputJøųúûüũū˙øųúûüũū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/000077500000000000000000000000001511334557700300655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/model.onnx000066400000000000000000000002721511334557700320720ustar00rootroot00000000000000  backend-test:Ą 3 inputoutput"Cast* to * saturate :#test_castlike_INT4_to_INT8_expandedZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/test_data_set_0/000077500000000000000000000000001511334557700331275ustar00rootroot00000000000000input_0.pb000066400000000000000000000000341511334557700347460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700347430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000511511334557700351460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_INT4_to_INT8_expanded/test_data_set_0BoutputJøųúûüũū˙øųúûüũū˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/000077500000000000000000000000001511334557700264635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/model.onnx000066400000000000000000000002371511334557700304710ustar00rootroot00000000000000  backend-test:†  input likeoutput"CastLiketest_castlike_UINT2_to_FLOATZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/test_data_set_0/000077500000000000000000000000001511334557700315255ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700333400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700333410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000541511334557700335470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT/test_data_set_0BoutputJ€?@@@€?@@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/000077500000000000000000000000001511334557700266325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/model.onnx000066400000000000000000000002411511334557700306330ustar00rootroot00000000000000  backend-test:ˆ  input likeoutput"CastLiketest_castlike_UINT2_to_FLOAT16Z input   Z like   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700316745ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700335070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700335100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000000361511334557700337160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16/test_data_set_0 BoutputJ<@B<@Bonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expanded/000077500000000000000000000000001511334557700305025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expanded/model.onnx000066400000000000000000000002761511334557700325130ustar00rootroot00000000000000  backend-test:Ĩ 3 inputoutput"Cast* to  * saturate :'test_castlike_UINT2_to_FLOAT16_expandedZ input   Z like   b output    B test_data_set_0/000077500000000000000000000000001511334557700334655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expandedinput_0.pb000066400000000000000000000000211511334557700353570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expanded/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700353600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expanded/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000000361511334557700355660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT16_expanded/test_data_set_0 BoutputJ<@B<@Bonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/000077500000000000000000000000001511334557700303335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/model.onnx000066400000000000000000000002741511334557700323420ustar00rootroot00000000000000  backend-test:Ŗ 3 inputoutput"Cast* to * saturate :%test_castlike_UINT2_to_FLOAT_expandedZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/test_data_set_0/000077500000000000000000000000001511334557700333755ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700352100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700352110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000541511334557700354170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_FLOAT_expanded/test_data_set_0BoutputJ€?@@@€?@@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/000077500000000000000000000000001511334557700264655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/model.onnx000066400000000000000000000002371511334557700304730ustar00rootroot00000000000000 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/test_data_set_0/000077500000000000000000000000001511334557700315275ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700333420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700333430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000271511334557700335510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8/test_data_set_0BoutputJonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/000077500000000000000000000000001511334557700303355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/model.onnx000066400000000000000000000002741511334557700323440ustar00rootroot00000000000000 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/test_data_set_0/000077500000000000000000000000001511334557700333775ustar00rootroot00000000000000input_0.pb000066400000000000000000000000211511334557700352120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/test_data_set_0BinputJ99input_1.pb000066400000000000000000000000121511334557700352130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/test_data_set_0Blikeoutput_0.pb000066400000000000000000000000271511334557700354210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT2_to_UINT8_expanded/test_data_set_0BoutputJonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT/000077500000000000000000000000001511334557700264655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT/model.onnx000066400000000000000000000002371511334557700304730ustar00rootroot00000000000000 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A0A@APA`ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/000077500000000000000000000000001511334557700266345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/model.onnx000066400000000000000000000002411511334557700306350ustar00rootroot00000000000000  backend-test:ˆ  input likeoutput"CastLiketest_castlike_UINT4_to_FLOAT16Z input   Z like   b output    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/test_data_set_0/000077500000000000000000000000001511334557700316765ustar00rootroot00000000000000input_0.pb000066400000000000000000000000341511334557700335150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700335120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000001021511334557700337120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16/test_data_set_0 BoutputJ2GH€HI€IJ€JK€K<@BDEFGH€HI€IJ€JK€Konnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expanded/000077500000000000000000000000001511334557700305045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expanded/model.onnx000066400000000000000000000002761511334557700325150ustar00rootroot00000000000000  backend-test:Ĩ 3 inputoutput"Cast* to  * saturate :'test_castlike_UINT4_to_FLOAT16_expandedZ input   Z like   b output    B test_data_set_0/000077500000000000000000000000001511334557700334675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expandedinput_0.pb000066400000000000000000000000341511334557700353650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expanded/test_data_set_0BinputJ ‡ŠËí!Ce‡ŠËíinput_1.pb000066400000000000000000000000121511334557700353620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expanded/test_data_set_0 Blikeoutput_0.pb000066400000000000000000000001021511334557700355620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT16_expanded/test_data_set_0 BoutputJ2GH€HI€IJ€JK€K<@BDEFGH€HI€IJ€JK€Konnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT_expanded/000077500000000000000000000000001511334557700303355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT_expanded/model.onnx000066400000000000000000000002741511334557700323440ustar00rootroot00000000000000  backend-test:Ŗ 3 inputoutput"Cast* to * saturate :%test_castlike_UINT4_to_FLOAT_expandedZ input   Z like  b output   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT_expanded/test_data_set_0/000077500000000000000000000000001511334557700333775ustar00rootroot00000000000000input_0.pb000066400000000000000000000000341511334557700352160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_castlike_UINT4_to_FLOAT_expanded/test_data_set_0BinputJ 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¸ŋû¨Ž?ž0ŋˇü&ŋĒlŋ´éëŋšôžr•õžÍĪ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_and_pad_expanded/000077500000000000000000000000001511334557700320045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_and_pad_expanded/model.onnx000066400000000000000000000051421511334557700340120ustar00rootroot00000000000000 backend-test:É gDCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_k2"Constant* value*: : W xICenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_x_shape"Shape: Ļ ICenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_x_shape shapeKCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padded_sh"Max: í KCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padded_sh ICenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_x_shapeLCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount"Sub: î LCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount DCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_k2QCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount_left"Div: ü LCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount QCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount_leftRCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount_right"Sub: † QCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount_left RCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pad_amount_rightFCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_pads"Concat* axis : ĸ x FCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padsNCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padded_input"Pad: Ĩ NCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padded_inputJCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_x_shape2"Shape: Ĩ JCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_x_shape2 shapeICenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_sh_diff"Sub: æ ICenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_sh_diff DCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_k2LCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_start_dims"Div: ¨ LCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_start_dims shapeJCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_end_dims"Add: ö NCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_padded_input LCenterCropPad_test_center_crop_pad_crop_and_pad_expanded_function_start_dims 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Āęaũ=Â:žjĀ=xkq?ßV/Āožŋį0Š>aīž.]ĩŋdt^?(>Ošxŋ¸/Ą>qSR?īm­;ŅņL?ßF =onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_axes_chw_expanded/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_axes_chw_expanded/model.onnx000066400000000000000000000066351511334557700342350ustar00rootroot00000000000000 backend-test:„ hECenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_k2"Constant* value*: : pMCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_axes_input"Constant* value_ints@@ : ` xRCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape_alldims"Shape: ų RCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape_alldims MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_axes_inputJCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape"Gather: ¨ JCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape shapeLCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_padded_sh"Max: đ LCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_padded_sh JCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shapeMCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount"Sub: ņ MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount ECenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_k2RCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount_left"Div: ˙ MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount RCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount_leftSCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount_right"Sub: ‰ RCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount_left SCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pad_amount_rightGCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pads"Concat* axis : õ x GCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_pads MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_axes_inputOCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_padded_input"Pad: ¯ OCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_padded_inputSCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape_alldims2"Shape: û SCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape_alldims2 MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_axes_inputKCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape2"Gather: § KCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_x_shape2 shapeJCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_sh_diff"Sub: é JCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_sh_diff ECenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_k2MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_start_dims"Div: Ē MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_start_dims shapeKCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_end_dims"Add: Č OCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_padded_input MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_start_dims KCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_end_dims MCenterCropPad_test_center_crop_pad_crop_axes_chw_expanded_function_axes_inputy"Slice:+test_center_crop_pad_crop_axes_chw_expandedZ x    Z shape  b y     B 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¸ŋû¨Ž?ž0ŋˇü&ŋĒlŋ´éëŋšôžr•õžÍĪ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_axes_hwc_expanded/000077500000000000000000000000001511334557700322175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_axes_hwc_expanded/model.onnx000066400000000000000000000066351511334557700342350ustar00rootroot00000000000000 backend-test:„ hECenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_k2"Constant* value*: : pMCenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_axes_input"Constant* value_ints@@ : ` xRCenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_x_shape_alldims"Shape: ų RCenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_x_shape_alldims MCenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_axes_inputJCenterCropPad_test_center_crop_pad_crop_axes_hwc_expanded_function_x_shape"Gather: ¨ 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¸ŋû¨Ž?ž0ŋˇü&ŋĒlŋ´éëŋšôžr•õžÍĪ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_negative_axes_hwc_expanded/000077500000000000000000000000001511334557700341015ustar00rootroot00000000000000model.onnx000066400000000000000000000074301511334557700360320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_crop_negative_axes_hwc_expanded backend-test:˙ qNCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_k2"Constant* value*: : ‹VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_axes_input"Constant*% value_ints@ũ˙˙˙˙˙˙˙˙@ū˙˙˙˙˙˙˙˙ : i x[CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape_alldims"Shape: ” [CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape_alldims VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_axes_inputSCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape"Gather: ē SCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape shapeUCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_padded_sh"Max: ‹ UCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_padded_sh SCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shapeVCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount"Sub: Œ VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount NCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_k2[CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount_left"Div: š VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount [CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount_left\CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount_right"Sub: ¤ [CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount_left \CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pad_amount_rightPCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pads"Concat* axis :  x PCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_pads VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_axes_inputXCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_padded_input"Pad: Á XCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_padded_input\CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape_alldims2"Shape: – \CenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape_alldims2 VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_axes_inputTCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape2"Gather: š TCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_x_shape2 shapeSCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_sh_diff"Sub: „ SCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_sh_diff NCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_k2VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_start_dims"Div: ŧ VCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_start_dims shapeTCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_end_dims"Add: ė XCenterCropPad_test_center_crop_pad_crop_negative_axes_hwc_expanded_function_padded_input 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FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?ŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžA‹ŊĐNÛ?A¨>ŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> s?Nŗ=ۜŋ,(X?€ŋģÅŋ\˜?MFĸ>gŊk?E0Ŗ>@Y[?Š&ŋb„ŋú|.?AŦMŋV†0ŋŽ;éžt0<ą>ĩžgū¯ŋ-Ä$ŋ=LĀ+ ?:Íŋo\ŋ ĢU=T=ŋÅ?V|Ĩŋäēˆ>į Ŋ„•ŋvõ?׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊonnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/000077500000000000000000000000001511334557700301375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/model.onnx000066400000000000000000000045131511334557700321460ustar00rootroot00000000000000 backend-test:˛ ^;CenterCropPad_test_center_crop_pad_pad_expanded_function_k2"Constant* value*: : N x@CenterCropPad_test_center_crop_pad_pad_expanded_function_x_shape"Shape: ” @CenterCropPad_test_center_crop_pad_pad_expanded_function_x_shape shapeBCenterCropPad_test_center_crop_pad_pad_expanded_function_padded_sh"Max: Ō BCenterCropPad_test_center_crop_pad_pad_expanded_function_padded_sh @CenterCropPad_test_center_crop_pad_pad_expanded_function_x_shapeCCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount"Sub: Ķ CCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount ;CenterCropPad_test_center_crop_pad_pad_expanded_function_k2HCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount_left"Div: á CCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount HCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount_leftICenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount_right"Sub: ë HCenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount_left ICenterCropPad_test_center_crop_pad_pad_expanded_function_pad_amount_right=CenterCropPad_test_center_crop_pad_pad_expanded_function_pads"Concat* axis :  x =CenterCropPad_test_center_crop_pad_pad_expanded_function_padsECenterCropPad_test_center_crop_pad_pad_expanded_function_padded_input"Pad: “ ECenterCropPad_test_center_crop_pad_pad_expanded_function_padded_inputACenterCropPad_test_center_crop_pad_pad_expanded_function_x_shape2"Shape: “ ACenterCropPad_test_center_crop_pad_pad_expanded_function_x_shape2 shape@CenterCropPad_test_center_crop_pad_pad_expanded_function_sh_diff"Sub: Ë @CenterCropPad_test_center_crop_pad_pad_expanded_function_sh_diff ;CenterCropPad_test_center_crop_pad_pad_expanded_function_k2CCenterCropPad_test_center_crop_pad_pad_expanded_function_start_dims"Div: – CCenterCropPad_test_center_crop_pad_pad_expanded_function_start_dims shapeACenterCropPad_test_center_crop_pad_pad_expanded_function_end_dims"Add: Û ECenterCropPad_test_center_crop_pad_pad_expanded_function_padded_input CCenterCropPad_test_center_crop_pad_pad_expanded_function_start_dims ACenterCropPad_test_center_crop_pad_pad_expanded_function_end_dimsy"Slice:!test_center_crop_pad_pad_expandedZ x    Z shape  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/test_data_set_0/000077500000000000000000000000001511334557700332015ustar00rootroot00000000000000input_0.pb000066400000000000000000000015261511334557700350270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/test_data_set_0 BxJČxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?ŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžA‹ŊĐNÛ?A¨>ŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> s?Nŗ=ۜŋ,(X?€ŋģÅŋ\˜?MFĸ>gŊk?E0Ŗ>@Y[?Š&ŋb„ŋú|.?AŦMŋV†0ŋŽ;éžt0<ą>ĩžgū¯ŋ-Ä$ŋ=LĀ+ ?:Íŋo\ŋ ĢU=T=ŋÅ?V|Ĩŋäēˆ>į Ŋ„•ŋvõ?׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊinput_1.pb000066400000000000000000000000451511334557700350230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/test_data_set_0BshapeJ output_0.pb000066400000000000000000000045561511334557700352360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_center_crop_pad_pad_expanded/test_data_set_0 ByJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?ŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžA‹ŊĐNÛ?A¨>ŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> s?Nŗ=ۜŋ,(X?€ŋģÅŋ\˜?MFĸ>gŊk?E0Ŗ>@Y[?Š&ŋb„ŋú|.?AŦMŋV†0ŋŽ;éžt0<ą>ĩžgū¯ŋ-Ä$ŋ=LĀ+ ?:Íŋo\ŋ ĢU=T=ŋÅ?V|Ĩŋäēˆ>į Ŋ„•ŋvõ?׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/000077500000000000000000000000001511334557700232435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/model.onnx000066400000000000000000000002131511334557700252430ustar00rootroot00000000000000 backend-test:s  x min maxy"Clip test_clipZ x    Z min Z max b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/test_data_set_0/000077500000000000000000000000001511334557700263055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700302140ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700302030ustar00rootroot00000000000000BminJ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700302040ustar00rootroot00000000000000BmaxJ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700304150ustar00rootroot00000000000000ByJđ€?háĖ>“Žz?€?€?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>€?^ĶB?Ā0ų= Bã>]×Ē>€?RžiJ >ĻZŋ€ŋŒS'?ąK]?‡ū=ŋ€?€ŋHm;= ­?ž€?€?ŠĒ>…žÁ>íEcŋ€ŋ‹!˛žō >€?€?ŗOÆžmĮšž€ŋ€ŋ€ŋ€?‘xŋFKāž€ŋœ G?€ŋ—ØYžL=eŋÆ> Äŋ€ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_inbounds/000077500000000000000000000000001511334557700266505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_inbounds/model.onnx000066400000000000000000000001501511334557700306500ustar00rootroot00000000000000 backend-test:P  x y"Cliptest_clip_default_inboundsZ x  b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_inbounds/test_data_set_0/000077500000000000000000000000001511334557700317125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_inbounds/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700336100ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_inbounds/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700340110ustar00rootroot00000000000000ByJ 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test_data_set_0/000077500000000000000000000000001511334557700344455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_int8_inbounds_expandedinput_0.pb000066400000000000000000000000141511334557700363410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_int8_inbounds_expanded/test_data_set_0BxJ˙output_0.pb000066400000000000000000000000141511334557700365420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_int8_inbounds_expanded/test_data_set_0ByJ˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_int8_max/000077500000000000000000000000001511334557700265565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_int8_max/model.onnx000066400000000000000000000002121511334557700305550ustar00rootroot00000000000000 backend-test:r  x maxy"Cliptest_clip_default_int8_maxZ x    Z max b y    B  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backend-test:ž X max xFClip_test_clip_default_int8_max_expanded_function_input_large_than_max"Less: \ FClip_test_clip_default_int8_max_expanded_function_input_large_than_max max xy"Where:#test_clip_default_int8_max_expandedZ x    Z max b y    B  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backend-test:œ W x minEClip_test_clip_default_int8_min_expanded_function_input_less_than_min"Less: [ EClip_test_clip_default_int8_min_expanded_function_input_less_than_min min xy"Where:#test_clip_default_int8_min_expandedZ x    Z min b y    B  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backend-test:m  x maxy"Cliptest_clip_default_maxZ x    Z max b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max/test_data_set_0/000077500000000000000000000000001511334557700306565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700325650ustar00rootroot00000000000000BxJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700325540ustar00rootroot00000000000000BmaxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700327660ustar00rootroot00000000000000ByJđ^&,ŋZ¸ž[*PŋÔöÜŋ;ļÍžWĒĐŋĩDhŋÆžŋˇO/ŋė^ŋ~/ŋЃŸžĄ#•ŋĸŖÄŋ7>8žl‰ŋ5mΞ^ƒŦŋAŸĸŋč(–ŋŪÅĶž3Y?ŋIy\ŋŊ7‰ž÷ēžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/000077500000000000000000000000001511334557700274645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/model.onnx000066400000000000000000000004501511334557700314670ustar00rootroot00000000000000 backend-test: S max xAClip_test_clip_default_max_expanded_function_input_large_than_max"Less: W AClip_test_clip_default_max_expanded_function_input_large_than_max max xy"Where:test_clip_default_max_expandedZ x    Z max b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/test_data_set_0/000077500000000000000000000000001511334557700325265ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700343560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/test_data_set_0BxJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000000151511334557700343450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/test_data_set_0BmaxJoutput_0.pb000066400000000000000000000003761511334557700345570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_max_expanded/test_data_set_0ByJđ^&,ŋZ¸ž[*PŋÔöÜŋ;ļÍžWĒĐŋĩDhŋÆžŋˇO/ŋė^ŋ~/ŋЃŸžĄ#•ŋĸŖÄŋ7>8žl‰ŋ5mΞ^ƒŦŋAŸĸŋč(–ŋŪÅĶž3Y?ŋIy\ŋŊ7‰ž÷ēžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/000077500000000000000000000000001511334557700256125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/model.onnx000066400000000000000000000002031511334557700276110ustar00rootroot00000000000000 backend-test:k  x miny"Cliptest_clip_default_minZ x    Z min b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/test_data_set_0/000077500000000000000000000000001511334557700306545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700325630ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700325520ustar00rootroot00000000000000BminJonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700327640ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī?˙8s?ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?iJ >ŒS'?ąK]?ŠC@Hm;=2Ä?ķŧ?ŠĒ>…žÁ>ō >*z?•į™?ŗų?œ G?Æ>QNÛ>.:ˆ=™Ũš>onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/000077500000000000000000000000001511334557700274625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/model.onnx000066400000000000000000000004461511334557700314720ustar00rootroot00000000000000 backend-test: R x min@Clip_test_clip_default_min_expanded_function_input_less_than_min"Less: V @Clip_test_clip_default_min_expanded_function_input_less_than_min min xy"Where:test_clip_default_min_expandedZ x    Z min b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/test_data_set_0/000077500000000000000000000000001511334557700325245ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700343540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžinput_1.pb000066400000000000000000000000151511334557700343430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/test_data_set_0BminJoutput_0.pb000066400000000000000000000003761511334557700345550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_default_min_expanded/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī?˙8s?ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?iJ >ŒS'?ąK]?ŠC@Hm;=2Ä?ķŧ?ŠĒ>…žÁ>ō >*z?•į™?ŗų?œ G?Æ>QNÛ>.:ˆ=™Ũš>onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/000077500000000000000000000000001511334557700247565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/model.onnx000066400000000000000000000002031511334557700267550ustar00rootroot00000000000000 backend-test:k  x min maxy"Cliptest_clip_exampleZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/test_data_set_0/000077500000000000000000000000001511334557700300205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700317160ustar00rootroot00000000000000BxJ Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700317160ustar00rootroot00000000000000BminJ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700317170ustar00rootroot00000000000000BmaxJ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700321170ustar00rootroot00000000000000ByJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example_expanded/000077500000000000000000000000001511334557700266265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example_expanded/model.onnx000066400000000000000000000011021511334557700306240ustar00rootroot00000000000000 backend-test:Š N x minClip_test_clip_example_expanded_function_output_large_than_max"Less:  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Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example_expanded/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700335660ustar00rootroot00000000000000BminJ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example_expanded/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700335670ustar00rootroot00000000000000BmaxJ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_example_expanded/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700337670ustar00rootroot00000000000000ByJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/000077500000000000000000000000001511334557700251135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/model.onnx000066400000000000000000000010221511334557700271120ustar00rootroot00000000000000 backend-test:ų F x min4Clip_test_clip_expanded_function_input_less_than_min"Less: m 4Clip_test_clip_expanded_function_input_less_than_min min x$Clip_test_clip_expanded_function_tmp"Where: k max $Clip_test_clip_expanded_function_tmp6Clip_test_clip_expanded_function_output_large_than_max"Less: o 6Clip_test_clip_expanded_function_output_large_than_max max $Clip_test_clip_expanded_function_tmpy"Where:test_clip_expandedZ x    Z min Z max b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/test_data_set_0/000077500000000000000000000000001511334557700301555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700320640ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700320530ustar00rootroot00000000000000BminJ€ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700320540ustar00rootroot00000000000000BmaxJ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_expanded/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700322650ustar00rootroot00000000000000ByJđ€?háĖ>“Žz?€?€?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>€?^ĶB?Ā0ų= Bã>]×Ē>€?RžiJ >ĻZŋ€ŋŒS'?ąK]?‡ū=ŋ€?€ŋHm;= ­?ž€?€?ŠĒ>…žÁ>íEcŋ€ŋ‹!˛žō >€?€?ŗOÆžmĮšž€ŋ€ŋ€ŋ€?‘xŋFKāž€ŋœ G?€ŋ—ØYžL=eŋÆ> Äŋ€ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds/000077500000000000000000000000001511334557700251445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds/model.onnx000066400000000000000000000002041511334557700271440ustar00rootroot00000000000000 backend-test:l  x min maxy"Cliptest_clip_inboundsZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds/test_data_set_0/000077500000000000000000000000001511334557700302065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700321040ustar00rootroot00000000000000BxJ 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=Clip_test_clip_inbounds_expanded_function_input_less_than_min min x-Clip_test_clip_inbounds_expanded_function_tmp"Where: } max -Clip_test_clip_inbounds_expanded_function_tmp?Clip_test_clip_inbounds_expanded_function_output_large_than_max"Less:  ?Clip_test_clip_inbounds_expanded_function_output_large_than_max max -Clip_test_clip_inbounds_expanded_function_tmpy"Where:test_clip_inbounds_expandedZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds_expanded/test_data_set_0/000077500000000000000000000000001511334557700320565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_inbounds_expanded/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700337540ustar00rootroot00000000000000BxJ 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max/test_data_set_0/000077500000000000000000000000001511334557700325405ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700343570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max/test_data_set_0BxJ ĀĀ@input_1.pb000066400000000000000000000000151511334557700343570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max/test_data_set_0BminJ@input_2.pb000066400000000000000000000000151511334557700343600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max/test_data_set_0BmaxJ€?output_0.pb000066400000000000000000000000251511334557700345600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max/test_data_set_0ByJ €?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/000077500000000000000000000000001511334557700313465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/model.onnx000066400000000000000000000012551511334557700333550ustar00rootroot00000000000000 backend-test:” [ x minIClip_test_clip_min_greater_than_max_expanded_function_input_less_than_min"Less: — IClip_test_clip_min_greater_than_max_expanded_function_input_less_than_min min x9Clip_test_clip_min_greater_than_max_expanded_function_tmp"Where: • max 9Clip_test_clip_min_greater_than_max_expanded_function_tmpKClip_test_clip_min_greater_than_max_expanded_function_output_large_than_max"Less: ™ KClip_test_clip_min_greater_than_max_expanded_function_output_large_than_max max 9Clip_test_clip_min_greater_than_max_expanded_function_tmpy"Where:'test_clip_min_greater_than_max_expandedZ x  Z min Z max b y  B  test_data_set_0/000077500000000000000000000000001511334557700343315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expandedinput_0.pb000066400000000000000000000000251511334557700362270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/test_data_set_0BxJ ĀĀ@input_1.pb000066400000000000000000000000151511334557700362270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/test_data_set_0BminJ@input_2.pb000066400000000000000000000000151511334557700362300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/test_data_set_0BmaxJ€?output_0.pb000066400000000000000000000000251511334557700364300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_min_greater_than_max_expanded/test_data_set_0ByJ €?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/000077500000000000000000000000001511334557700253455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/model.onnx000066400000000000000000000002051511334557700273460ustar00rootroot00000000000000 backend-test:m  x min maxy"Cliptest_clip_outboundsZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/test_data_set_0/000077500000000000000000000000001511334557700304075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700323050ustar00rootroot00000000000000BxJ ĀĀĀ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700323050ustar00rootroot00000000000000BminJ Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700323060ustar00rootroot00000000000000BmaxJ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700325060ustar00rootroot00000000000000ByJ  Ā @onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/000077500000000000000000000000001511334557700272155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/model.onnx000066400000000000000000000011241511334557700312170ustar00rootroot00000000000000 backend-test:ģ P x min>Clip_test_clip_outbounds_expanded_function_input_less_than_min"Less:  >Clip_test_clip_outbounds_expanded_function_input_less_than_min min x.Clip_test_clip_outbounds_expanded_function_tmp"Where:  max .Clip_test_clip_outbounds_expanded_function_tmp@Clip_test_clip_outbounds_expanded_function_output_large_than_max"Less: ƒ @Clip_test_clip_outbounds_expanded_function_output_large_than_max max .Clip_test_clip_outbounds_expanded_function_tmpy"Where:test_clip_outbounds_expandedZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/test_data_set_0/000077500000000000000000000000001511334557700322575ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700340760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/test_data_set_0BxJ ĀĀĀ@input_1.pb000066400000000000000000000000151511334557700340760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/test_data_set_0BminJ Āinput_2.pb000066400000000000000000000000151511334557700340770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/test_data_set_0BmaxJ @output_0.pb000066400000000000000000000000251511334557700342770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_outbounds_expanded/test_data_set_0ByJ  Ā @onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/000077500000000000000000000000001511334557700256715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/model.onnx000066400000000000000000000002071511334557700276740ustar00rootroot00000000000000 backend-test:o  x min maxy"Cliptest_clip_splitboundsZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/test_data_set_0/000077500000000000000000000000001511334557700307335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700326310ustar00rootroot00000000000000BxJ €ŋĀ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/test_data_set_0/input_1.pb000066400000000000000000000000151511334557700326310ustar00rootroot00000000000000BminJ Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/test_data_set_0/input_2.pb000066400000000000000000000000151511334557700326320ustar00rootroot00000000000000BmaxJ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700330320ustar00rootroot00000000000000ByJ €ŋ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/000077500000000000000000000000001511334557700275415ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/model.onnx000066400000000000000000000011451511334557700315460ustar00rootroot00000000000000 backend-test:Ė R x min@Clip_test_clip_splitbounds_expanded_function_input_less_than_min"Less: … @Clip_test_clip_splitbounds_expanded_function_input_less_than_min min x0Clip_test_clip_splitbounds_expanded_function_tmp"Where: ƒ max 0Clip_test_clip_splitbounds_expanded_function_tmpBClip_test_clip_splitbounds_expanded_function_output_large_than_max"Less: ‡ BClip_test_clip_splitbounds_expanded_function_output_large_than_max max 0Clip_test_clip_splitbounds_expanded_function_tmpy"Where:test_clip_splitbounds_expandedZ x  Z min Z max b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/test_data_set_0/000077500000000000000000000000001511334557700326035ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700344220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/test_data_set_0BxJ €ŋĀ@input_1.pb000066400000000000000000000000151511334557700344220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/test_data_set_0BminJ Āinput_2.pb000066400000000000000000000000151511334557700344230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/test_data_set_0BmaxJ @output_0.pb000066400000000000000000000000251511334557700346230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_clip_splitbounds_expanded/test_data_set_0ByJ €ŋ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/000077500000000000000000000000001511334557700235015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/model.onnx000066400000000000000000000003161511334557700255050ustar00rootroot00000000000000 backend-test:ĩ 1 input image_shape block_shapeoutput"Col2Im test_col2imZ input    Z image_shape  Z block_shape  b output     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/test_data_set_0/000077500000000000000000000000001511334557700265435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/test_data_set_0/input_0.pb000066400000000000000000000001651511334557700304460ustar00rootroot00000000000000BinputJd€?Ā@0A€A¨A@ā@@AˆA°A@@APAA¸A€@A`A˜AĀA @pA AČAonnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/test_data_set_0/input_1.pb000066400000000000000000000000431511334557700304420ustar00rootroot00000000000000B image_shapeJonnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/test_data_set_0/input_2.pb000066400000000000000000000000431511334557700304430ustar00rootroot00000000000000B block_shapeJonnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im/test_data_set_0/output_0.pb000066400000000000000000000001701511334557700306430ustar00rootroot00000000000000BoutputJd€?@@@€@ @Ā@ā@AA0A@APA`ApA€AˆAA˜A A¨A°A¸AĀAČAonnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im_5d/000077500000000000000000000000001511334557700240715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im_5d/model.onnx000066400000000000000000000003251511334557700260750ustar00rootroot00000000000000 backend-test:ŧ 1 input image_shape block_shapeoutput"Col2Imtest_col2im_5dZ input     Z image_shape  Z block_shape  b$ output      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im_5d/test_data_set_0/000077500000000000000000000000001511334557700271335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_col2im_5d/test_data_set_0/input_0.pb000066400000000000000000000007621511334557700310410ustar00rootroot00000000000000  BinputJā€?Ā@0A€A¨AĐAøAB$B8BLB`B@ā@@AˆA°AØABB(B“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@B const_tensor  test_constantb values   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant/test_data_set_0/000077500000000000000000000000001511334557700272075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant/test_data_set_0/output_0.pb000066400000000000000000000001641511334557700313120ustar00rootroot00000000000000BvaluesJdxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/000077500000000000000000000000001511334557700247715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/model.onnx000066400000000000000000000002721511334557700267760ustar00rootroot00000000000000  backend-test:Ą - x pads valuey"Pad* mode"constant test_constant_padZ x     Z pads  Z value b y      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/test_data_set_0/000077500000000000000000000000001511334557700300335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/test_data_set_0/input_0.pb000066400000000000000000000004001511334557700317260ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/test_data_set_0/input_1.pb000066400000000000000000000001141511334557700317310ustar00rootroot00000000000000BpadsJ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/test_data_set_0/input_2.pb000066400000000000000000000000171511334557700317340ustar00rootroot00000000000000BvalueJš™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad/test_data_set_0/output_0.pb000066400000000000000000000020001511334557700321250ustar00rootroot00000000000000 ByJđš™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?xĖá?háĖ>“Žz?Ëj@$ ī?š™™?š™™?š™™?š™™?š™™?š™™?š™™?â.zŋ˙8s?bũžhdĶŊø9Ō>š™™?š™™?š™™?š™™?š™™?š™™?š™™?(€>ĸ%ē?^ĶB?Ā0ų= Bã>š™™?š™™?š™™?š™™?š™™?š™™?š™™?]×Ē>ü=ŋ?RžiJ >ĻZŋš™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?/d#ŒS'?ąK]?‡ū=ŋŠC@š™™?š™™?š™™?š™™?š™™?š™™?š™™?¨(ēŋHm;= ­?ž2Ä?ķŧ?š™™?š™™?š™™?š™™?š™™?š™™?š™™?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žš™™?š™™?š™™?š™™?š™™?š™™?š™™?ō >*z?•į™?ŗOÆžmĮšžš™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?ü6†ŋ&ÃĩŋgÚŋŗų?‘xŋš™™?š™™?š™™?š™™?š™™?š™™?š™™?FKāž™[ ŋœ G?4”Îŋ—ØYžš™™?š™™?š™™?š™™?š™™?š™™?š™™?L=eŋÆ> Äŋõ—ŋkŪæŧš™™?š™™?š™™?š™™?š™™?š™™?š™™?QNÛ>.:ˆ=™Ũš>īb"ŋ6ššžš™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?š™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/000077500000000000000000000000001511334557700260115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/model.onnx000066400000000000000000000003311511334557700300120ustar00rootroot00000000000000  backend-test:Ā 3 x pads value axesy"Pad* mode"constant test_constant_pad_axesZ x     Z pads  Z value Z axes  b y      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/000077500000000000000000000000001511334557700310535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/input_0.pb000066400000000000000000000004001511334557700327460ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/input_1.pb000066400000000000000000000000541511334557700327540ustar00rootroot00000000000000BpadsJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/input_2.pb000066400000000000000000000000171511334557700327540ustar00rootroot00000000000000BvalueJš™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/input_3.pb000066400000000000000000000000341511334557700327540ustar00rootroot00000000000000BaxesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_axes/test_data_set_0/output_0.pb000066400000000000000000000011201511334557700331470ustar00rootroot00000000000000 ByJĀš™™?š™™?š™™?xĖá?háĖ>“Žz?Ëj@$ ī?š™™?š™™?š™™?š™™?š™™?š™™?š™™?â.zŋ˙8s?bũžhdĶŊø9Ō>š™™?š™™?š™™?š™™?š™™?š™™?š™™?(€>ĸ%ē?^ĶB?Ā0ų= Bã>š™™?š™™?š™™?š™™?š™™?š™™?š™™?]×Ē>ü=ŋ?RžiJ >ĻZŋš™™?š™™?š™™?š™™?š™™?š™™?š™™?/d#ŒS'?ąK]?‡ū=ŋŠC@š™™?š™™?š™™?š™™?š™™?š™™?š™™?¨(ēŋHm;= ­?ž2Ä?ķŧ?š™™?š™™?š™™?š™™?š™™?š™™?š™™?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žš™™?š™™?š™™?š™™?š™™?š™™?š™™?ō >*z?•į™?ŗOÆžmĮšžš™™?š™™?š™™?š™™?š™™?š™™?š™™?ü6†ŋ&ÃĩŋgÚŋŗų?‘xŋš™™?š™™?š™™?š™™?š™™?š™™?š™™?FKāž™[ ŋœ G?4”Îŋ—ØYžš™™?š™™?š™™?š™™?š™™?š™™?š™™?L=eŋÆ> Äŋõ—ŋkŪæŧš™™?š™™?š™™?š™™?š™™?š™™?š™™?QNÛ>.:ˆ=™Ũš>īb"ŋ6ššžš™™?š™™?š™™?š™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_negative_axes/000077500000000000000000000000001511334557700276735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_negative_axes/model.onnx000066400000000000000000000003421511334557700316760ustar00rootroot00000000000000  backend-test:É 3 x pads value axesy"Pad* mode"constant test_constant_pad_negative_axesZ x     Z pads  Z value Z axes  b y      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_negative_axes/test_data_set_0/000077500000000000000000000000001511334557700327355ustar00rootroot00000000000000input_0.pb000066400000000000000000000004001511334557700345510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_negative_axes/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžinput_1.pb000066400000000000000000000000541511334557700345570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_constant_pad_negative_axes/test_data_set_0BpadsJ 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MŋŦ;DžüqŋÆ>0ËĖžčb1ŋ@ĨãŧQNÛ>.:ˆ=™Ũš>2}đž~ěžonnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_default_expanded_ver18/000077500000000000000000000000001511334557700275025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_default_expanded_ver18/model.onnx000066400000000000000000000024451511334557700315130ustar00rootroot00000000000000 backend-test:Œ Q,Elu_test_elu_default_expanded_function_Alpha"Constant* value_float€? : o ,Elu_test_elu_default_expanded_function_Alpha x0Elu_test_elu_default_expanded_function_AlphaCast"CastLike: Q+Elu_test_elu_default_expanded_function_Zero"Constant* value* "B : m +Elu_test_elu_default_expanded_function_Zero x/Elu_test_elu_default_expanded_function_ZeroCast"CastLike: P*Elu_test_elu_default_expanded_function_One"Constant* value* "€?B : k *Elu_test_elu_default_expanded_function_One x.Elu_test_elu_default_expanded_function_OneCast"CastLike: r x /Elu_test_elu_default_expanded_function_ZeroCast4Elu_test_elu_default_expanded_function_XLessThanZero"Less: 7 x+Elu_test_elu_default_expanded_function_ExpX"Exp: — +Elu_test_elu_default_expanded_function_ExpX .Elu_test_elu_default_expanded_function_OneCast1Elu_test_elu_default_expanded_function_ExpXSubOne"Sub: § 0Elu_test_elu_default_expanded_function_AlphaCast 1Elu_test_elu_default_expanded_function_ExpXSubOne9Elu_test_elu_default_expanded_function_AlphaMulExpXSubOne"Mul: € 4Elu_test_elu_default_expanded_function_XLessThanZero 9Elu_test_elu_default_expanded_function_AlphaMulExpXSubOne xy"Where:test_elu_default_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_default_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700325445ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700343740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_default_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700345750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_default_expanded_ver18/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī?‘¨ŋ˙8s?˜Ôž0ŲČŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?čî=žiJ >nŋlŋŒS'?ąK]?|ŋŠC@+6DŋHm;=HÎ.ž2Ä?ķŧ?ŠĒ>…žÁ>œŖŋŗŽ\ŋr–žō >*z?•į™?Bk¤žŌ’…žFI&ŋPBŋ}†Qŋŗų?vpĖž&žĩž Ü6ŋœ G? MŋŦ;DžüqŋÆ>0ËĖžčb1ŋ@ĨãŧQNÛ>.:ˆ=™Ũš>2}đž~ěžonnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example/000077500000000000000000000000001511334557700246145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example/model.onnx000066400000000000000000000001521511334557700266160ustar00rootroot00000000000000  backend-test:R  xy"Elu* alpha@ test_elu_exampleZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example/test_data_set_0/000077500000000000000000000000001511334557700276565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700315540ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700317550ustar00rootroot00000000000000ByJ ¨ŌĄŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example_expanded_ver18/000077500000000000000000000000001511334557700275115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example_expanded_ver18/model.onnx000066400000000000000000000024251511334557700315200ustar00rootroot00000000000000 backend-test:ü Q,Elu_test_elu_example_expanded_function_Alpha"Constant* value_float@ : o ,Elu_test_elu_example_expanded_function_Alpha x0Elu_test_elu_example_expanded_function_AlphaCast"CastLike: Q+Elu_test_elu_example_expanded_function_Zero"Constant* value* "B : m +Elu_test_elu_example_expanded_function_Zero x/Elu_test_elu_example_expanded_function_ZeroCast"CastLike: P*Elu_test_elu_example_expanded_function_One"Constant* value* "€?B : k *Elu_test_elu_example_expanded_function_One x.Elu_test_elu_example_expanded_function_OneCast"CastLike: r x /Elu_test_elu_example_expanded_function_ZeroCast4Elu_test_elu_example_expanded_function_XLessThanZero"Less: 7 x+Elu_test_elu_example_expanded_function_ExpX"Exp: — +Elu_test_elu_example_expanded_function_ExpX .Elu_test_elu_example_expanded_function_OneCast1Elu_test_elu_example_expanded_function_ExpXSubOne"Sub: § 0Elu_test_elu_example_expanded_function_AlphaCast 1Elu_test_elu_example_expanded_function_ExpXSubOne9Elu_test_elu_example_expanded_function_AlphaMulExpXSubOne"Mul: € 4Elu_test_elu_example_expanded_function_XLessThanZero 9Elu_test_elu_example_expanded_function_AlphaMulExpXSubOne xy"Where:test_elu_example_expanded_ver18Z x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700325535ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700343720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example_expanded_ver18/test_data_set_0BxJ €ŋ€?output_0.pb000066400000000000000000000000251511334557700345730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_example_expanded_ver18/test_data_set_0ByJ ¨ŌĄŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_expanded_ver18/000077500000000000000000000000001511334557700257765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_expanded_ver18/model.onnx000066400000000000000000000021731511334557700300050ustar00rootroot00000000000000 backend-test:â I$Elu_test_elu_expanded_function_Alpha"Constant* value_float@ : _ $Elu_test_elu_expanded_function_Alpha x(Elu_test_elu_expanded_function_AlphaCast"CastLike: I#Elu_test_elu_expanded_function_Zero"Constant* value* "B : ] #Elu_test_elu_expanded_function_Zero x'Elu_test_elu_expanded_function_ZeroCast"CastLike: H"Elu_test_elu_expanded_function_One"Constant* value* "€?B : [ "Elu_test_elu_expanded_function_One x&Elu_test_elu_expanded_function_OneCast"CastLike: b x 'Elu_test_elu_expanded_function_ZeroCast,Elu_test_elu_expanded_function_XLessThanZero"Less: / x#Elu_test_elu_expanded_function_ExpX"Exp:  #Elu_test_elu_expanded_function_ExpX &Elu_test_elu_expanded_function_OneCast)Elu_test_elu_expanded_function_ExpXSubOne"Sub:  (Elu_test_elu_expanded_function_AlphaCast )Elu_test_elu_expanded_function_ExpXSubOne1Elu_test_elu_expanded_function_AlphaMulExpXSubOne"Mul: p ,Elu_test_elu_expanded_function_XLessThanZero 1Elu_test_elu_expanded_function_AlphaMulExpXSubOne xy"Where:test_elu_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700310405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_expanded_ver18/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700327470ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_elu_expanded_ver18/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700331500ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī?‘¨Ÿŋ˙8s?˜Ôž0ŲHžø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?čîŊžiJ >n“ŋėŋŒS'?ąK]?|†ŋŠC@+6ÄŋHm;=HÎŽž2Ä?ķŧ?ŠĒ>…žÁ>œŖ–ŋŗŽÜŋrŋō >*z?•į™?Bk$ŋŌ’ŋFIĻŋPÂŋ}†Ņŋŗų?vpLŋ&ž5ŋ Üļŋœ G? 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ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis0/test_data_set_0/output_0.pb000066400000000000000000000007541511334557700322270ustar00rootroot00000000000000xBbJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis1/000077500000000000000000000000001511334557700250565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis1/model.onnx000066400000000000000000000001741511334557700270640ustar00rootroot00000000000000  backend-test:d  ab"Flatten* axis test_flatten_axis1Z a     b b   QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis1/test_data_set_0/output_0.pb000066400000000000000000000007541511334557700322300ustar00rootroot00000000000000<BbJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis2/000077500000000000000000000000001511334557700250575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis2/model.onnx000066400000000000000000000001741511334557700270650ustar00rootroot00000000000000  backend-test:d  ab"Flatten* axis test_flatten_axis2Z a     b b   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis2/test_data_set_0/000077500000000000000000000000001511334557700301215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis2/test_data_set_0/input_0.pb000066400000000000000000000007601511334557700320250ustar00rootroot00000000000000BaJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis2/test_data_set_0/output_0.pb000066400000000000000000000007541511334557700322310ustar00rootroot00000000000000BbJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis3/000077500000000000000000000000001511334557700250605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis3/model.onnx000066400000000000000000000001741511334557700270660ustar00rootroot00000000000000  backend-test:d  ab"Flatten* axis test_flatten_axis3Z a     b b   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis3/test_data_set_0/000077500000000000000000000000001511334557700301225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis3/test_data_set_0/input_0.pb000066400000000000000000000007601511334557700320260ustar00rootroot00000000000000BaJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_axis3/test_data_set_0/output_0.pb000066400000000000000000000007541511334557700322320ustar00rootroot00000000000000BbJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_default_axis/000077500000000000000000000000001511334557700265015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_default_axis/model.onnx000066400000000000000000000001661511334557700305100ustar00rootroot00000000000000  backend-test:^  ab"Flattentest_flatten_default_axisZ a     b b   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_default_axis/test_data_set_0/000077500000000000000000000000001511334557700315435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_flatten_default_axis/test_data_set_0/input_0.pb000066400000000000000000000007601511334557700334470ustar00rootroot00000000000000BaJā  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋŊ7‰žČmM?r?÷ēžN4?Ŋl?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gather_2d_indices/000077500000000000000000000000001511334557700256515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gather_2d_indices/model.onnx000066400000000000000000000002461511334557700276570ustar00rootroot00000000000000 backend-test: ' data indicesy"Gather* axis test_gather_2d_indicesZ data   Z indices   b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_gather_2d_indices/test_data_set_0/000077500000000000000000000000001511334557700307135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gather_2d_indices/test_data_set_0/input_0.pb000066400000000000000000000000621511334557700326120ustar00rootroot00000000000000BdataJ$xĖá?háĖ>“Žz?Ëj@$ 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output_0.pb000066400000000000000000000000261511334557700337250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gathernd_example_int32/test_data_set_0BoutputJonnx-onnx-bca0315/onnx/backend/test/data/node/test_gathernd_example_int32_batch_dim1/000077500000000000000000000000001511334557700307155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gathernd_example_int32_batch_dim1/model.onnx000066400000000000000000000003101511334557700327130ustar00rootroot00000000000000 backend-test:¯ 4 data indicesoutput"GatherND* batch_dims &test_gathernd_example_int32_batch_dim1Z data    Z indices   b output   B  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input_1.pb000066400000000000000000000000411511334557700355750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gathernd_example_int32_batch_dim1/test_data_set_0BindicesJoutput_0.pb000066400000000000000000000000401511334557700357740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gathernd_example_int32_batch_dim1/test_data_set_0BoutputJonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1/000077500000000000000000000000001511334557700251745ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1/model.onnx000066400000000000000000000001351511334557700271770ustar00rootroot00000000000000  backend-test:E xy"Gelutest_gelu_default_1Z x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1/test_data_set_0/000077500000000000000000000000001511334557700302365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700321340ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700323350ustar00rootroot00000000000000ByJ †v"ž_bW?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1_expanded/000077500000000000000000000000001511334557700270445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1_expanded/model.onnx000066400000000000000000000026251511334557700310550ustar00rootroot00000000000000  backend-test:ü U/Gelu_test_gelu_default_1_expanded_function_Half"Constant* value* "?B : u /Gelu_test_gelu_default_1_expanded_function_Half x3Gelu_test_gelu_default_1_expanded_function_HalfCast"CastLike: T.Gelu_test_gelu_default_1_expanded_function_One"Constant* value* "€?B : s .Gelu_test_gelu_default_1_expanded_function_One x2Gelu_test_gelu_default_1_expanded_function_OneCast"CastLike: T.Gelu_test_gelu_default_1_expanded_function_Two"Constant* value* "@B : s .Gelu_test_gelu_default_1_expanded_function_Two x2Gelu_test_gelu_default_1_expanded_function_TwoCast"CastLike: p 2Gelu_test_gelu_default_1_expanded_function_TwoCast2Gelu_test_gelu_default_1_expanded_function_SqrtTwo"Sqrt: p x 2Gelu_test_gelu_default_1_expanded_function_SqrtTwo0Gelu_test_gelu_default_1_expanded_function_XSqrt"Div: n 0Gelu_test_gelu_default_1_expanded_function_XSqrt3Gelu_test_gelu_default_1_expanded_function_ErfXSqrt"Erf:   2Gelu_test_gelu_default_1_expanded_function_OneCast 3Gelu_test_gelu_default_1_expanded_function_ErfXSqrt.Gelu_test_gelu_default_1_expanded_function_Phi"Sum: q 3Gelu_test_gelu_default_1_expanded_function_HalfCast x0Gelu_test_gelu_default_1_expanded_function_MultX"Mul: l 0Gelu_test_gelu_default_1_expanded_function_MultX .Gelu_test_gelu_default_1_expanded_function_Phiy"Mul:test_gelu_default_1_expandedZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1_expanded/test_data_set_0/000077500000000000000000000000001511334557700321065ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700337250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1_expanded/test_data_set_0BxJ €ŋ€?output_0.pb000066400000000000000000000000251511334557700341260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_1_expanded/test_data_set_0ByJ †v"ž_bW?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2/000077500000000000000000000000001511334557700251755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2/model.onnx000066400000000000000000000001551511334557700272020ustar00rootroot00000000000000  backend-test:U xy"Gelutest_gelu_default_2Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2/test_data_set_0/000077500000000000000000000000001511334557700302375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700321460ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700323470ustar00rootroot00000000000000ByJđŲ?…K†>ƒ€Q?AŸ @t¨į? V$ž–ŸI?ôWˆŊuBŊ›Š>úd¤=’Ŧ?VQ?VЈ=q–˜>Ž~W>‡Q˛?Ķî¯Ŋx¯G>eá+ž8^_ŧ$Áø>šo2?Š.žŪ“@Û3ŲŊxDÂ<+7ŖŊ¯æˇ?ĐŋŽ?24˛=I­z>kL*žŸ'AŊĨŠžūŗ=DŒ?Dˆ?÷… ž|ėŊí žãBâŊŽ™ŊKRķ?h@ž:UžZáž’?S°Ŋ;%ĩŊ<â)žā€>fžˇž›­aŧJ’>˛s=Š­?>ŪÉ*žS žonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2_expanded/000077500000000000000000000000001511334557700270455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2_expanded/model.onnx000066400000000000000000000026451511334557700310600ustar00rootroot00000000000000  backend-test:Œ U/Gelu_test_gelu_default_2_expanded_function_Half"Constant* value* "?B : u /Gelu_test_gelu_default_2_expanded_function_Half x3Gelu_test_gelu_default_2_expanded_function_HalfCast"CastLike: T.Gelu_test_gelu_default_2_expanded_function_One"Constant* value* "€?B : s .Gelu_test_gelu_default_2_expanded_function_One x2Gelu_test_gelu_default_2_expanded_function_OneCast"CastLike: T.Gelu_test_gelu_default_2_expanded_function_Two"Constant* value* "@B : s .Gelu_test_gelu_default_2_expanded_function_Two x2Gelu_test_gelu_default_2_expanded_function_TwoCast"CastLike: p 2Gelu_test_gelu_default_2_expanded_function_TwoCast2Gelu_test_gelu_default_2_expanded_function_SqrtTwo"Sqrt: p x 2Gelu_test_gelu_default_2_expanded_function_SqrtTwo0Gelu_test_gelu_default_2_expanded_function_XSqrt"Div: n 0Gelu_test_gelu_default_2_expanded_function_XSqrt3Gelu_test_gelu_default_2_expanded_function_ErfXSqrt"Erf:   2Gelu_test_gelu_default_2_expanded_function_OneCast 3Gelu_test_gelu_default_2_expanded_function_ErfXSqrt.Gelu_test_gelu_default_2_expanded_function_Phi"Sum: q 3Gelu_test_gelu_default_2_expanded_function_HalfCast x0Gelu_test_gelu_default_2_expanded_function_MultX"Mul: l 0Gelu_test_gelu_default_2_expanded_function_MultX .Gelu_test_gelu_default_2_expanded_function_Phiy"Mul:test_gelu_default_2_expandedZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2_expanded/test_data_set_0/000077500000000000000000000000001511334557700321075ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700337370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2_expanded/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700341400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_default_2_expanded/test_data_set_0ByJđŲ?…K†>ƒ€Q?AŸ @t¨į? V$ž–ŸI?ôWˆŊuBŊ›Š>úd¤=’Ŧ?VQ?VЈ=q–˜>Ž~W>‡Q˛?Ķî¯Ŋx¯G>eá+ž8^_ŧ$Áø>šo2?Š.žŪ“@Û3ŲŊxDÂ<+7ŖŊ¯æˇ?ĐŋŽ?24˛=I­z>kL*žŸ'AŊĨŠžūŗ=DŒ?Dˆ?÷… ž|ėŊí žãBâŊŽ™ŊKRķ?h@ž:UžZáž’?S°Ŋ;%ĩŊ<â)žā€>fžˇž›­aŧJ’>˛s=Š­?>ŪÉ*žS žonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1/000077500000000000000000000000001511334557700245025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1/model.onnx000066400000000000000000000001621511334557700265050ustar00rootroot00000000000000  backend-test:Z $ xy"Gelu* approximate"tanh test_gelu_tanh_1Z x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1/test_data_set_0/000077500000000000000000000000001511334557700275445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700314420ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700316430ustar00rootroot00000000000000ByJ ž"ž\XW?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1_expanded/000077500000000000000000000000001511334557700263525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1_expanded/model.onnx000066400000000000000000000042771511334557700303700ustar00rootroot00000000000000  backend-test:Ļ R,Gelu_test_gelu_tanh_1_expanded_function_Half"Constant* value* "?B : o ,Gelu_test_gelu_tanh_1_expanded_function_Half x0Gelu_test_gelu_tanh_1_expanded_function_HalfCast"CastLike: Q+Gelu_test_gelu_tanh_1_expanded_function_One"Constant* value* "€?B : m +Gelu_test_gelu_tanh_1_expanded_function_One x/Gelu_test_gelu_tanh_1_expanded_function_OneCast"CastLike: W1Gelu_test_gelu_tanh_1_expanded_function_TwoOverPi"Constant* value* "ƒų"?B : y 1Gelu_test_gelu_tanh_1_expanded_function_TwoOverPi x5Gelu_test_gelu_tanh_1_expanded_function_TwoOverPiCast"CastLike: P*Gelu_test_gelu_tanh_1_expanded_function_C0"Constant* value* "'7=B : k *Gelu_test_gelu_tanh_1_expanded_function_C0 x.Gelu_test_gelu_tanh_1_expanded_function_C0Cast"CastLike: v 5Gelu_test_gelu_tanh_1_expanded_function_TwoOverPiCast5Gelu_test_gelu_tanh_1_expanded_function_SqrtTwoOverPi"Sqrt: S-Gelu_test_gelu_tanh_1_expanded_function_Three"Constant* value* "@@B : q -Gelu_test_gelu_tanh_1_expanded_function_Three x1Gelu_test_gelu_tanh_1_expanded_function_ThreeCast"CastLike: m x 1Gelu_test_gelu_tanh_1_expanded_function_ThreeCast.Gelu_test_gelu_tanh_1_expanded_function_XCubed"Pow: ™ .Gelu_test_gelu_tanh_1_expanded_function_C0Cast .Gelu_test_gelu_tanh_1_expanded_function_XCubed0Gelu_test_gelu_tanh_1_expanded_function_XCubedC0"Mul: o x 0Gelu_test_gelu_tanh_1_expanded_function_XCubedC01Gelu_test_gelu_tanh_1_expanded_function_XC0XCubed"Sum: ¤ 5Gelu_test_gelu_tanh_1_expanded_function_SqrtTwoOverPi 1Gelu_test_gelu_tanh_1_expanded_function_XC0XCubed1Gelu_test_gelu_tanh_1_expanded_function_TanhInput"Mul: n 1Gelu_test_gelu_tanh_1_expanded_function_TanhInput1Gelu_test_gelu_tanh_1_expanded_function_ErfApprox"Tanh: ž /Gelu_test_gelu_tanh_1_expanded_function_OneCast 1Gelu_test_gelu_tanh_1_expanded_function_ErfApprox1Gelu_test_gelu_tanh_1_expanded_function_PhiApprox"Sum: k 0Gelu_test_gelu_tanh_1_expanded_function_HalfCast x-Gelu_test_gelu_tanh_1_expanded_function_MultX"Mul: l -Gelu_test_gelu_tanh_1_expanded_function_MultX 1Gelu_test_gelu_tanh_1_expanded_function_PhiApproxy"Mul:test_gelu_tanh_1_expandedZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1_expanded/test_data_set_0/000077500000000000000000000000001511334557700314145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1_expanded/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700333120ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_1_expanded/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700335130ustar00rootroot00000000000000ByJ ž"ž\XW?onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2/000077500000000000000000000000001511334557700245035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2/model.onnx000066400000000000000000000002021511334557700265010ustar00rootroot00000000000000  backend-test:j $ xy"Gelu* approximate"tanh test_gelu_tanh_2Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2/test_data_set_0/000077500000000000000000000000001511334557700275455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700314540ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700316550ustar00rootroot00000000000000ByJđŲ?ˆJ†>ūvQ?¤ @¨į?ü{$žŧ–I? XˆŊBŊũ™Š>įd¤=“ŠŦ?œL?LЈ=ü”˜>ą}W>ZJ˛?ī¯Ŋ˛ŽG> ü+žÕįWŧ]ģø>Õh2?%.ž™@šĒŲŊwDÂ<`7ŖŊØßˇ?x¸Ž?4˛=°Ģz>j*žZŌ@ŊÎĒžūŗ=Ú<Œ? =ˆ?ļ‡ ž×ėŊ_7žŧâŊ*ō™ŊTķ??EžXžž…Š?°s°Ŋ“%ĩŊ˜*ž˛ß€>pkžęížœ­aŧ’>°s=ûŦ?>PÔ*ž°!žonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2_expanded/000077500000000000000000000000001511334557700263535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2_expanded/model.onnx000066400000000000000000000043171511334557700303640ustar00rootroot00000000000000  backend-test:ļ R,Gelu_test_gelu_tanh_2_expanded_function_Half"Constant* value* "?B : o ,Gelu_test_gelu_tanh_2_expanded_function_Half x0Gelu_test_gelu_tanh_2_expanded_function_HalfCast"CastLike: Q+Gelu_test_gelu_tanh_2_expanded_function_One"Constant* value* "€?B : m +Gelu_test_gelu_tanh_2_expanded_function_One x/Gelu_test_gelu_tanh_2_expanded_function_OneCast"CastLike: W1Gelu_test_gelu_tanh_2_expanded_function_TwoOverPi"Constant* value* "ƒų"?B : y 1Gelu_test_gelu_tanh_2_expanded_function_TwoOverPi x5Gelu_test_gelu_tanh_2_expanded_function_TwoOverPiCast"CastLike: P*Gelu_test_gelu_tanh_2_expanded_function_C0"Constant* value* "'7=B : k *Gelu_test_gelu_tanh_2_expanded_function_C0 x.Gelu_test_gelu_tanh_2_expanded_function_C0Cast"CastLike: v 5Gelu_test_gelu_tanh_2_expanded_function_TwoOverPiCast5Gelu_test_gelu_tanh_2_expanded_function_SqrtTwoOverPi"Sqrt: S-Gelu_test_gelu_tanh_2_expanded_function_Three"Constant* value* "@@B : q -Gelu_test_gelu_tanh_2_expanded_function_Three x1Gelu_test_gelu_tanh_2_expanded_function_ThreeCast"CastLike: m x 1Gelu_test_gelu_tanh_2_expanded_function_ThreeCast.Gelu_test_gelu_tanh_2_expanded_function_XCubed"Pow: ™ .Gelu_test_gelu_tanh_2_expanded_function_C0Cast .Gelu_test_gelu_tanh_2_expanded_function_XCubed0Gelu_test_gelu_tanh_2_expanded_function_XCubedC0"Mul: o x 0Gelu_test_gelu_tanh_2_expanded_function_XCubedC01Gelu_test_gelu_tanh_2_expanded_function_XC0XCubed"Sum: ¤ 5Gelu_test_gelu_tanh_2_expanded_function_SqrtTwoOverPi 1Gelu_test_gelu_tanh_2_expanded_function_XC0XCubed1Gelu_test_gelu_tanh_2_expanded_function_TanhInput"Mul: n 1Gelu_test_gelu_tanh_2_expanded_function_TanhInput1Gelu_test_gelu_tanh_2_expanded_function_ErfApprox"Tanh: ž /Gelu_test_gelu_tanh_2_expanded_function_OneCast 1Gelu_test_gelu_tanh_2_expanded_function_ErfApprox1Gelu_test_gelu_tanh_2_expanded_function_PhiApprox"Sum: k 0Gelu_test_gelu_tanh_2_expanded_function_HalfCast x-Gelu_test_gelu_tanh_2_expanded_function_MultX"Mul: l -Gelu_test_gelu_tanh_2_expanded_function_MultX 1Gelu_test_gelu_tanh_2_expanded_function_PhiApproxy"Mul:test_gelu_tanh_2_expandedZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2_expanded/test_data_set_0/000077500000000000000000000000001511334557700314155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2_expanded/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700333240ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_gelu_tanh_2_expanded/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700335250ustar00rootroot00000000000000ByJđŲ?ˆJ†>ūvQ?¤ @¨į?ü{$žŧ–I? XˆŊBŊũ™Š>įd¤=“ŠŦ?œL?LЈ=ü”˜>ą}W>ZJ˛?ī¯Ŋ˛ŽG> ü+žÕįWŧ]ģø>Õh2?%.ž™@šĒŲŊwDÂ<`7ŖŊØßˇ?x¸Ž?4˛=°Ģz>j*žZŌ@ŊÎĒžūŗ=Ú<Œ? =ˆ?ļ‡ ž×ėŊ_7žŧâŊ*ō™ŊTķ??EžXžž…Š?°s°Ŋ“%ĩŊ˜*ž˛ß€>pkžęížœ­aŧ’>°s=ûŦ?>PÔ*ž°!žonnx-onnx-bca0315/onnx/backend/test/data/node/test_gemm_all_attributes/000077500000000000000000000000001511334557700263375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gemm_all_attributes/model.onnx000066400000000000000000000003321511334557700303410ustar00rootroot00000000000000 backend-test:Á Q a b cy"Gemm* alpha€> * beta33ŗ> * transA * transB test_gemm_all_attributesZ a   Z b   Z c   b y   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_gemm_all_attributes/test_data_set_0/000077500000000000000000000000001511334557700314015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_gemm_all_attributes/test_data_set_0/input_0.pb000066400000000000000000000000731511334557700333020ustar00rootroot00000000000000BaJ0  ?Ļ7?ŗN?w} ?HéØ>QY%?n 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 backend-test:= xy"IsInf test_isinfZ x  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf/test_data_set_0/000077500000000000000000000000001511334557700264665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700303620ustar00rootroot00000000000000BxJš™™ŋĀ€333@€˙€onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700305660ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_float16/000077500000000000000000000000001511334557700247605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_float16/model.onnx000066400000000000000000000001351511334557700267630ustar00rootroot00000000000000  backend-test:E xy"IsInftest_isinf_float16Z x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_float16/test_data_set_0/000077500000000000000000000000001511334557700300225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_float16/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700317200ustar00rootroot00000000000000 BxJ Íŧ~|šAü|onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_float16/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700321220ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_negative/000077500000000000000000000000001511334557700253065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_negative/model.onnx000066400000000000000000000001661511334557700273150ustar00rootroot00000000000000  backend-test:^ % xy"IsInf* detect_positive test_isinf_negativeZ x  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_negative/test_data_set_0/000077500000000000000000000000001511334557700303505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_negative/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700322440ustar00rootroot00000000000000BxJš™ŲŋĀ€fffĀ€˙€onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_negative/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700324500ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_positive/000077500000000000000000000000001511334557700253465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_positive/model.onnx000066400000000000000000000001661511334557700273550ustar00rootroot00000000000000  backend-test:^ % xy"IsInf* detect_negative test_isinf_positiveZ x  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_positive/test_data_set_0/000077500000000000000000000000001511334557700304105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_positive/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700323040ustar00rootroot00000000000000BxJš™ŲŋĀ€fff@€˙€onnx-onnx-bca0315/onnx/backend/test/data/node/test_isinf_positive/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700325100ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan/000077500000000000000000000000001511334557700234245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan/model.onnx000066400000000000000000000001251511334557700254260ustar00rootroot00000000000000  backend-test:= xy"IsNaN test_isnanZ x  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan/test_data_set_0/000077500000000000000000000000001511334557700264665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700303620ustar00rootroot00000000000000BxJš™™ŋĀ€333@€˙€onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700305660ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan_float16/000077500000000000000000000000001511334557700247605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan_float16/model.onnx000066400000000000000000000001351511334557700267630ustar00rootroot00000000000000  backend-test:E xy"IsNaNtest_isnan_float16Z x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan_float16/test_data_set_0/000077500000000000000000000000001511334557700300225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan_float16/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700317200ustar00rootroot00000000000000 BxJ Íŧ~|šAü|onnx-onnx-bca0315/onnx/backend/test/data/node/test_isnan_float16/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700321220ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_0/000077500000000000000000000000001511334557700267025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_0/model.onnx000066400000000000000000000002071511334557700307050ustar00rootroot00000000000000  backend-test:o . xy"LpNormalization* axis * p test_l1normalization_axis_0Z x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_0/test_data_set_0/000077500000000000000000000000001511334557700317445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_0/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700336360ustar00rootroot00000000000000BxJ@@€@output_0.pb000066400000000000000000000000211511334557700337600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_0/test_data_set_0ByJˇmÛ>%I?onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_1/000077500000000000000000000000001511334557700267035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_1/model.onnx000066400000000000000000000002171511334557700307070ustar00rootroot00000000000000  backend-test:w . xy"LpNormalization* axis * p test_l1normalization_axis_1Z x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_1/test_data_set_0/000077500000000000000000000000001511334557700317455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_1/test_data_set_0/input_0.pb000066400000000000000000000000331511334557700336420ustar00rootroot00000000000000BxJ@@€@Ā@Aoutput_0.pb000066400000000000000000000000331511334557700337640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_1/test_data_set_0ByJˇmÛ>%I?ˇmÛ>%I?onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_last/000077500000000000000000000000001511334557700275065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_last/model.onnx000066400000000000000000000002441511334557700315120ustar00rootroot00000000000000  backend-test:‹ 7 xy"LpNormalization* axis˙˙˙˙˙˙˙˙˙ * p test_l1normalization_axis_lastZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_last/test_data_set_0/000077500000000000000000000000001511334557700325505ustar00rootroot00000000000000input_0.pb000066400000000000000000000000751511334557700343740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_last/test_data_set_0BxJ0€?@@@@€@ @ @Ā@Aoutput_0.pb000066400000000000000000000000751511334557700345750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l1normalization_axis_last/test_data_set_0ByJ0ÍĖL>ÍĖĖ>ÍĖĖ>ˇmÛ>%I???ˇmÛ>%I?onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_0/000077500000000000000000000000001511334557700267035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_0/model.onnx000066400000000000000000000002271511334557700307100ustar00rootroot00000000000000  backend-test: . xy"LpNormalization* axis * p test_l2normalization_axis_0Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_0/test_data_set_0/000077500000000000000000000000001511334557700317455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_0/test_data_set_0/input_0.pb000066400000000000000000000000751511334557700336500ustar00rootroot00000000000000BxJ0€?@@@@€@ @ @Ā@Aoutput_0.pb000066400000000000000000000000751511334557700337720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_0/test_data_set_0ByJ0€?ë&ž>ë&ž>.ųä>.ųä>Ā˙ϰm?ϰm?.ųd?.ųd?Ā˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_1/000077500000000000000000000000001511334557700267045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_1/model.onnx000066400000000000000000000002171511334557700307100ustar00rootroot00000000000000  backend-test:w . xy"LpNormalization* axis * p test_l2normalization_axis_1Z x   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_1/test_data_set_0/000077500000000000000000000000001511334557700317465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_1/test_data_set_0/input_0.pb000066400000000000000000000000331511334557700336430ustar00rootroot00000000000000BxJ@@€@Ā@Aoutput_0.pb000066400000000000000000000000331511334557700337650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_l2normalization_axis_1/test_data_set_0ByJš™?ÍĖL?š™?ÍĖL?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/000077500000000000000000000000001511334557700300675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/model.onnx000066400000000000000000000004051511334557700320720ustar00rootroot00000000000000 backend-test:ė > X W BYMean InvStdDev"LayerNormalization* axis !test_layer_normalization_2d_axis0Z X   Z W   Z B   b Y   b Mean   b InvStdDev   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0/000077500000000000000000000000001511334557700331315ustar00rootroot00000000000000input_0.pb000066400000000000000000000000731511334557700347530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000731511334557700347540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0BWJ0^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋinput_2.pb000066400000000000000000000000731511334557700347550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0BBJ0ŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >output_0.pb000066400000000000000000000000731511334557700351540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0BYJ0ēâF@”Āŋ h >^‡´>V@./í?´Íd><üš?‚åģ?œĀ@öiŋWļŌžoutput_1.pb000066400000000000000000000000221511334557700351470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0BMeanJ؊??output_2.pb000066400000000000000000000000271511334557700351550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0/test_data_set_0B InvStdDevJ¸Š?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/000077500000000000000000000000001511334557700317375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/model.onnx000066400000000000000000000136401511334557700337470ustar00rootroot00000000000000 backend-test:‡/ wSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShapeKLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShape MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Rank MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1DULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_X2DILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU"Cast* to : ĩ ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SquareSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Var NLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_EpsilonULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Deviation MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_StdDevQLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scaled"Mul: g 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SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_2d_axis0_expandedZ X   Z W   Z B   b Y   b Mean   b InvStdDev   B test_data_set_0/000077500000000000000000000000001511334557700347225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expandedinput_0.pb000066400000000000000000000000731511334557700366230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000731511334557700366240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0BWJ0^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋinput_2.pb000066400000000000000000000000731511334557700366250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0BBJ0ŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >output_0.pb000066400000000000000000000000731511334557700370240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0BYJ0ēâF@”Āŋ h >^‡´>V@./í?´Íd><üš?‚åģ?œĀ@öiŋWļŌžoutput_1.pb000066400000000000000000000000221511334557700370170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0BMeanJ؊??output_2.pb000066400000000000000000000000271511334557700370250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded/test_data_set_0B InvStdDevJ¸Š?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/000077500000000000000000000000001511334557700327645ustar00rootroot00000000000000model.onnx000066400000000000000000000142341511334557700347150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18 backend-test:ƒ1 wSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShapeKLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShape MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Rank MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axis1DULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_X2DILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU"Cast* to : pMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axes_1"Constant* value*: : ÷ ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axes_1MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2D" ReduceMean: ė ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Square"Mul:  MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Square MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Axes_1SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_MeanOfSquare" ReduceMean: ú MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Var NLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_EpsilonULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_2d_axis0_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XU MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Deviation MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_StdDevQLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Scaled JLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_B2DMLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Biased MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_StdDevRLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_2d_axis0_expanded_function_ReducedShape InvStdDev"Reshape:0test_layer_normalization_2d_axis0_expanded_ver18Z X   Z W   Z B   b Y   b Mean   b InvStdDev   B test_data_set_0/000077500000000000000000000000001511334557700357475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18input_0.pb000066400000000000000000000000731511334557700376500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000731511334557700376510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0BWJ0^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋinput_2.pb000066400000000000000000000000731511334557700376520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0BBJ0ŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >output_0.pb000066400000000000000000000000731511334557700400510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0BYJ0ēâF@”Āŋ h >^‡´>V@./í?´Íd><üš?‚åģ?œĀ@öiŋWļŌžoutput_1.pb000066400000000000000000000000221511334557700400440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0BMeanJ؊??output_2.pb000066400000000000000000000000271511334557700400520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis0_expanded_ver18/test_data_set_0B InvStdDevJ¸Š?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1/000077500000000000000000000000001511334557700300705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1/model.onnx000066400000000000000000000003751511334557700321010ustar00rootroot00000000000000 backend-test:ä > X W BYMean InvStdDev"LayerNormalization* axis !test_layer_normalization_2d_axis1Z X   Z W  Z B  b Y   b Mean   b InvStdDev   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1/test_data_set_0/000077500000000000000000000000001511334557700331325ustar00rootroot00000000000000input_0.pb000066400000000000000000000000731511334557700347540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700351570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded/000077500000000000000000000000001511334557700317405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded/model.onnx000066400000000000000000000136301511334557700337470ustar00rootroot00000000000000 backend-test:˙. wSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShapeKLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShape MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Rank MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1DULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_X2DILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU"Cast* to : ĩ ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SquareSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Var 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QLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scaled JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_B2DMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Biased MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_StdDevRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_2d_axis1_expandedZ X   Z W  Z B  b Y   b Mean   b InvStdDev   B 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700370270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded_ver18/000077500000000000000000000000001511334557700327655ustar00rootroot00000000000000model.onnx000066400000000000000000000142241511334557700347150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded_ver18 backend-test:û0 wSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShapeKLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShape MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Rank MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axis1DULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_X2DILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU"Cast* to : pMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axes_1"Constant* value*: : ÷ ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axes_1MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D" ReduceMean: ė ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XUMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Square"Mul:  MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Square MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Axes_1SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_MeanOfSquare" ReduceMean: ú MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Var NLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_EpsilonULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_2d_axis1_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XU MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Deviation MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_StdDevQLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Scaled JLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_B2DMLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Biased MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_StdDevRLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_2d_axis1_expanded_function_ReducedShape InvStdDev"Reshape:0test_layer_normalization_2d_axis1_expanded_ver18Z X   Z W  Z B  b Y   b Mean   b InvStdDev   B test_data_set_0/000077500000000000000000000000001511334557700357505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded_ver18input_0.pb000066400000000000000000000000731511334557700376510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded_ver18/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700400540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded_ver18/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1/000077500000000000000000000000001511334557700321115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1/model.onnx000066400000000000000000000004201511334557700341110ustar00rootroot00000000000000 backend-test:÷ G X W BYMean InvStdDev"LayerNormalization* axis˙˙˙˙˙˙˙˙˙ +test_layer_normalization_2d_axis_negative_1Z X   Z W  Z B  b Y   b Mean   b InvStdDev   B test_data_set_0/000077500000000000000000000000001511334557700350745ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1input_0.pb000066400000000000000000000000731511334557700367750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700372000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded/000077500000000000000000000000001511334557700337615ustar00rootroot00000000000000model.onnx000066400000000000000000000147511511334557700357160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded backend-test:Đ3 ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ė ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_FloatEpsilonXLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Epsilon"Cast* to : e XWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShape"Shape: ¸ WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShapeULayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Rank"Size: zWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Zero1D"Constant* value*: : ƒWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ō WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShape WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Zero1D WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axis1D\LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_PrefixShape"Slice: Á 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]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_MeanOfSquare ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_SquareOfMeanTLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Var"Sub: ˜ TLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Var XLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Epsilon_LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_VarPlusEpsilon"Add:  _LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_VarPlusEpsilonWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_StdDev"Sqrt: ‘ SLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XU 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XLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Scale2DWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Scaled"Mul: q BTLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_B2D"Flatten* axis :  WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Scaled TLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_B2DWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Biased"Add: Ā WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Biased WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShapeY"Reshape: Å WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_StdDev\LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_InvStdDev2D" Reciprocal: É 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700410500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?test_layer_normalization_2d_axis_negative_1_expanded_ver18/000077500000000000000000000000001511334557700347275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000154031511334557700367360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded_ver18 backend-test:ę5 ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ė ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_FloatEpsilonXLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Epsilon"Cast* to : e XWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShape"Shape: ¸ WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShapeULayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Rank"Size: zWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Zero1D"Constant* value*: : ƒWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ō WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XShape WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Zero1D WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axis1D\LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_PrefixShape"Slice: Á WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axis1D_LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_NumReducedAxes"Neg: į _LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_NumReducedAxes\LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_SuffixShape"ConstantOfShape* value*: : ˛ \LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_PrefixShape \LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_SuffixShape]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_ReducedShape"Concat* axis : z XTLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_X2D"Flatten* axis˙˙˙˙˙˙˙˙˙ : ž TLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_X2DSLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XU"Cast* to : zWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axes_1"Constant* value*: : • SLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XU WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axes_1WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Mean2D" ReduceMean: Š SLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XU SLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_XUWLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Square"Mul: Ÿ WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Square WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Axes_1]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_MeanOfSquare" ReduceMean: ˜ WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Mean2D WLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Mean2D]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_SquareOfMean"Mul: › ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_MeanOfSquare ]LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_SquareOfMeanTLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Var"Sub: ˜ TLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Var XLayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_Epsilon_LayerNormalization_test_layer_normalization_2d_axis_negative_1_expanded_function_VarPlusEpsilon"Add: 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test_data_set_0/000077500000000000000000000000001511334557700377715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded_ver18input_0.pb000066400000000000000000000000731511334557700416720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded_ver18/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ 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lHŦ?†1Ø>Ūíķ>output_2.pb000066400000000000000000000000371511334557700420750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_1_expanded_ver18/test_data_set_0B InvStdDevJ €É´?ÃLm?•Ņ×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2/000077500000000000000000000000001511334557700321125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2/model.onnx000066400000000000000000000004301511334557700341130ustar00rootroot00000000000000 backend-test:˙ G X W BYMean InvStdDev"LayerNormalization* axisū˙˙˙˙˙˙˙˙ +test_layer_normalization_2d_axis_negative_2Z X   Z W   Z B   b Y   b Mean   b InvStdDev   B 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Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000000731511334557700371770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2/test_data_set_0BYJ0Đׄža‘*ŋõ~ŋ$(ŅŊ^2äŋŊÅŧ?0Íž›ŧŋk ?Œîė>m>>Âm>output_1.pb000066400000000000000000000000221511334557700371720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2/test_data_set_0BMeanJ؊??output_2.pb000066400000000000000000000000271511334557700372000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2/test_data_set_0B InvStdDevJ¸Š?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2_expanded/000077500000000000000000000000001511334557700337625ustar00rootroot00000000000000model.onnx000066400000000000000000000147611511334557700357200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_2d_axis_negative_2_expanded backend-test:Ø3 ]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ė ]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_FloatEpsilonXLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Epsilon"Cast* to : e XWLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XShape"Shape: ¸ WLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XShapeULayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Rank"Size: 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value*: : ˛ \LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_PrefixShape \LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_SuffixShape]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_ReducedShape"Concat* axis : z XTLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_X2D"Flatten* axisū˙˙˙˙˙˙˙˙ : ž TLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_X2DSLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XU"Cast* to : É SLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XUWLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Mean2D" ReduceMean* axes@ : Š SLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XU SLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_XUWLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Square"Mul: Ķ WLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Square]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_MeanOfSquare" ReduceMean* axes@ : ˜ WLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Mean2D WLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Mean2D]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_SquareOfMean"Mul: › ]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_MeanOfSquare ]LayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_SquareOfMeanTLayerNormalization_test_layer_normalization_2d_axis_negative_2_expanded_function_Var"Sub: ˜ 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InvStdDev    B test_data_set_0/000077500000000000000000000000001511334557700346055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsiloninput_0.pb000066400000000000000000000002051511334557700365030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000001071511334557700365050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon/test_data_set_0BWJŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> 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InvStdDevJ§>Ž?KĨI?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded/000077500000000000000000000000001511334557700334725ustar00rootroot00000000000000model.onnx000066400000000000000000000147001511334557700354210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded backend-test:§3 [LayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Č [LayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_FloatEpsilonVLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Epsilon"Cast* to : c XULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XShape"Shape: ´ ULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XShapeSLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Rank"Size: 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]LayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_NumReducedAxesZLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_SuffixShape"ConstantOfShape* value*: : Ŧ ZLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_PrefixShape ZLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_SuffixShape[LayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_ReducedShape"Concat* axis : o XRLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_X2D"Flatten* axis : ē RLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_X2DQLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XU"Cast* to : Å QLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XUULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Mean2D" ReduceMean* axes@ : „ 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XRLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_X2D"Flatten* axis : ē RLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_X2DQLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XU"Cast* to : xULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Axes_1"Constant* value*: :  QLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XU ULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Axes_1ULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Mean2D" ReduceMean: „ QLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XU QLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_XUULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Square"Mul: ™ 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YLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_NormalizedZLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_NormalizedT"Cast* to : s WVLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Scale2D"Flatten* axis : ’ ZLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_NormalizedT VLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Scale2DULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Scaled"Mul: o BRLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_B2D"Flatten* axis : ‰ ULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Scaled RLayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_B2DULayerNormalization_test_layer_normalization_3d_axis1_epsilon_expanded_function_Biased"Add: ŧ 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test_data_set_0/000077500000000000000000000000001511334557700375025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded_ver18input_0.pb000066400000000000000000000002051511334557700414000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded_ver18/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000001071511334557700414020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded_ver18/test_data_set_0BWJŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> 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ēžÁšŋoutput_1.pb000066400000000000000000000000301511334557700415760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded_ver18/test_data_set_0BMeanJÆß/?7yK>output_2.pb000066400000000000000000000000351511334557700416040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis1_epsilon_expanded_ver18/test_data_set_0B InvStdDevJ§>Ž?KĨI?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/000077500000000000000000000000001511334557700316235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/model.onnx000066400000000000000000000004501511334557700336260ustar00rootroot00000000000000 backend-test: Q X W BYMean InvStdDev"LayerNormalization* axis * epsilonÍĖĖ= )test_layer_normalization_3d_axis2_epsilonZ X    Z W  Z B  b Y    b Mean    b InvStdDev    B test_data_set_0/000077500000000000000000000000001511334557700346065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsiloninput_0.pb000066400000000000000000000002051511334557700365040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000000351511334557700365060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0BWJ|i?mjĸ>ĘLI?|Îîž;Įqŋinput_2.pb000066400000000000000000000000351511334557700365070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0BBJ ōŅžen‹ŧ- Â>…˜@Ö-Ŋoutput_0.pb000066400000000000000000000002051511334557700367050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0BYJxĀŧŧŧ+×īžüŠüŊj:á?‰ŋÁč×ŋ7;É>ßĖ<>úú@đ ŋŋ.ŒŋäOč>­?ž4(@Âĩ=>ēažŅđ>pĨ”ŧ)" @,ˇ•?îŋČØĩ= ļ=?­@0#ŖŋRâŋÍdĻŊXæ=õā?|â€ŋoutput_1.pb000066400000000000000000000000501511334557700367040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0BMeanJÄĸš?}Ķ<(Á?C]>“ĘÉ=А>output_2.pb000066400000000000000000000000551511334557700367120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon/test_data_set_0B InvStdDevJät­?Xŗ?ĄƒÚ?õq™?Ö?\?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon_expanded/000077500000000000000000000000001511334557700334735ustar00rootroot00000000000000model.onnx000066400000000000000000000146701511334557700354300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis2_epsilon_expanded backend-test:Ÿ3 [LayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Č [LayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_FloatEpsilonVLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Epsilon"Cast* to : c XULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XShape"Shape: ´ ULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XShapeSLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Rank"Size: 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]LayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_NumReducedAxesZLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_SuffixShape"ConstantOfShape* value*: : Ŧ ZLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_PrefixShape ZLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_SuffixShape[LayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_ReducedShape"Concat* axis : o XRLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_X2D"Flatten* axis : ē RLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_X2DQLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XU"Cast* to : Å QLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XUULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Mean2D" ReduceMean* axes@ : „ 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[LayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_FloatEpsilonVLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Epsilon"Cast* to : c XULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XShape"Shape: ´ ULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XShapeSLayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Rank"Size: xULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Zero1D"Constant* value*: : xULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Axis1D"Constant* value*: : ę ULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_XShape ULayerNormalization_test_layer_normalization_3d_axis2_epsilon_expanded_function_Zero1D 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InvStdDevJät­?Xŗ?ĄƒÚ?õq™?Ö?\?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon/000077500000000000000000000000001511334557700336435ustar00rootroot00000000000000model.onnx000066400000000000000000000004731511334557700355740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon backend-test:ĸ Z X W BYMean InvStdDev"LayerNormalization* axis˙˙˙˙˙˙˙˙˙ * epsilonÍĖĖ= 3test_layer_normalization_3d_axis_negative_1_epsilonZ X    Z W  Z B  b Y    b Mean    b InvStdDev    B test_data_set_0/000077500000000000000000000000001511334557700366265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsiloninput_0.pb000066400000000000000000000002051511334557700405240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ 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ŋڞŪ?úË?ĒW(=Āš<Â.WŋqãĀÁŦž?€ūg>ųwÖ>^ĸŊ]åįŋoutput_1.pb000066400000000000000000000000501511334557700407240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon/test_data_set_0BMeanJÄĸš?}Ķ<(Á?C]>“ĘÉ=А>output_2.pb000066400000000000000000000000551511334557700407320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon/test_data_set_0B InvStdDevJät­?Xŗ?ĄƒÚ?õq™?Ö?\?test_layer_normalization_3d_axis_negative_1_epsilon_expanded/000077500000000000000000000000001511334557700354345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000160031511334557700374400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_1_epsilon_expanded backend-test:ę7 ‰eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ü eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon`LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Epsilon"Cast* to : m X_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape"Shape: Č _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape]LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Rank"Size: ‚_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Zero1D"Constant* value*: : ‹_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ’ _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape 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dLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_SuffixShapeeLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_ReducedShape"Concat* axis : ‚ X\LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_X2D"Flatten* axis˙˙˙˙˙˙˙˙˙ : Î \LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_X2D[LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XU"Cast* to : Ų [LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XU_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Mean2D" ReduceMean* axes@ : ĸ [LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XU 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eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_SquareOfMean\LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Var"Sub: ° \LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Var `LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_EpsilongLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_VarPlusEpsilon"Add: Ō gLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_VarPlusEpsilon_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_StdDev"Sqrt: Š [LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XU 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`LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scale2D_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scaled"Mul: y B\LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_B2D"Flatten* axis : § _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scaled \LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_B2D_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Biased"Add: Đ _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Biased _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShapeY"Reshape: Õ 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‰eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ü eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon`LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Epsilon"Cast* to : m X_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape"Shape: Č _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape]LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Rank"Size: ‚_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Zero1D"Constant* value*: : ‹_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ’ _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape 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eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_MeanOfSquare eLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_SquareOfMean\LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Var"Sub: ° \LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Var `LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_EpsilongLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_VarPlusEpsilon"Add: Ō gLayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_VarPlusEpsilon_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_StdDev"Sqrt: Š [LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XU 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`LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scale2D_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scaled"Mul: y B\LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_B2D"Flatten* axis : § _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Scaled \LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_B2D_LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Biased"Add: Đ _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_Biased _LayerNormalization_test_layer_normalization_3d_axis_negative_1_epsilon_expanded_function_XShapeY"Reshape: Õ 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Ŋ„•ŋvõ?input_2.pb000066400000000000000000000001071511334557700405300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_2_epsilon/test_data_set_0BBJ<׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊoutput_0.pb000066400000000000000000000002051511334557700407260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_2_epsilon/test_data_set_0BYJxāaŋ3äš?Œf?†ŒÖŋĪ @3&@áīŋ‰†?<§?˜&*>mcÃ=ŋmÁ?qJužc3Vžk‘!ŋŖƒ?įAû?LkQ?͚F@v–"ŋ˙A?š?ķJ@Āˆ=ÔjgŊá9ŋŒ‘rŋžåŲ>output_1.pb000066400000000000000000000000301511334557700407230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_2_epsilon/test_data_set_0BMeanJÆß/?7yK>output_2.pb000066400000000000000000000000351511334557700407310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_2_epsilon/test_data_set_0B InvStdDevJ§>Ž?KĨI?test_layer_normalization_3d_axis_negative_2_epsilon_expanded/000077500000000000000000000000001511334557700354355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000160131511334557700374420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_2_epsilon_expanded backend-test:ō7 ‰eLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ü eLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_FloatEpsilon`LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Epsilon"Cast* to : m X_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_XShape"Shape: Č 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_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Axis1DgLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_NumReducedAxes"Neg: ÷ gLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_NumReducedAxesdLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_SuffixShape"ConstantOfShape* value*: : Ę dLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_PrefixShape dLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_SuffixShapeeLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_ReducedShape"Concat* axis : ‚ X\LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_X2D"Flatten* axisū˙˙˙˙˙˙˙˙ : Î 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‰eLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ü eLayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_FloatEpsilon`LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Epsilon"Cast* to : m X_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_XShape"Shape: Č _LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_XShape]LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Rank"Size: ‚_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Zero1D"Constant* value*: : ‹_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Axis1D"Constant* value*: ū˙˙˙˙˙˙˙˙ : ’ _LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_XShape 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`LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Scale2D_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Scaled"Mul: y B\LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_B2D"Flatten* axis : § _LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Scaled \LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_B2D_LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Biased"Add: Đ _LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_Biased _LayerNormalization_test_layer_normalization_3d_axis_negative_2_epsilon_expanded_function_XShapeY"Reshape: Õ 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‰eLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ü eLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_FloatEpsilon`LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Epsilon"Cast* to : m X_LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_XShape"Shape: Č _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_XShape]LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Rank"Size: ‚_LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Zero1D"Constant* value*: : ‹_LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Axis1D"Constant* value*: ũ˙˙˙˙˙˙˙˙ : ’ _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_XShape _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Zero1D _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Axis1DdLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_PrefixShape"Slice: Ņ _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Axis1DgLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_NumReducedAxes"Neg: ÷ gLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_NumReducedAxesdLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_SuffixShape"ConstantOfShape* value*: : Ę dLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_PrefixShape 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_LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Mean2DbLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Deviation"Sub: ą bLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Deviation _LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_StdDevcLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Normalized"Div: Ū cLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_NormalizeddLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_NormalizedT"Cast* to : } W`LayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_Scale2D"Flatten* axis : ° dLayerNormalization_test_layer_normalization_3d_axis_negative_3_epsilon_expanded_function_NormalizedT 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FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_2.pb000066400000000000000000000002051511334557700424000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_3_epsilon_expanded/test_data_set_0BBJxŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžoutput_0.pb000066400000000000000000000002051511334557700425770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_3d_axis_negative_3_epsilon_expanded/test_data_set_0BYJxėųÄŊ°Ļ’ŋüeË>ԙ8@`÷yž …ŋaÜžbh?Ō]?ÚēĘ>tĸŋ‚+ŋ9„ŋĒFzž| 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ILayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_SquareSLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_MeanOfSquare 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MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_4d_axis0_expandedZ X     Z W     Z B     b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700347245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expandedinput_0.pb000066400000000000000000000007601511334557700366300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000007601511334557700366310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded/test_data_set_0BWJāŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžA‹ŊĐNÛ?A¨>ŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> s?Nŗ=ۜŋ,(X?€ŋģÅŋ\˜?MFĸ>gŊk?E0Ŗ>@Y[?Š&ŋb„ŋú|.?AŦMŋV†0ŋŽ;éžt0<ą>ĩžgū¯ŋ-Ä$ŋ=LĀ+ ?:Íŋo\ŋ ĢU=T=ŋÅ?V|Ĩŋäēˆ>į Ŋ„•ŋvõ?׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊ|i?mjĸ>ĘLI?|Îîž;Įqŋ ōŅžen‹ŧ- Â>…˜@Ö-Ŋиtŋ†$ąžt\ížŊ„ö>Ö8Åŋ=@C >ŪĀm>ĩéŋÁĄsžĄGļŋl”üžøø ŋƒÕ>Éũ“ŋ™üG?EKŋ?ĸzĀ–>Ú>ØI-?input_2.pb000066400000000000000000000007601511334557700366320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded/test_data_set_0BBJā/#ŋ6gËžŲž 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7Ām$Ã>ŨāŋÄ|†?ĖB2ŋGĶ?&Xŋ0+=°†Ŋ“TŋôŊ?Ö.žšäž¤íŊ`Ēãž`a/=ī1HĀäÃæŋāģđ?€M”<ô$Vž€¨ŧfŸ-ŋĐŊģ= ‘žŨ?output_1.pb000066400000000000000000000000261511334557700370250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded/test_data_set_0BMeanJ`Ë>output_2.pb000066400000000000000000000000331511334557700370240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded/test_data_set_0B InvStdDevJķīt?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/000077500000000000000000000000001511334557700327665ustar00rootroot00000000000000model.onnx000066400000000000000000000143141511334557700347160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18 backend-test:ŗ1 wSLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis0_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis0_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis0_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis0_expanded_function_XU"Cast* to : 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FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000007601511334557700376560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/test_data_set_0BWJāŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋĖ9žrĖŪž­´ė?‚,?֞Đ>8Eŋ< ?Ą,ŋÂ`=ĪÆ"ŋģ*-?u›?ELUždÁĘ>qé‹ŋ‡ážŋķ÷ā>uŦ*>l‘"?r…@hÉq?¸ŽiŋdúŽ?§o¨ŋŅTėžA‹ŊĐNÛ?A¨>ŋz‘SŋzĄÉŊˇŲ)ŋ›5?2;ŠŋAā’ŋķ)āžūūžčúö?> s?Nŗ=ۜŋ,(X?€ŋģÅŋ\˜?MFĸ>gŊk?E0Ŗ>@Y[?Š&ŋb„ŋú|.?AŦMŋV†0ŋŽ;éžt0<ą>ĩžgū¯ŋ-Ä$ŋ=LĀ+ ?:Íŋo\ŋ ĢU=T=ŋÅ?V|Ĩŋäēˆ>į Ŋ„•ŋvõ?׊/ž”E?+ŅR?ur @YĢ?gŊžŅužĨÁŒ?]ŋ'?Šß#?jøÎŋ•GĮŧ˜ī<ŋGR>ÉŊ|i?mjĸ>ĘLI?|Îîž;Įqŋ ōŅžen‹ŧ- Â>…˜@Ö-Ŋиtŋ†$ąžt\ížŊ„ö>Ö8Åŋ=@C >ŪĀm>ĩéŋÁĄsžĄGļŋl”üžøø ŋƒÕ>Éũ“ŋ™üG?EKŋ?ĸzĀ–>Ú>ØI-?input_2.pb000066400000000000000000000007601511334557700376570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/test_data_set_0BBJā/#ŋ6gËžŲž x˜žķ6žžK‡Öŋš“?ņ0Š?¤8PŋËŗģŋ‚d?×fŋ,\>Ŗž¯1?Û1?ĀĀ9ŋąŋēĘŋĶA?Š,˜ŋˇžŋ ¨ŋÎPWŊØ÷ŋ-OA>š?¤ĩ=y,ŸžŊyĮ=ÍOĖ>)r1ĀV[ú?PēĮ>@'ŋ +ČžšËü>įĮíŊŧö§ @$câŊ•‚?.*1ŋ¨Ä?¤›’>1Ũ?ŨʅŋĪ›?ė—0?æĸĻ?YĘ ŋ0Iöž_s@™Ž‡ŋf6 ž¨…‘?$Č=t<?•„Ėž÷wŊ>F<§ŋ =Ô?ū˙ņŊ) .ŋ˜*?wãëžûČĒŋ=aŦŋ›1?8g#žéž‚ķ‰?Ô;ŋ˛ ;ŋ÷Åžh;Á=ųģ,ŊáⒾ÷k|ŊŪÂÛŊū78ŋO PŋhŒ>dŋ8$”ŋÅ䟾vs!ž(n@=g4ŋ‰yq?ŧG??Y/˜ŋčķE?g‰—ŋá/*ĀÂ7?Áāŋâāæ>W/ŋ)lÔ?ęĈ?/"čž 0ŋãf›ŋœĀážÁŠžšēžúv >ü?éŗ>đžCŋ ¸ŋû¨Ž?ž0ŋˇü&ŋĒlŋ´éëŋšôžr•õžÍĪ?output_0.pb000066400000000000000000000007601511334557700400560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/test_data_set_0BYJāP9LŊSŋ-ŋœUŪ=>-@"ģŋm0ÂŋŠŨO?đÁ?5wŋ–Ô­ŋž<? TŲ=Ę醾ŦĒŖž?(+S?€ÎĐ<=t¨ŋBŨÁŋ7Ō?Ü&)@JX“žYõžÅ…ŋ˛ķ;@fŸŋūˆ?)¸žl¸Āûdüž§Ë>¯Ā’ļ+@ë@ÍmŋâĪžn§Ö?¯XœŋwÜēŋ–É@w“į>ÁPėŋÔ2°&Ø?” …?Y>đĻ>(„>kĐĨŋ6;™?ŽVÄŋņ”Ξlbã?tĐwž0ÂŊ<5Ē?”>@ņ>Ŧ~…Ŋ$:š>AU„ŋĖŽ@´°í>đQ@u1?ŋˇ>:G?@EĒŋ´ũļ? ­Žž(_ŋ Չ?É•ŋ/L?ōÔž‡j> {Hŋ&ķXŋļ{ŋúûQžĪģ…ž:+}ŋŒû?(ö÷ŋbu§ž“B†b:žŽ@’tƒŋ&P[?›Ē>ą[ŋŒ†T?ĀŋĮŋ˜ 7Ām$Ã>ŨāŋÄ|†?ĖB2ŋGĶ?&Xŋ0+=°†Ŋ“TŋôŊ?Ö.žšäž¤íŊ`Ēãž`a/=ī1HĀäÃæŋāģđ?€M”<ô$Vž€¨ŧfŸ-ŋĐŊģ= ‘žŨ?output_1.pb000066400000000000000000000000261511334557700400520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/test_data_set_0BMeanJ`Ë>output_2.pb000066400000000000000000000000331511334557700400510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis0_expanded_ver18/test_data_set_0B InvStdDevJķīt?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/000077500000000000000000000000001511334557700300725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/model.onnx000066400000000000000000000004551511334557700321020ustar00rootroot00000000000000 backend-test:” > X W BYMean InvStdDev"LayerNormalization* axis !test_layer_normalization_4d_axis1Z X     Z W    Z B    b Y     b Mean     b# InvStdDev     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/test_data_set_0/000077500000000000000000000000001511334557700331345ustar00rootroot00000000000000input_0.pb000066400000000000000000000007601511334557700347610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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ŋoutput_0.pb000066400000000000000000000007601511334557700351620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/test_data_set_0BYJā ­YĀa^‘ŋöÜŗžˆ?ģ4Ā8"€ŊFsęŊüŪˇ?G…Ā˃>Û(ŋ2eĀöĮŋÉī_ŋĘú1>œ€û> ž#ŋ6ëm?ˆÛÚ?õÜ?ŽDĀ`j?‚ĩ?ëŒ?ņcˇŋiˆŋį!@#¸Ā22įŊP@@ĸC>ēsj?›öŋ´Š‡ŋĒŗž)?ž8ŦŋEÅŋ^%žú|?{ĢŦŋ˛IĀ%Ø!Ā(Ŋ*ŋšh?åī@p_ĩž×ÄŠŊžH@āvYž8Ôwŋœ6:ÖC_ŋ]Fhŋn§?ŪXD=tpMŋ<Į>ŋęžĸ?āW$žÜÎģ>ž,žŋ˯?Äąž<ŠŨžÆ×žžŅU0>ÎĮ"?Ž!ĀīÆÚ>¸4ģŋ€įŋÂZąŋTÖ Ā›ĒD>€jH=Íŧ•ŋ[QE?ËL??(ÎŊŋÄ \?ōä1?đü¯?ZȐŋ%ė›ĀÉ@ūt Āé[=K@bû9?!ŊT?N)ŋŋķYŋ`ĨĨ?ž?ž–ÔÍžHO>ß§Ž?… ĖŋŦ`PĀ{ĀÚŧŦ>´ĩė?ī8Î?ÂĸĒ>tü{ @€¤qž­Ũpŋž‚ŋqÄ? ?8+>SZŋpœŋŧKD^?Ī+ŋoutput_1.pb000066400000000000000000000000321511334557700351530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/test_data_set_0BMeanJž=áA>output_2.pb000066400000000000000000000000371511334557700351610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1/test_data_set_0B InvStdDevJm‹q?„6y?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1_expanded/000077500000000000000000000000001511334557700317425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1_expanded/model.onnx000066400000000000000000000137101511334557700337500ustar00rootroot00000000000000 backend-test:¯/ wSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XU"Cast* to : ĩ ILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SquareSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_VarPlusEpsilon"Add: Ž 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RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_4d_axis1_expandedZ X     Z W    Z B    b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700347255ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1_expandedinput_0.pb000066400000000000000000000007601511334557700366310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1_expanded/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō 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ŋoutput_0.pb000066400000000000000000000007601511334557700370320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis1_expanded/test_data_set_0BYJā ­YĀa^‘ŋöÜŗžˆ?ģ4Ā8"€ŊFsęŊüŪˇ?G…Ā˃>Û(ŋ2eĀöĮŋÉī_ŋĘú1>œ€û> ž#ŋ6ëm?ˆÛÚ?õÜ?ŽDĀ`j?‚ĩ?ëŒ?ņcˇŋiˆŋį!@#¸Ā22įŊP@@ĸC>ēsj?›öŋ´Š‡ŋĒŗž)?ž8ŦŋEÅŋ^%žú|?{ĢŦŋ˛IĀ%Ø!Ā(Ŋ*ŋšh?åī@p_ĩž×ÄŠŊžH@āvYž8Ôwŋœ6:ÖC_ŋ]Fhŋn§?ŪXD=tpMŋ<Į>ŋęžĸ?āW$žÜÎģ>ž,žŋ˯?Äąž<ŠŨžÆ×žžŅU0>ÎĮ"?Ž!ĀīÆÚ>¸4ģŋ€įŋÂZąŋTÖ Ā›ĒD>€jH=Íŧ•ŋ[QE?ËL??(ÎŊŋÄ \?ōä1?đü¯?ZȐŋ%ė›ĀÉ@ūt Āé[=K@bû9?!ŊT?N)ŋŋķYŋ`ĨĨ?ž?ž–ÔÍžHO>ß§Ž?… ĖŋŦ`PĀ{ĀÚŧŦ>´ĩė?ī8Î?ÂĸĒ>tü{ @€¤qž­Ũpŋž‚ŋqÄ? 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SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XU"Cast* to : 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MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_4d_axis1_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_4d_axis1_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Deviation MLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis1_expanded_function_Scaled"Mul: g 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Eˇŋŗ¯š@†2>pî€ŋrZŋ ąŌžĪ¨ž Z_?,r#@ōiÔŋš&Hž.ŒĒ>g‰>PHŧŋØ‹ŋ,@—ŋa”I>ĄŠåŋc†?Žq4>ø”ŋ;Ģxƒ?ˇy€ŋĩļŋ3¤“žƒNŋN8b?gą-ĀŽfąŋZrŋĘ>?Ö¸žč ]ŋå~o?ęÛ;ŋ\¯?X€“ŧč<Š=*cŒĀ–u1?+OŠŋg ?R((?øg-žėQc?E”W@€īĪŋ‚ĸ%ž~Â×ŋ!tE?Íø˜ŋ$˜?x!ēŋxqa>~ēåŋÔˏ?Ŗ]D?7Ŋŋ•z?€rēžÎTĄŋÆä6ŋpˇd?bƒĪd? TĀŋ‡…ŋž.>ė"Ā&Ÿą?aŊ–ŋ+^ŋrÕ ŋ |ģztäŋoutput_1.pb000066400000000000000000000000521511334557700351560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2/test_data_set_0BMeanJęŋ?~Zd=SHĘžÄO´žģ¨×>XFū>output_2.pb000066400000000000000000000000571511334557700351640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2/test_data_set_0B InvStdDevJŽ–?…W?ĶՑ?ĸô­?į|‰?ģ'_?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/000077500000000000000000000000001511334557700317435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/model.onnx000066400000000000000000000137001511334557700337500ustar00rootroot00000000000000 backend-test:§/ wSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU"Cast* to : ĩ ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SquareSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Deviation MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_4d_axis2_expandedZ X     Z W   Z B   b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700347265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expandedinput_0.pb000066400000000000000000000007601511334557700366320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000001331511334557700366250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0BWJP/Uu>”VŊžøx?L‘@ąĐ>ĐEž2xA?™ ŋĩë?ŋwb=ŠL%ĀĨ´“ŋ(˛žŲ;­ŋĻ-„ŋzßž°LŌŋ¤čĪžw ŋ•Đ<input_2.pb000066400000000000000000000001331511334557700366260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0BBJPMŧ“?˙¤0>CŠŦ<ĮŽË=§Ųh>~$‚ŋSëŊĨž>u¯ŋn›]?ˆjŠ?ÛĄ!ŋA!wžŅ`ŋ™ 3?"ևŋŅcž-â[ŋoĩP=NŠåŋoutput_0.pb000066400000000000000000000007601511334557700370330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0BYJāUÔž?!˙{>#ú>|o‰@\Y?žR*ŋf e>8 D?ĐGŋû \?Tå@ęŋxŧŖž|Œ)ž)Z?rŽpŋ÷›Ā†^úžydY>ëŋæÄ ?ĐF_ŧĻä.?;ŋĒŋ#|?ėiEŋTøŊ‰Ö>dĀJšg?c]?âĐqŋ4=ĸš¸?vp†?/’Œŋ´lėŋÆ ŋQ€>>¤æŋ Îy?đĩ? Eˇŋŗ¯š@†2>pî€ŋrZŋ ąŌžĪ¨ž Z_?,r#@ōiÔŋš&Hž.ŒĒ>g‰>PHŧŋØ‹ŋ,@—ŋa”I>ĄŠåŋc†?Žq4>ø”ŋ;Ģxƒ?ˇy€ŋĩļŋ3¤“žƒNŋN8b?gą-ĀŽfąŋZrŋĘ>?Ö¸žč ]ŋå~o?ęÛ;ŋ\¯?X€“ŧč<Š=*cŒĀ–u1?+OŠŋg ?R((?øg-žėQc?E”W@€īĪŋ‚ĸ%ž~Â×ŋ!tE?Íø˜ŋ$˜?x!ēŋxqa>~ēåŋÔˏ?Ŗ]D?7Ŋŋ•z?€rēžÎTĄŋÆä6ŋpˇd?bƒĪd? TĀŋ‡…ŋž.>ė"Ā&Ÿą?aŊ–ŋ+^ŋrÕ ŋ |ģztäŋoutput_1.pb000066400000000000000000000000521511334557700370260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0BMeanJęŋ?~Zd=SHĘžÄO´žģ¨×>XFū>output_2.pb000066400000000000000000000000571511334557700370340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded/test_data_set_0B InvStdDevJŽ–?…W?ĶՑ?ĸô­?į|‰?ģ'_?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/000077500000000000000000000000001511334557700327705ustar00rootroot00000000000000model.onnx000066400000000000000000000142741511334557700347250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18 backend-test:Ŗ1 wSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU"Cast* to : pMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axes_1"Constant* value*: : ÷ ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axes_1MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D" ReduceMean: ė ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Square"Mul:  MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Square MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Axes_1SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_MeanOfSquare" ReduceMean: ú MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_4d_axis2_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Deviation MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis2_expanded_function_ReducedShape InvStdDev"Reshape:0test_layer_normalization_4d_axis2_expanded_ver18Z X     Z W   Z B   b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700357535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18input_0.pb000066400000000000000000000007601511334557700376570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000001331511334557700376520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0BWJP/Uu>”VŊžøx?L‘@ąĐ>ĐEž2xA?™ ŋĩë?ŋwb=ŠL%ĀĨ´“ŋ(˛žŲ;­ŋĻ-„ŋzßž°LŌŋ¤čĪžw ŋ•Đ<input_2.pb000066400000000000000000000001331511334557700376530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0BBJPMŧ“?˙¤0>CŠŦ<ĮŽË=§Ųh>~$‚ŋSëŊĨž>u¯ŋn›]?ˆjŠ?ÛĄ!ŋA!wžŅ`ŋ™ 3?"ևŋŅcž-â[ŋoĩP=NŠåŋoutput_0.pb000066400000000000000000000007601511334557700400600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0BYJāUÔž?!˙{>#ú>|o‰@\Y?žR*ŋf e>8 D?ĐGŋû \?Tå@ęŋxŧŖž|Œ)ž)Z?rŽpŋ÷›Ā†^úžydY>ëŋæÄ ?ĐF_ŧĻä.?;ŋĒŋ#|?ėiEŋTøŊ‰Ö>dĀJšg?c]?âĐqŋ4=ĸš¸?vp†?/’Œŋ´lėŋÆ ŋQ€>>¤æŋ Îy?đĩ? Eˇŋŗ¯š@†2>pî€ŋrZŋ ąŌžĪ¨ž Z_?,r#@ōiÔŋš&Hž.ŒĒ>g‰>PHŧŋØ‹ŋ,@—ŋa”I>ĄŠåŋc†?Žq4>ø”ŋ;Ģxƒ?ˇy€ŋĩļŋ3¤“žƒNŋN8b?gą-ĀŽfąŋZrŋĘ>?Ö¸žč ]ŋå~o?ęÛ;ŋ\¯?X€“ŧč<Š=*cŒĀ–u1?+OŠŋg ?R((?øg-žėQc?E”W@€īĪŋ‚ĸ%ž~Â×ŋ!tE?Íø˜ŋ$˜?x!ēŋxqa>~ēåŋÔˏ?Ŗ]D?7Ŋŋ•z?€rēžÎTĄŋÆä6ŋpˇd?bƒĪd? TĀŋ‡…ŋž.>ė"Ā&Ÿą?aŊ–ŋ+^ŋrÕ ŋ |ģztäŋoutput_1.pb000066400000000000000000000000521511334557700400530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0BMeanJęŋ?~Zd=SHĘžÄO´žģ¨×>XFū>output_2.pb000066400000000000000000000000571511334557700400610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis2_expanded_ver18/test_data_set_0B InvStdDevJŽ–?…W?ĶՑ?ĸô­?į|‰?ģ'_?onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3/000077500000000000000000000000001511334557700300745ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3/model.onnx000066400000000000000000000004351511334557700321020ustar00rootroot00000000000000 backend-test:„ > X W BYMean InvStdDev"LayerNormalization* axis !test_layer_normalization_4d_axis3Z X     Z W  Z B  b Y     b Mean     b# InvStdDev     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3/test_data_set_0/000077500000000000000000000000001511334557700331365ustar00rootroot00000000000000input_0.pb000066400000000000000000000007601511334557700347630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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Z?0e?ĄŪ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded/000077500000000000000000000000001511334557700317445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded/model.onnx000066400000000000000000000136701511334557700337570ustar00rootroot00000000000000 backend-test:Ÿ/ wSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Rank"Size: 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RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU"Cast* to : ĩ ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D" ReduceMean* axes@ : ė ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Square"Mul: ŋ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SquareSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_MeanOfSquare" ReduceMean* axes@ : ú MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Deviation MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShape InvStdDev"Reshape:*test_layer_normalization_4d_axis3_expandedZ X     Z W  Z B  b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700347275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expandedinput_0.pb000066400000000000000000000007601511334557700366330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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Ięŋ@‡,ŋFŲđ>åÅ?Zj@&;*?P“j>PŅģ=æwÃ?(Û8?k4æ>!šŋ؆:=Jđž?ĖŪ‰@‘ã°?Åt]ŋË= ?Ë+ģ? ¨@Œ4ŧŋÁp @đV>Ēyē?ė˛>nŗ¯ŋ8Šŋ‹Z ? ¸?ŌŽ?ÉdŅŋĀ–ę ? ~ŋ?Ō~?Üu[ŊųéŽ?ŋDˇ?ŧ§ã?"N:?„ũ2Āî}ˇ>S…Ā?output_1.pb000066400000000000000000000001621511334557700370310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded/test_data_set_0BMeanJ`Äĸš?}Ķ<(Á?C]>“ĘÉ=А>§c ŋ^Â>b÷ ŋôI ŋ~*䞥#ŊÆ-ŋ Føž—n>U¸ôžĻ>û;?5E@žËRV?Āü5?pN ?output_2.pb000066400000000000000000000001671511334557700370370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded/test_data_set_0B InvStdDevJ`”ųŋ?‰”Į?đĘ@ ŌĨ?æ°?éąd?Zŗ—?˛â´?°8C?O­˜?čEá?€v@gņĖ?ašŽ?’zž?eF@, Y?o„q?­˛Ũ?‡ûČ?]–=?ņ Z?0e?ĄŪ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/000077500000000000000000000000001511334557700327715ustar00rootroot00000000000000model.onnx000066400000000000000000000142641511334557700347250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18 backend-test:›1 wSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ¸ SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_FloatEpsilonNLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Epsilon"Cast* to : [ XMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShape"Shape: ¤ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShapeKLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Rank"Size: pMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Zero1D"Constant* value*: : pMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axis1D"Constant* value*: : Ę MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShape MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Zero1D MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axis1DRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_PrefixShape"Slice: ú KLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Rank MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axis1DULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NumReducedAxes"Sub: Ķ ULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NumReducedAxesRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SuffixShape"ConstantOfShape* value*: : ” RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_PrefixShape RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SuffixShapeSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShape"Concat* axis : g XJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_X2D"Flatten* axis : Ē JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_X2DILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU"Cast* to : pMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axes_1"Constant* value*: : ÷ ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axes_1MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D" ReduceMean: ė ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XUMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Square"Mul:  MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Square MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Axes_1SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_MeanOfSquare" ReduceMean: ú MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2DSLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SquareOfMean"Mul: ũ SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_MeanOfSquare SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_SquareOfMeanJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Var"Sub: ú JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Var NLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_EpsilonULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_VarPlusEpsilon"Add: Ž ULayerNormalization_test_layer_normalization_4d_axis3_expanded_function_VarPlusEpsilonMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDev"Sqrt: ķ ILayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XU MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2DPLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Deviation"Sub: û PLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Deviation MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDevQLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Normalized"Div: ē QLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedT"Cast* to : k WNLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scale2D"Flatten* axis : ú RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_NormalizedT NLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scale2DMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scaled"Mul: g BJLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_B2D"Flatten* axis : ņ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Scaled JLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_B2DMLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Biased"Add: Ŧ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Biased MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_XShapeY"Reshape: ą MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_StdDevRLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_InvStdDev2D" Reciprocal: ĩ MLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_Mean2D SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShapeMean"Reshape: ŋ RLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_InvStdDev2D SLayerNormalization_test_layer_normalization_4d_axis3_expanded_function_ReducedShape InvStdDev"Reshape:0test_layer_normalization_4d_axis3_expanded_ver18Z X     Z W  Z B  b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700357545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18input_0.pb000066400000000000000000000007601511334557700376600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?input_1.pb000066400000000000000000000000351511334557700376540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0BWJ^;ĻŋpVŖ?šŠ?ŗBR>rŪ8=input_2.pb000066400000000000000000000000351511334557700376550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0BBJjŧ@›ˆžFį„žIē>GTŧ?output_0.pb000066400000000000000000000007601511334557700400610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0BYJā`>Ũ?|QĀō+™ŋŖ?Üņŋ?Ũ‹@ˇČ?œ! ŋxĨ>´Ëŋ?^`@/’ü?ĤZ>ŌĢ/>­ē?e @Ûũę?ލ{ŋŽĘĮ>oQ´?GPŽ@ä >˛ š>‡„„>ģÄ?ÛM‹@ ŋ€gPŋ†?ũuÂ?É6Ŗ?eļ?î™Oŋ ÖU<%ŸŊ?˛ũ/@qé ?rčŖ?„Âļ?Ȋ5@C(ŋ¤1ˇŋĘkA?~ŧ?pÖ @6˛Ŧŋ ëę?ĐĶ=ĩŖž?t‰W@\ņË?@ļŌžÔ}Ę=ܒĀ?ėS?Đ?~=õō^?`w=ĖŽˇ?Sä@WnĀ>üi ŋ ž¤<Ī?Ä?ôM @~^Ā-ē?‚˙{>Ā?y^Å?‚ŦážH?Ā)ŧ]ŠŊ?|K@ Ięŋ@‡,ŋFŲđ>åÅ?Zj@&;*?P“j>PŅģ=æwÃ?(Û8?k4æ>!šŋ؆:=Jđž?ĖŪ‰@‘ã°?Åt]ŋË= ?Ë+ģ? ¨@Œ4ŧŋÁp @đV>Ēyē?ė˛>nŗ¯ŋ8Šŋ‹Z ? ¸?ŌŽ?ÉdŅŋĀ–ę ? ~ŋ?Ō~?Üu[ŊųéŽ?ŋDˇ?ŧ§ã?"N:?„ũ2Āî}ˇ>S…Ā?output_1.pb000066400000000000000000000001621511334557700400560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0BMeanJ`Äĸš?}Ķ<(Á?C]>“ĘÉ=А>§c ŋ^Â>b÷ ŋôI ŋ~*䞥#ŊÆ-ŋ Føž—n>U¸ôžĻ>û;?5E@žËRV?Āü5?pN ?output_2.pb000066400000000000000000000001671511334557700400640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis3_expanded_ver18/test_data_set_0B InvStdDevJ`”ųŋ?‰”Į?đĘ@ ŌĨ?æ°?éąd?Zŗ—?˛â´?°8C?O­˜?čEá?€v@gņĖ?ašŽ?’zž?eF@, Y?o„q?­˛Ũ?‡ûČ?]–=?ņ Z?0e?ĄŪ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_1/000077500000000000000000000000001511334557700321135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_1/model.onnx000066400000000000000000000004601511334557700341170ustar00rootroot00000000000000 backend-test:— G X W BYMean InvStdDev"LayerNormalization* axis˙˙˙˙˙˙˙˙˙ +test_layer_normalization_4d_axis_negative_1Z X     Z W  Z B  b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700350765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_1input_0.pb000066400000000000000000000007601511334557700370020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_1/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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value*: : ˛ \LayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_PrefixShape \LayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_SuffixShape]LayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_ReducedShape"Concat* axis : z XTLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_X2D"Flatten* axis˙˙˙˙˙˙˙˙˙ : ž TLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_X2DSLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_XU"Cast* to : É SLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_XUWLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_Mean2D" ReduceMean* axes@ : Š SLayerNormalization_test_layer_normalization_4d_axis_negative_1_expanded_function_XU 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WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Scaled TLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_B2DWLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Biased"Add: Ā WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Biased WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_XShapeY"Reshape: Å WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_StdDev\LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_InvStdDev2D" Reciprocal: É WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Mean2D ]LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_ReducedShapeMean"Reshape: Ķ \LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_InvStdDev2D ]LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_ReducedShape InvStdDev"Reshape:4test_layer_normalization_4d_axis_negative_2_expandedZ X     Z W   Z B   b Y     b Mean     b# InvStdDev     B test_data_set_0/000077500000000000000000000000001511334557700367475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_2_expandedinput_0.pb000066400000000000000000000007601511334557700406530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_2_expanded/test_data_set_0BXJāxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššž^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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InvStdDevJŽ–?…W?ĶՑ?ĸô­?į|‰?ģ'_?test_layer_normalization_4d_axis_negative_2_expanded_ver18/000077500000000000000000000000001511334557700347325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000154531511334557700367460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_2_expanded_ver18 backend-test:’6 ]LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ė ]LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_FloatEpsilonXLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Epsilon"Cast* to : e XWLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_XShape"Shape: ¸ WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_XShapeULayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Rank"Size: zWLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Zero1D"Constant* value*: : ƒWLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Axis1D"Constant* value*: ū˙˙˙˙˙˙˙˙ : ō WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_XShape WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Zero1D WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Axis1D\LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_PrefixShape"Slice: Á WLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Axis1D_LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_NumReducedAxes"Neg: į _LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_NumReducedAxes\LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_SuffixShape"ConstantOfShape* value*: : ˛ \LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_PrefixShape \LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_SuffixShape]LayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_ReducedShape"Concat* axis : z XTLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_X2D"Flatten* axisū˙˙˙˙˙˙˙˙ : ž TLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_X2DSLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_XU"Cast* to : zWLayerNormalization_test_layer_normalization_4d_axis_negative_2_expanded_function_Axes_1"Constant* value*: : • 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InvStdDevJm‹q?„6y?test_layer_normalization_4d_axis_negative_3_expanded_ver18/000077500000000000000000000000001511334557700347335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000154631511334557700367500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_4d_axis_negative_3_expanded_ver18 backend-test:š6 ]LayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ė ]LayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_FloatEpsilonXLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Epsilon"Cast* to : e XWLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_XShape"Shape: ¸ WLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_XShapeULayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Rank"Size: zWLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Zero1D"Constant* value*: : ƒWLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Axis1D"Constant* value*: ũ˙˙˙˙˙˙˙˙ : ō WLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_XShape WLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Zero1D WLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Axis1D\LayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_PrefixShape"Slice: Á WLayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_Axis1D_LayerNormalization_test_layer_normalization_4d_axis_negative_3_expanded_function_NumReducedAxes"Neg: į 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{WLayerNormalization_test_layer_normalization_default_axis_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ā WLayerNormalization_test_layer_normalization_default_axis_expanded_function_FloatEpsilonRLayerNormalization_test_layer_normalization_default_axis_expanded_function_Epsilon"Cast* to : _ XQLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShape"Shape: Ŧ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShapeOLayerNormalization_test_layer_normalization_default_axis_expanded_function_Rank"Size: tQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Zero1D"Constant* value*: : }QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ú QLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShape QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Zero1D QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1DVLayerNormalization_test_layer_normalization_default_axis_expanded_function_PrefixShape"Slice: ĩ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1DYLayerNormalization_test_layer_normalization_default_axis_expanded_function_NumReducedAxes"Neg: Û YLayerNormalization_test_layer_normalization_default_axis_expanded_function_NumReducedAxesVLayerNormalization_test_layer_normalization_default_axis_expanded_function_SuffixShape"ConstantOfShape* value*: :   VLayerNormalization_test_layer_normalization_default_axis_expanded_function_PrefixShape VLayerNormalization_test_layer_normalization_default_axis_expanded_function_SuffixShapeWLayerNormalization_test_layer_normalization_default_axis_expanded_function_ReducedShape"Concat* axis : t XNLayerNormalization_test_layer_normalization_default_axis_expanded_function_X2D"Flatten* axis˙˙˙˙˙˙˙˙˙ : ˛ NLayerNormalization_test_layer_normalization_default_axis_expanded_function_X2DMLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU"Cast* to : Ŋ MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XUQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2D" ReduceMean* axes@ : ø MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XUQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Square"Mul: Į QLayerNormalization_test_layer_normalization_default_axis_expanded_function_SquareWLayerNormalization_test_layer_normalization_default_axis_expanded_function_MeanOfSquare" ReduceMean* axes@ : † QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2D QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2DWLayerNormalization_test_layer_normalization_default_axis_expanded_function_SquareOfMean"Mul: ‰ WLayerNormalization_test_layer_normalization_default_axis_expanded_function_MeanOfSquare WLayerNormalization_test_layer_normalization_default_axis_expanded_function_SquareOfMeanNLayerNormalization_test_layer_normalization_default_axis_expanded_function_Var"Sub: † NLayerNormalization_test_layer_normalization_default_axis_expanded_function_Var RLayerNormalization_test_layer_normalization_default_axis_expanded_function_EpsilonYLayerNormalization_test_layer_normalization_default_axis_expanded_function_VarPlusEpsilon"Add: ļ YLayerNormalization_test_layer_normalization_default_axis_expanded_function_VarPlusEpsilonQLayerNormalization_test_layer_normalization_default_axis_expanded_function_StdDev"Sqrt: ˙ MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2DTLayerNormalization_test_layer_normalization_default_axis_expanded_function_Deviation"Sub: ‡ TLayerNormalization_test_layer_normalization_default_axis_expanded_function_Deviation QLayerNormalization_test_layer_normalization_default_axis_expanded_function_StdDevULayerNormalization_test_layer_normalization_default_axis_expanded_function_Normalized"Div:  ULayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedVLayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedT"Cast* to : o WRLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scale2D"Flatten* axis : † VLayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedT RLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scale2DQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scaled"Mul: k 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backend-test:đ2 {WLayerNormalization_test_layer_normalization_default_axis_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ā WLayerNormalization_test_layer_normalization_default_axis_expanded_function_FloatEpsilonRLayerNormalization_test_layer_normalization_default_axis_expanded_function_Epsilon"Cast* to : _ XQLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShape"Shape: Ŧ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShapeOLayerNormalization_test_layer_normalization_default_axis_expanded_function_Rank"Size: tQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Zero1D"Constant* value*: : }QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ú QLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShape QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Zero1D QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1DVLayerNormalization_test_layer_normalization_default_axis_expanded_function_PrefixShape"Slice: ĩ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axis1DYLayerNormalization_test_layer_normalization_default_axis_expanded_function_NumReducedAxes"Neg: Û YLayerNormalization_test_layer_normalization_default_axis_expanded_function_NumReducedAxesVLayerNormalization_test_layer_normalization_default_axis_expanded_function_SuffixShape"ConstantOfShape* value*: :   VLayerNormalization_test_layer_normalization_default_axis_expanded_function_PrefixShape VLayerNormalization_test_layer_normalization_default_axis_expanded_function_SuffixShapeWLayerNormalization_test_layer_normalization_default_axis_expanded_function_ReducedShape"Concat* axis : t XNLayerNormalization_test_layer_normalization_default_axis_expanded_function_X2D"Flatten* axis˙˙˙˙˙˙˙˙˙ : ˛ NLayerNormalization_test_layer_normalization_default_axis_expanded_function_X2DMLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU"Cast* to : tQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axes_1"Constant* value*: : ƒ MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axes_1QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2D" ReduceMean: ø MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XUQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Square"Mul:  QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Square QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Axes_1WLayerNormalization_test_layer_normalization_default_axis_expanded_function_MeanOfSquare" ReduceMean: † QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2D QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2DWLayerNormalization_test_layer_normalization_default_axis_expanded_function_SquareOfMean"Mul: ‰ WLayerNormalization_test_layer_normalization_default_axis_expanded_function_MeanOfSquare WLayerNormalization_test_layer_normalization_default_axis_expanded_function_SquareOfMeanNLayerNormalization_test_layer_normalization_default_axis_expanded_function_Var"Sub: † NLayerNormalization_test_layer_normalization_default_axis_expanded_function_Var RLayerNormalization_test_layer_normalization_default_axis_expanded_function_EpsilonYLayerNormalization_test_layer_normalization_default_axis_expanded_function_VarPlusEpsilon"Add: ļ YLayerNormalization_test_layer_normalization_default_axis_expanded_function_VarPlusEpsilonQLayerNormalization_test_layer_normalization_default_axis_expanded_function_StdDev"Sqrt: ˙ MLayerNormalization_test_layer_normalization_default_axis_expanded_function_XU QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Mean2DTLayerNormalization_test_layer_normalization_default_axis_expanded_function_Deviation"Sub: ‡ TLayerNormalization_test_layer_normalization_default_axis_expanded_function_Deviation QLayerNormalization_test_layer_normalization_default_axis_expanded_function_StdDevULayerNormalization_test_layer_normalization_default_axis_expanded_function_Normalized"Div:  ULayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedVLayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedT"Cast* to : o WRLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scale2D"Flatten* axis : † VLayerNormalization_test_layer_normalization_default_axis_expanded_function_NormalizedT RLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scale2DQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scaled"Mul: k BNLayerNormalization_test_layer_normalization_default_axis_expanded_function_B2D"Flatten* axis : ũ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Scaled NLayerNormalization_test_layer_normalization_default_axis_expanded_function_B2DQLayerNormalization_test_layer_normalization_default_axis_expanded_function_Biased"Add: ´ QLayerNormalization_test_layer_normalization_default_axis_expanded_function_Biased QLayerNormalization_test_layer_normalization_default_axis_expanded_function_XShapeY"Reshape: š QLayerNormalization_test_layer_normalization_default_axis_expanded_function_StdDevVLayerNormalization_test_layer_normalization_default_axis_expanded_function_InvStdDev2D" Reciprocal: Ŋ 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Œį?Ij?‚R>ķo ŋ ˇ?@Ģ‚?ÛT<pĻŋæ›Ŗ?ļ"?đ–ÅŊoutput_1.pb000066400000000000000000000001621511334557700411100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_default_axis_expanded_ver18/test_data_set_0BMeanJ`Äĸš?}Ķ<(Á?C]>“ĘÉ=А>§c ŋ^Â>b÷ ŋôI ŋ~*䞥#ŊÆ-ŋ Føž—n>U¸ôžĻ>û;?5E@žËRV?Āü5?pN ?output_2.pb000066400000000000000000000001671511334557700411160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_layer_normalization_default_axis_expanded_ver18/test_data_set_0B InvStdDevJ`”ųŋ?‰”Į?đĘ@ ŌĨ?æ°?éąd?Zŗ—?˛â´?°8C?O­˜?čEá?€v@gņĖ?ašŽ?’zž?eF@, Y?o„q?­˛Ũ?‡ûČ?]–=?ņ Z?0e?ĄŪ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu/000077500000000000000000000000001511334557700243115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu/model.onnx000066400000000000000000000001761511334557700263210ustar00rootroot00000000000000 backend-test:f " xy" LeakyRelu* alphaÍĖĖ= test_leakyreluZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu/test_data_set_0/000077500000000000000000000000001511334557700273535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700312620ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700314630ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī?‚%ČŊ˙8s?Đûwŧ )ŧø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?΍ŧiJ >7ëŽŊŒļ‚žŒS'?ąK]?Ķū—ŊŠC@SížHm;=MW™ŧ2Ä?ķŧ?ŠĒ>…žÁ>‹ŅĩŊdÕJž<Ŋō >*z?•į™?)ĻŊ{Ĩ÷ŧ`žÖŊëhžÚ¸.žŗų?čĀPŊko3ŊGIžœ G?]C%žßFŽŧ=dˇŊÆ>Í9QŊ"ËņŊīą8ģQNÛ>.:ˆ=™Ũš>ŋčŊ+”Ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default/000077500000000000000000000000001511334557700260155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default/model.onnx000066400000000000000000000001651511334557700300230ustar00rootroot00000000000000 backend-test:]  xy" LeakyRelutest_leakyrelu_defaultZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default/test_data_set_0/000077500000000000000000000000001511334557700310575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700327660ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700331670ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī? ŧ˙8s? cÆē€J‡ēø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?ØsģiJ >_ī ŧ$ŅŧŒS'?ąK]?P1ķģŠC@…HnŧHm;={Xõē2Ä?ķŧ?ŠĒ>…žÁ>otŧPDĸŧųdģō >*z?•į™?§Ö}ģüFģ€Ë+ŧŪ§hŧĮ‹ŧŗų?š§ģVŒģ BMŧœ G?ä5„ŧæk ģ—ļŧÆ>pa§ģNoAŧŒÁ“šQNÛ>.:ˆ=™Ũš>ËÚĪģŪšmģonnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default_expanded/000077500000000000000000000000001511334557700276655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default_expanded/model.onnx000066400000000000000000000017441511334557700316770ustar00rootroot00000000000000 backend-test:Ë ]8LeakyRelu_test_leakyrelu_default_expanded_function_Alpha"Constant* value_float ×#< : ‡ 8LeakyRelu_test_leakyrelu_default_expanded_function_Alpha x“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700347600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_leakyrelu_default_expanded/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī? 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FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?onnx-onnx-bca0315/onnx/backend/test/data/node/test_less/test_data_set_0/output_0.pb000066400000000000000000000001141511334557700304220ustar00rootroot00000000000000 BlessJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/000077500000000000000000000000001511334557700244365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/model.onnx000066400000000000000000000002031511334557700264350ustar00rootroot00000000000000 backend-test:k  x yless"Lesstest_less_bcastZ x    Z y  b less     B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/000077500000000000000000000000001511334557700275005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700314070ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/input_1.pb000066400000000000000000000000351511334557700314000ustar00rootroot00000000000000ByJ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_bcast/test_data_set_0/output_0.pb000066400000000000000000000001141511334557700315760ustar00rootroot00000000000000 BlessJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal/000077500000000000000000000000001511334557700244515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal/model.onnx000066400000000000000000000002371511334557700264570ustar00rootroot00000000000000 backend-test:†  x y less_equal" LessOrEqualtest_less_equalZ x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal/test_data_set_0/000077500000000000000000000000001511334557700275135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700314220ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal/test_data_set_0/input_1.pb000066400000000000000000000003761511334557700314230ustar00rootroot00000000000000ByJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8/test_data_set_0/000077500000000000000000000000001511334557700304555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700323470ustar00rootroot00000000000000BxJ<˙˙˙˙˙˙˙˙˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8/test_data_set_0/input_1.pb000066400000000000000000000001111511334557700323500ustar00rootroot00000000000000ByJ<˙ū˙˙˙˙˙˙˙˙ūonnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8/test_data_set_0/output_0.pb000066400000000000000000000001221511334557700325520ustar00rootroot00000000000000 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/000077500000000000000000000000001511334557700272635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/model.onnx000066400000000000000000000006361511334557700312740ustar00rootroot00000000000000 backend-test:… E x y5LessOrEqual_test_less_equal_int8_expanded_function_O1"Less: F x y5LessOrEqual_test_less_equal_int8_expanded_function_O2"Equal: € 5LessOrEqual_test_less_equal_int8_expanded_function_O1 5LessOrEqual_test_less_equal_int8_expanded_function_O2 less_equal"Or:test_less_equal_int8_expandedZ x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/test_data_set_0/000077500000000000000000000000001511334557700323255ustar00rootroot00000000000000input_0.pb000066400000000000000000000001111511334557700341400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/test_data_set_0BxJ<˙˙˙˙˙˙˙˙˙˙input_1.pb000066400000000000000000000001111511334557700341410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/test_data_set_0ByJ<˙ū˙˙˙˙˙˙˙˙ūoutput_0.pb000066400000000000000000000001221511334557700343430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_int8_expanded/test_data_set_0 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/000077500000000000000000000000001511334557700256575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/model.onnx000066400000000000000000000002461511334557700276650ustar00rootroot00000000000000 backend-test:  x y less_equal" LessOrEqualtest_less_equal_uint16Z x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/test_data_set_0/000077500000000000000000000000001511334557700307215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700326170ustar00rootroot00000000000000BxJx         onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/test_data_set_0/input_1.pb000066400000000000000000000002051511334557700326200ustar00rootroot00000000000000ByJx      onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16/test_data_set_0/output_0.pb000066400000000000000000000001221511334557700330160ustar00rootroot00000000000000 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/000077500000000000000000000000001511334557700275275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/model.onnx000066400000000000000000000006501511334557700315340ustar00rootroot00000000000000 backend-test: G x y7LessOrEqual_test_less_equal_uint16_expanded_function_O1"Less: H x y7LessOrEqual_test_less_equal_uint16_expanded_function_O2"Equal: „ 7LessOrEqual_test_less_equal_uint16_expanded_function_O1 7LessOrEqual_test_less_equal_uint16_expanded_function_O2 less_equal"Or:test_less_equal_uint16_expandedZ x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/test_data_set_0/000077500000000000000000000000001511334557700325715ustar00rootroot00000000000000input_0.pb000066400000000000000000000002051511334557700344100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/test_data_set_0BxJx         input_1.pb000066400000000000000000000002051511334557700344110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/test_data_set_0ByJx      output_0.pb000066400000000000000000000001221511334557700346070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint16_expanded/test_data_set_0 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/000077500000000000000000000000001511334557700256555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/model.onnx000066400000000000000000000002461511334557700276630ustar00rootroot00000000000000 backend-test:  x y less_equal" LessOrEqualtest_less_equal_uint32Z x     Z y     b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/test_data_set_0/000077500000000000000000000000001511334557700307175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700326260ustar00rootroot00000000000000 BxJđ          onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/test_data_set_0/input_1.pb000066400000000000000000000003761511334557700326270ustar00rootroot00000000000000 ByJđ      onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32/test_data_set_0/output_0.pb000066400000000000000000000001221511334557700330140ustar00rootroot00000000000000 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/000077500000000000000000000000001511334557700275255ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/model.onnx000066400000000000000000000006501511334557700315320ustar00rootroot00000000000000 backend-test: G x y7LessOrEqual_test_less_equal_uint32_expanded_function_O1"Less: H x y7LessOrEqual_test_less_equal_uint32_expanded_function_O2"Equal: „ 7LessOrEqual_test_less_equal_uint32_expanded_function_O1 7LessOrEqual_test_less_equal_uint32_expanded_function_O2 less_equal"Or:test_less_equal_uint32_expandedZ x     Z y     b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/test_data_set_0/000077500000000000000000000000001511334557700325675ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700344170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/test_data_set_0 BxJđ          input_1.pb000066400000000000000000000003761511334557700344200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/test_data_set_0 ByJđ      output_0.pb000066400000000000000000000001221511334557700346050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint32_expanded/test_data_set_0 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/000077500000000000000000000000001511334557700256625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/model.onnx000066400000000000000000000002461511334557700276700ustar00rootroot00000000000000 backend-test:  x y less_equal" LessOrEqualtest_less_equal_uint64Z x     Z y     b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/test_data_set_0/000077500000000000000000000000001511334557700307245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/test_data_set_0/input_0.pb000066400000000000000000000007561511334557700326350ustar00rootroot00000000000000 BxJā       onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/test_data_set_0/input_1.pb000066400000000000000000000007561511334557700326360ustar00rootroot00000000000000 ByJā       onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64/test_data_set_0/output_0.pb000066400000000000000000000001221511334557700330210ustar00rootroot00000000000000 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/000077500000000000000000000000001511334557700275325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/model.onnx000066400000000000000000000006501511334557700315370ustar00rootroot00000000000000 backend-test: G x y7LessOrEqual_test_less_equal_uint64_expanded_function_O1"Less: H x y7LessOrEqual_test_less_equal_uint64_expanded_function_O2"Equal: „ 7LessOrEqual_test_less_equal_uint64_expanded_function_O1 7LessOrEqual_test_less_equal_uint64_expanded_function_O2 less_equal"Or:test_less_equal_uint64_expandedZ x     Z y     b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/test_data_set_0/000077500000000000000000000000001511334557700325745ustar00rootroot00000000000000input_0.pb000066400000000000000000000007561511334557700344260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/test_data_set_0 BxJā       input_1.pb000066400000000000000000000007561511334557700344270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/test_data_set_0 ByJā       output_0.pb000066400000000000000000000001221511334557700346120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint64_expanded/test_data_set_0 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/000077500000000000000000000000001511334557700256005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/model.onnx000066400000000000000000000002451511334557700276050ustar00rootroot00000000000000 backend-test:Œ  x y less_equal" LessOrEqualtest_less_equal_uint8Z x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/test_data_set_0/000077500000000000000000000000001511334557700306425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700325340ustar00rootroot00000000000000BxJ<              onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/test_data_set_0/input_1.pb000066400000000000000000000001111511334557700325350ustar00rootroot00000000000000ByJ<           onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8/test_data_set_0/output_0.pb000066400000000000000000000001221511334557700327370ustar00rootroot00000000000000 B less_equalJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/000077500000000000000000000000001511334557700274505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/model.onnx000066400000000000000000000006431511334557700314570ustar00rootroot00000000000000 backend-test:Š F x y6LessOrEqual_test_less_equal_uint8_expanded_function_O1"Less: G x y6LessOrEqual_test_less_equal_uint8_expanded_function_O2"Equal: ‚ 6LessOrEqual_test_less_equal_uint8_expanded_function_O1 6LessOrEqual_test_less_equal_uint8_expanded_function_O2 less_equal"Or:test_less_equal_uint8_expandedZ x    Z y    b less_equal     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/test_data_set_0/000077500000000000000000000000001511334557700325125ustar00rootroot00000000000000input_0.pb000066400000000000000000000001111511334557700343250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/test_data_set_0BxJ<              input_1.pb000066400000000000000000000001111511334557700343260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/test_data_set_0ByJ<           output_0.pb000066400000000000000000000001221511334557700345300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_equal_uint8_expanded/test_data_set_0 B 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BlessJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_int8/000077500000000000000000000000001511334557700242245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_less_int8/model.onnx000066400000000000000000000002121511334557700262230ustar00rootroot00000000000000 backend-test:r  x yless"Lesstest_less_int8Z x    Z y    b less     B  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onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0/000077500000000000000000000000001511334557700326565ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700345060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= ­?>2Ä?ķŧ?ŠĒ>…žÁ>íEc?ŊŠũ?‹!˛>ō >*z?•į™?ŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗų?‘x?FKā>™[ ?œ G?4”Î?—ØY>L=e?Æ> Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700347070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_axis_2_expanded/test_data_set_0ByJđ7žŋ*’6žŠĀ‡ŋ‹PąŋézœŋÚõŸŋWĀ .ĀÜåŋęŅ ĀpÜ_ŋ+ĒČŋg@ ĀWCņŋYéĀ•bŋô" Ā÷:†5ÃŋNŋOŋ -Ā Ā`Ô'Āŗ Œŋ^ĤŋΈ,Āą{#ĀęēšŋŲĸŋ†˛Ā^iĀf—Ũŋ?_#ŋũXĀøõ Āu’ŋ˜–ŋ@[ūŋŠžĀŦWĀ/#ŅŋEŦŋS3ŋÕ"Ā&w Ā„ĨŦŋP|éŋĶŲ|ŋãĀx˛ŋŧķŋ˛´ãŋÍ÷ŋ¤ŊĀÂCĮŋ´“õŋđ_×ŋßåŦŋ 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0/000077500000000000000000000000001511334557700322715ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700341210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= ­?>2Ä?ķŧ?ŠĒ>…žÁ>íEc?ŊŠũ?‹!˛>ō >*z?•į™?ŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗų?‘x?FKā>™[ ?œ G?4”Î?—ØY>L=e?Æ> Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700343220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis/test_data_set_0ByJđ7žŋ*’6žŠĀ‡ŋ‹PąŋézœŋÚõŸŋWĀ .ĀÜåŋęŅ ĀpÜ_ŋ+ĒČŋg@ ĀWCņŋYéĀ•bŋô" Ā÷:†5ÃŋNŋOŋ -Ā Ā`Ô'Āŗ Œŋ^ĤŋΈ,Āą{#ĀęēšŋŲĸŋ†˛Ā^iĀf—Ũŋ?_#ŋũXĀøõ Āu’ŋ˜–ŋ@[ūŋŠžĀŦWĀ/#ŅŋEŦŋS3ŋÕ"Ā&w Ā„ĨŦŋP|éŋĶŲ|ŋãĀx˛ŋŧķŋ˛´ãŋÍ÷ŋ¤ŊĀÂCĮŋ´“õŋđ_×ŋßåŦŋ 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test_data_set_0/000077500000000000000000000000001511334557700351075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded_ver18input_0.pb000066400000000000000000000003761511334557700370160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= ­?>2Ä?ķŧ?ŠĒ>…žÁ>íEc?ŊŠũ?‹!˛>ō >*z?•į™?ŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗų?‘x?FKā>™[ ?œ G?4”Î?—ØY>L=e?Æ> Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700372170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_default_axis_expanded_ver18/test_data_set_0ByJđ7žŋ*’6žŠĀ‡ŋ‹PąŋézœŋÚõŸŋWĀ .ĀÜåŋęŅ ĀpÜ_ŋ+ĒČŋg@ ĀWCņŋYéĀ•bŋô" Ā÷:†5ÃŋNŋOŋ -Ā Ā`Ô'Āŗ Œŋ^ĤŋΈ,Āą{#ĀęēšŋŲĸŋ†˛Ā^iĀf—Ũŋ?_#ŋũXĀøõ Āu’ŋ˜–ŋ@[ūŋŠžĀŦWĀ/#ŅŋEŦŋS3ŋÕ"Ā&w 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8Āo,´ŋŧąĐžonnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_example_1_expanded/000077500000000000000000000000001511334557700303025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_example_1_expanded/model.onnx000066400000000000000000000021271511334557700323100ustar00rootroot00000000000000 backend-test:ž g;LogSoftmax_test_logsoftmax_example_1_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ : { xBLogSoftmax_test_logsoftmax_example_1_expanded_function_X_ReduceMax" ReduceMax* keepdims * axes@˙˙˙˙˙˙˙˙˙ : Œ x BLogSoftmax_test_logsoftmax_example_1_expanded_function_X_ReduceMaxLogSoftmax_test_logsoftmax_large_number_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ~ xELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceMax" ReduceMax* keepdims * axes@˙˙˙˙˙˙˙˙˙ : ’ x ELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceMax?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub"Sub: ‰ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Exp"Exp: æ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Exp >LogSoftmax_test_logsoftmax_large_number_expanded_function_axesELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceSum" ReduceSum* keepdims :  ELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceSum?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Log"Log: Œ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Logy"Sub:%test_logsoftmax_large_number_expandedZ x   b y   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded/test_data_set_0/000077500000000000000000000000001511334557700341335ustar00rootroot00000000000000input_0.pb000066400000000000000000000000531511334557700357530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded/test_data_set_0BxJ €?@@@@FDFHFLFoutput_0.pb000066400000000000000000000000531511334557700361540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded/test_data_set_0ByJ ,\Ā,Ā"X¸ŋŠ`áž,\Ā,Ā"X¸ŋŠ`ážonnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded_ver18/000077500000000000000000000000001511334557700321165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded_ver18/model.onnx000066400000000000000000000022621511334557700341240ustar00rootroot00000000000000 backend-test:™ j>LogSoftmax_test_logsoftmax_large_number_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ¨ x >LogSoftmax_test_logsoftmax_large_number_expanded_function_axesELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceMax" ReduceMax* keepdims : ’ x ELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceMax?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub"Sub: ‰ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Exp"Exp: æ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Exp >LogSoftmax_test_logsoftmax_large_number_expanded_function_axesELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceSum" ReduceSum* keepdims :  ELogSoftmax_test_logsoftmax_large_number_expanded_function_X_ReduceSum?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Log"Log: Œ ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Sub ?LogSoftmax_test_logsoftmax_large_number_expanded_function_X_Logy"Sub:+test_logsoftmax_large_number_expanded_ver18Z x   b y   B test_data_set_0/000077500000000000000000000000001511334557700351015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded_ver18input_0.pb000066400000000000000000000000531511334557700370000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded_ver18/test_data_set_0BxJ €?@@@@FDFHFLFoutput_0.pb000066400000000000000000000000531511334557700372010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_large_number_expanded_ver18/test_data_set_0ByJ ,\Ā,Ā"X¸ŋŠ`áž,\Ā,Ā"X¸ŋŠ`ážonnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis/000077500000000000000000000000001511334557700274055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis/model.onnx000066400000000000000000000002231511334557700314060ustar00rootroot00000000000000 backend-test:{ ( xy" LogSoftmax* axis˙˙˙˙˙˙˙˙˙ test_logsoftmax_negative_axisZ x    b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis/test_data_set_0/000077500000000000000000000000001511334557700324475ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700342770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= ­?>2Ä?ķŧ?ŠĒ>…žÁ>íEc?ŊŠũ?‹!˛>ō >*z?•į™?ŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗų?‘x?FKā>™[ ?œ G?4”Î?—ØY>L=e?Æ> Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700345000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis/test_data_set_0ByJđ7žŋ*’6žŠĀ‡ŋ‹PąŋézœŋÚõŸŋWĀ .ĀÜåŋęŅ ĀpÜ_ŋ+ĒČŋg@ ĀWCņŋYéĀ•bŋô" Ā÷:†5ÃŋNŋOŋ -Ā Ā`Ô'Āŗ Œŋ^ĤŋΈ,Āą{#ĀęēšŋŲĸŋ†˛Ā^iĀf—Ũŋ?_#ŋũXĀøõ Āu’ŋ˜–ŋ@[ūŋŠžĀŦWĀ/#ŅŋEŦŋS3ŋÕ"Ā&w Ā„ĨŦŋP|éŋĶŲ|ŋãĀx˛ŋŧķŋ˛´ãŋÍ÷ŋ¤ŊĀÂCĮŋ´“õŋđ_×ŋßåŦŋ ŠĪŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded/000077500000000000000000000000001511334557700312555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded/model.onnx000066400000000000000000000022271511334557700332640ustar00rootroot00000000000000 backend-test:ū k?LogSoftmax_test_logsoftmax_negative_axis_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ :  xFLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceMax" ReduceMax* keepdims * axes@˙˙˙˙˙˙˙˙˙ : ” x FLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceMax@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub"Sub: ‹ @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Exp"Exp: é @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Exp ?LogSoftmax_test_logsoftmax_negative_axis_expanded_function_axesFLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceSum" ReduceSum* keepdims : ‘ FLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceSum@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Log"Log: Ž @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Logy"Sub:&test_logsoftmax_negative_axis_expandedZ x    b y    B  test_data_set_0/000077500000000000000000000000001511334557700342405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expandedinput_0.pb000066400000000000000000000003761511334557700361470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= ­?>2Ä?ķŧ?ŠĒ>…žÁ>íEc?ŊŠũ?‹!˛>ō >*z?•į™?ŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗų?‘x?FKā>™[ ?œ G?4”Î?—ØY>L=e?Æ> Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700363500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded/test_data_set_0ByJđ7žŋ*’6žŠĀ‡ŋ‹PąŋézœŋÚõŸŋWĀ .ĀÜåŋęŅ ĀpÜ_ŋ+ĒČŋg@ ĀWCņŋYéĀ•bŋô" Ā÷:†5ÃŋNŋOŋ -Ā Ā`Ô'Āŗ Œŋ^ĤŋΈ,Āą{#ĀęēšŋŲĸŋ†˛Ā^iĀf—Ũŋ?_#ŋũXĀøõ Āu’ŋ˜–ŋ@[ūŋŠžĀŦWĀ/#ŅŋEŦŋS3ŋÕ"Ā&w Ā„ĨŦŋP|éŋĶŲ|ŋãĀx˛ŋŧķŋ˛´ãŋÍ÷ŋ¤ŊĀÂCĮŋ´“õŋđ_×ŋßåŦŋ ŠĪŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded_ver18/000077500000000000000000000000001511334557700323025ustar00rootroot00000000000000model.onnx000066400000000000000000000023111511334557700342240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded_ver18 backend-test:° k?LogSoftmax_test_logsoftmax_negative_axis_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ē x ?LogSoftmax_test_logsoftmax_negative_axis_expanded_function_axesFLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceMax" ReduceMax* keepdims : ” x FLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceMax@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub"Sub: ‹ @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Exp"Exp: é @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Exp ?LogSoftmax_test_logsoftmax_negative_axis_expanded_function_axesFLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceSum" ReduceSum* keepdims : ‘ FLogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_ReduceSum@LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Log"Log: Ž @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Sub @LogSoftmax_test_logsoftmax_negative_axis_expanded_function_X_Logy"Sub:,test_logsoftmax_negative_axis_expanded_ver18Z x    b y    B test_data_set_0/000077500000000000000000000000001511334557700352655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded_ver18input_0.pb000066400000000000000000000003761511334557700371740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_logsoftmax_negative_axis_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.z?˙8s?bũ>hdĶ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?R>iJ >ĻZ?/d#@ŒS'?ąK]?‡ū=?ŠC@¨(ē?Hm;= 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backend-test:j  x yz"Mul test_mul_int8Z x    Z y    b z    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_int8/test_data_set_0/000077500000000000000000000000001511334557700271155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_int8/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700310070ustar00rootroot00000000000000BxJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_int8/test_data_set_0/input_1.pb000066400000000000000000000001111511334557700310100ustar00rootroot00000000000000ByJ<            onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_int8/test_data_set_0/output_0.pb000066400000000000000000000001111511334557700312100ustar00rootroot00000000000000BzJ<93(*B0 <3 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BxJđonnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint32/test_data_set_0/input_1.pb000066400000000000000000000003761511334557700312670ustar00rootroot00000000000000 ByJđ          onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint32/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700314670ustar00rootroot00000000000000 BzJđ*.,"B0  $ onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/000077500000000000000000000000001511334557700243225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/model.onnx000066400000000000000000000002041511334557700263220ustar00rootroot00000000000000 backend-test:l  x yz"Multest_mul_uint64Z x     Z y     b z     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/test_data_set_0/000077500000000000000000000000001511334557700273645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/test_data_set_0/input_0.pb000066400000000000000000000007561511334557700312750ustar00rootroot00000000000000 BxJāonnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/test_data_set_0/input_1.pb000066400000000000000000000007561511334557700312760ustar00rootroot00000000000000 ByJā            onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint64/test_data_set_0/output_0.pb000066400000000000000000000007561511334557700314760ustar00rootroot00000000000000 BzJā  E"''.B$-*   66 onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/000077500000000000000000000000001511334557700242405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/model.onnx000066400000000000000000000002031511334557700262370ustar00rootroot00000000000000 backend-test:k  x yz"Multest_mul_uint8Z x    Z y    b z    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/test_data_set_0/000077500000000000000000000000001511334557700273025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700311740ustar00rootroot00000000000000BxJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/test_data_set_0/input_1.pb000066400000000000000000000001111511334557700311750ustar00rootroot00000000000000ByJ<         onnx-onnx-bca0315/onnx/backend/test/data/node/test_mul_uint8/test_data_set_0/output_0.pb000066400000000000000000000001111511334557700313750ustar00rootroot00000000000000BzJ< (* * 3B E,(     3 onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn/000077500000000000000000000000001511334557700231145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn/model.onnx000066400000000000000000000001771511334557700251250ustar00rootroot00000000000000 backend-test:g ! XY"MeanVarianceNormalizationtest_mvnZ X     b Y     B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn/test_data_set_0/000077500000000000000000000000001511334557700261565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn/test_data_set_0/input_0.pb000066400000000000000000000001731511334557700300600ustar00rootroot00000000000000BXJlNX??šo=Ļčî<Á>‚Š?9žK?2îp?ldt?š5>į€ė>t”‡>¸,?øjˆ<[î?ːl?Uäx?šõ=WžĶ>Bļi?â?šŊQ?]HI?î¨ņ=ŠF1?ŗ‹ ?íŪ™=onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn/test_data_set_0/output_0.pb000066400000000000000000000001731511334557700302610ustar00rootroot00000000000000BYJlëd­?á;Š>:ÅÅŋr÷šŋdŋú™>DüÂ>6nQ?įĐ[?:“ŋ#rcŊvyHŋE3U?>  ŋŧˇ,?˙UD?§Pi?āģŌŋņ¤ož˛úÍ?ÛÛ>Ö2Ĩ?ĻĪ—?úđmŋ­ē“=msÞÛÔãŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded/000077500000000000000000000000001511334557700247645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded/model.onnx000066400000000000000000000032621511334557700267730ustar00rootroot00000000000000 backend-test:™ c=MeanVarianceNormalization_test_mvn_expanded_function_Exponent"Constant* value* "@B : bMeanVarianceNormalization_test_mvn_expanded_function_X_squared"Pow:   >MeanVarianceNormalization_test_mvn_expanded_function_X_squared?MeanVarianceNormalization_test_mvn_expanded_function_E_Xsquared" ReduceMean* axes@@@ : Č ?MeanVarianceNormalization_test_mvn_expanded_function_E_Xsquared ?MeanVarianceNormalization_test_mvn_expanded_function_EX_squared=MeanVarianceNormalization_test_mvn_expanded_function_Variance"Sub:  =MeanVarianceNormalization_test_mvn_expanded_function_Variance8MeanVarianceNormalization_test_mvn_expanded_function_STD"Sqrt: † X 9MeanVarianceNormalization_test_mvn_expanded_function_X_RM?MeanVarianceNormalization_test_mvn_expanded_function_X_variance"Sub: à 8MeanVarianceNormalization_test_mvn_expanded_function_STD ‚Š?9žK?2îp?ldt?š5>į€ė>t”‡>¸,?øjˆ<[î?ːl?Uäx?šõ=WžĶ>Bļi?â?šŊQ?]HI?î¨ņ=ŠF1?ŗ‹ ?íŪ™=onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded/test_data_set_0/output_0.pb000066400000000000000000000001731511334557700321310ustar00rootroot00000000000000BYJlëd­?á;Š>:ÅÅŋr÷šŋdŋú™>DüÂ>6nQ?įĐ[?:“ŋ#rcŊvyHŋE3U?>  ŋŧˇ,?˙UD?§Pi?āģŌŋņ¤ož˛úÍ?ÛÛ>Ö2Ĩ?ĻĪ—?úđmŋ­ē“=msÞÛÔãŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded_ver18/000077500000000000000000000000001511334557700260115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded_ver18/model.onnx000066400000000000000000000035551511334557700300250ustar00rootroot00000000000000 backend-test:Ô c=MeanVarianceNormalization_test_mvn_expanded_function_Exponent"Constant* value* "@B : bMeanVarianceNormalization_test_mvn_expanded_function_X_squared"Pow: Ę >MeanVarianceNormalization_test_mvn_expanded_function_X_squared 9MeanVarianceNormalization_test_mvn_expanded_function_axes?MeanVarianceNormalization_test_mvn_expanded_function_E_Xsquared" ReduceMean: Č ?MeanVarianceNormalization_test_mvn_expanded_function_E_Xsquared ?MeanVarianceNormalization_test_mvn_expanded_function_EX_squared=MeanVarianceNormalization_test_mvn_expanded_function_Variance"Sub:  =MeanVarianceNormalization_test_mvn_expanded_function_Variance8MeanVarianceNormalization_test_mvn_expanded_function_STD"Sqrt: † X 9MeanVarianceNormalization_test_mvn_expanded_function_X_RM?MeanVarianceNormalization_test_mvn_expanded_function_X_variance"Sub: à 8MeanVarianceNormalization_test_mvn_expanded_function_STD ‚Š?9žK?2îp?ldt?š5>į€ė>t”‡>¸,?øjˆ<[î?ːl?Uäx?šõ=WžĶ>Bļi?â?šŊQ?]HI?î¨ņ=ŠF1?ŗ‹ ?íŪ™=onnx-onnx-bca0315/onnx/backend/test/data/node/test_mvn_expanded_ver18/test_data_set_0/output_0.pb000066400000000000000000000001731511334557700331560ustar00rootroot00000000000000BYJlëd­?á;Š>:ÅÅŋr÷šŋdŋú™>DüÂ>6nQ?įĐ[?:“ŋ#rcŊvyHŋE3U?>  ŋŧˇ,?˙UD?§Pi?āģŌŋņ¤ož˛úÍ?ÛÛ>Ö2Ĩ?ĻĪ—?úđmŋ­ē“=msÞÛÔãŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_neg/000077500000000000000000000000001511334557700230655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg/model.onnx000066400000000000000000000001411511334557700250650ustar00rootroot00000000000000 backend-test:I xy"Negtest_negZ x    b y    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg/test_data_set_0/000077500000000000000000000000001511334557700261275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700300360ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_neg/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700302370ustar00rootroot00000000000000ByJđxĖáŋháĖž“ŽzŋËjĀ$ īŋâ.z?˙8sŋbũ>hdĶ=ø9Ōž(€žĸ%ēŋ^ĶBŋĀ0ųŊ Bãž]×Ēžü=ŋŋR>iJ žĻZ?/d#@ŒS'ŋąK]ŋ‡ū=?ŠC(ē?Hm;Ŋ ­?>2ÄŋķŧŋŠĒž…žÁžíEc?ŊŠũ?‹!˛>ō ž*zŋ•į™ŋŗOÆ>mĮš>ü6†?&Ãĩ?gÚ?ŗųŋ‘x?FKā>™[ ?œ Gŋ4”Î?—ØY>L=e?Æž Ä?õ—?kŪæonnx-onnx-bca0315/onnx/backend/test/data/node/test_neg_example/000077500000000000000000000000001511334557700246005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg_example/model.onnx000066400000000000000000000001311511334557700265770ustar00rootroot00000000000000 backend-test:A xy"Negtest_neg_exampleZ x  b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg_example/test_data_set_0/000077500000000000000000000000001511334557700276425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg_example/test_data_set_0/input_0.pb000066400000000000000000000000211511334557700315340ustar00rootroot00000000000000BxJ€Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_neg_example/test_data_set_0/output_0.pb000066400000000000000000000000211511334557700317350ustar00rootroot00000000000000ByJ€@Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/000077500000000000000000000000001511334557700261025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/model.onnx000066400000000000000000000005261511334557700301110ustar00rootroot00000000000000 backend-test:Ĩ “ R T X G VX_newV_new"Momentum* alpha33s? * beta€? * mode"nesterov * norm_coefficient ×#< :ai.onnx.preview.trainingtest_nesterov_momentumZ R Z T Z X  Z G  Z V  b X_new  b V_new  B ai.onnx.preview.trainingonnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/000077500000000000000000000000001511334557700311445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/input_0.pb000066400000000000000000000000131511334557700330370ustar00rootroot00000000000000BRJÍĖĖ=onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/input_1.pb000066400000000000000000000000171511334557700330440ustar00rootroot00000000000000BTJonnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/input_2.pb000066400000000000000000000000211511334557700330400ustar00rootroot00000000000000BXJš™™?333@onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/input_3.pb000066400000000000000000000000211511334557700330410ustar00rootroot00000000000000BGJ×Ŗpŋ Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/input_4.pb000066400000000000000000000000211511334557700330420ustar00rootroot00000000000000BVJš™Ų?fff@onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700332430ustar00rootroot00000000000000BX_newJŪ?ČA=@onnx-onnx-bca0315/onnx/backend/test/data/node/test_nesterov_momentum/test_data_set_0/output_1.pb000066400000000000000000000000251511334557700332440ustar00rootroot00000000000000BV_newJ<ß/? °r?onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/000077500000000000000000000000001511334557700243625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/model.onnx000066400000000000000000000002651511334557700263710ustar00rootroot00000000000000  backend-test:œ F input targetloss"NegativeLogLikelihoodLoss* reduction"none test_nllloss_NCZ input   Z target  b loss  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/test_data_set_0/000077500000000000000000000000001511334557700274245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/test_data_set_0/input_0.pb000066400000000000000000000001131511334557700313200ustar00rootroot00000000000000BinputJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/test_data_set_0/input_1.pb000066400000000000000000000000461511334557700313260ustar00rootroot00000000000000BtargetJonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC/test_data_set_0/output_0.pb000066400000000000000000000000301511334557700315170ustar00rootroot00000000000000BlossJ Ļ7ŋrRÄžįķlŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/000077500000000000000000000000001511334557700262325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/model.onnx000066400000000000000000000027171511334557700302450ustar00rootroot00000000000000  backend-test:ļ iFNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_const_zero"Constant* value*: : hENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_const_one"Constant* value*: : c@NegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_axes"Constant* value*: : ¤ target @NegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_axesKNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_expanded_target" Unsqueeze: Å input KNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_expanded_targetPNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_input_gather_element"GatherElements* axis :   PNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_input_gather_elementENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_loss_NCdd"Neg: í ENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_loss_NCdd FNegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_const_zero ENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_const_one ENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_const_oneENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_loss_N1dd"Slice: š ENegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_loss_N1dd @NegativeLogLikelihoodLoss_test_nllloss_NC_expanded_function_axesloss"Squeeze:test_nllloss_NC_expandedZ input   Z target  b loss  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/test_data_set_0/000077500000000000000000000000001511334557700312745ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/test_data_set_0/input_0.pb000066400000000000000000000001131511334557700331700ustar00rootroot00000000000000BinputJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/test_data_set_0/input_1.pb000066400000000000000000000000461511334557700331760ustar00rootroot00000000000000BtargetJonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NC_expanded/test_data_set_0/output_0.pb000066400000000000000000000000301511334557700333670ustar00rootroot00000000000000BlossJ Ļ7ŋrRÄžįķlŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/000077500000000000000000000000001511334557700246075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/model.onnx000066400000000000000000000002731511334557700266150ustar00rootroot00000000000000  backend-test:ĸ F input targetloss"NegativeLogLikelihoodLoss* reduction"mean test_nllloss_NCd1Z input    Z target   b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/test_data_set_0/000077500000000000000000000000001511334557700276515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/test_data_set_0/input_0.pb000066400000000000000000000002111511334557700315440ustar00rootroot00000000000000BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/test_data_set_0/input_1.pb000066400000000000000000000001001511334557700315420ustar00rootroot00000000000000BtargetJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1/test_data_set_0/output_0.pb000066400000000000000000000000161511334557700317500ustar00rootroot00000000000000BlossJģŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/000077500000000000000000000000001511334557700264575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/model.onnx000066400000000000000000000032461511334557700304700ustar00rootroot00000000000000  backend-test: kHNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_const_zero"Constant* value*: : jGNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_const_one"Constant* value*: : eBNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_axes"Constant* value*: : ¨ target BNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_axesMNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_expanded_target" Unsqueeze: É input MNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_expanded_targetRNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_input_gather_element"GatherElements* axis : ¤ RNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_input_gather_elementGNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_NCdd"Neg: ÷ GNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_NCdd HNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_const_zero GNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_const_one GNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_const_oneGNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_N1dd"Slice: ā GNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_N1dd BNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_axesFNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_Ndd"Squeeze: m FNegativeLogLikelihoodLoss_test_nllloss_NCd1_expanded_function_loss_Nddloss" ReduceMean* keepdims :test_nllloss_NCd1_expandedZ input    Z target   b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/test_data_set_0/000077500000000000000000000000001511334557700315215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/test_data_set_0/input_0.pb000066400000000000000000000002111511334557700334140ustar00rootroot00000000000000BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/test_data_set_0/input_1.pb000066400000000000000000000001001511334557700334120ustar00rootroot00000000000000BtargetJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_expanded/test_data_set_0/output_0.pb000066400000000000000000000000161511334557700336200ustar00rootroot00000000000000BlossJģŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/000077500000000000000000000000001511334557700252705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/model.onnx000066400000000000000000000003231511334557700272720ustar00rootroot00000000000000  backend-test:ē [ input targetloss"NegativeLogLikelihoodLoss* ignore_index * reduction"mean test_nllloss_NCd1_iiZ input    Z target   b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/test_data_set_0/000077500000000000000000000000001511334557700303325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/test_data_set_0/input_0.pb000066400000000000000000000002111511334557700322250ustar00rootroot00000000000000BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/test_data_set_0/input_1.pb000066400000000000000000000001001511334557700322230ustar00rootroot00000000000000BtargetJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii/test_data_set_0/output_0.pb000066400000000000000000000000161511334557700324310ustar00rootroot00000000000000BlossJ"Đ ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/000077500000000000000000000000001511334557700271405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/model.onnx000066400000000000000000000112771511334557700311540ustar00rootroot00000000000000  backend-test:Ļ% nKNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero"Constant* value*: : mJNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_one"Constant* value*: : hENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_axes"Constant* value*: : Ž target ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_axesPNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_target" Unsqueeze: vSNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_ignore_index"Constant* value*: : … PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_target PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_targetXNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero_target_typed"Sub: Ŋ PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_targetVNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_target_int64"Cast* to : ũ VNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_target_int64 SNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_ignore_indexENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_mask"Equal: Đ ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_mask XNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero_target_typed PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_expanded_targetRNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_transform_targets"Where: Ņ input RNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_transform_targetsUNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_input_gather_element"GatherElements* axis : wQNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero_float"Constant* value* " : Û ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_mask QNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero_float UNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_input_gather_element_NegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_input_gather_element_transform"Where: ´ _NegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_input_gather_element_transformJNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_NCdd"Neg: † JNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_NCdd KNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero JNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_one JNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_oneJNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_N1dd"Slice: č ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_mask ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_axesMNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_squeeze_mask"Squeeze: vPNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_one_float"Constant* value* "€? : Í MNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_squeeze_mask QNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_zero_float PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_const_one_floatNNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_weight_gather"Where: đ JNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_N1dd ENegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_axesPNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_unweighted"Squeeze: ô PNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_unweighted NNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_weight_gatherINegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_Ndd"Mul: ´ INegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_NddINegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_sum" ReduceSum* keepdims :  NNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_weight_gatherRNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_weight_gather_sum" ReduceSum* keepdims : Ŧ INegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_loss_sum RNegativeLogLikelihoodLoss_test_nllloss_NCd1_ii_expanded_function_weight_gather_sumloss"Div:test_nllloss_NCd1_ii_expandedZ input    Z target   b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/test_data_set_0/000077500000000000000000000000001511334557700322025ustar00rootroot00000000000000input_0.pb000066400000000000000000000002111511334557700340160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/test_data_set_0BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000001001511334557700340140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/test_data_set_0BtargetJ0output_0.pb000066400000000000000000000000161511334557700342220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_ii_expanded/test_data_set_0BlossJ"Đ ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/000077500000000000000000000000001511334557700315215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/model.onnx000066400000000000000000000004171511334557700335270ustar00rootroot00000000000000  backend-test:ö l input target weightloss"NegativeLogLikelihoodLoss* ignore_index˙˙˙˙˙˙˙˙˙ * reduction"mean )test_nllloss_NCd1_mean_weight_negative_iiZ input    Z target   Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700345045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_iiinput_0.pb000066400000000000000000000005721511334557700364110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/test_data_set_0BinputJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?input_1.pb000066400000000000000000000002411511334557700364030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/test_data_set_0BtargetJ˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000421511334557700364030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/test_data_set_0BweightJ/ŗ“>ī×Ũ>5A?eÍĘ>Æbe?output_0.pb000066400000000000000000000000161511334557700366030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii/test_data_set_0BlossJI¤âžonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded/000077500000000000000000000000001511334557700333715ustar00rootroot00000000000000model.onnx000066400000000000000000000136511511334557700353240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded  backend-test:/ ƒ`NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero"Constant* value*: : ‚_NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_one"Constant* value*: : }ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_axes"Constant* value*: : Ø target ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_axeseNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_target" Unsqueeze: ”hNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_ignore_index"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ä eNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_target eNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_targetmNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero_target_typed"Sub: į eNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_targetkNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_target_int64"Cast* to : ŧ kNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_target_int64 hNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_ignore_indexZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_mask"Equal: ¤ ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_mask mNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero_target_typed eNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_expanded_targetgNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_transform_targets"Where: û input gNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_transform_targetsjNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_input_gather_element"GatherElements* axis : ŒfNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero_float"Constant* value* " : ¯ ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_mask fNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero_float jNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_input_gather_elementtNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_input_gather_element_transform"Where: Ū tNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_input_gather_element_transform_NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_NCdd"Neg: ī _NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_NCdd `NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero _NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_one _NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_one_NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_N1dd"Slice: å weight gNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_transform_targetshNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_temp"Gather: Ŗ ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_mask fNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_const_zero_float hNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_tempjNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_temp_1"Where: ¸ jNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_temp_1 ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_axescNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather"Squeeze: ¯ _NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_N1dd ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_axeseNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_unweighted"Squeeze: ŗ eNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_unweighted cNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather^NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_Ndd"Mul: Ū ^NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_Ndd^NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_sum" ReduceSum* keepdims : ė cNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gathergNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_sum" ReduceSum* keepdims : Ö ^NegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_loss_sum gNegativeLogLikelihoodLoss_test_nllloss_NCd1_mean_weight_negative_ii_expanded_function_weight_gather_sumloss"Div:2test_nllloss_NCd1_mean_weight_negative_ii_expandedZ input    Z target   Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700363545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expandedinput_0.pb000066400000000000000000000005721511334557700402610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BinputJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?input_1.pb000066400000000000000000000002411511334557700402530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BtargetJ˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000421511334557700402530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BweightJ/ŗ“>ī×Ũ>5A?eÍĘ>Æbe?output_0.pb000066400000000000000000000000161511334557700404530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BlossJI¤âžonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/000077500000000000000000000000001511334557700261565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/model.onnx000066400000000000000000000003401511334557700301570ustar00rootroot00000000000000  backend-test:Į N input target weightloss"NegativeLogLikelihoodLoss* reduction"mean test_nllloss_NCd1_weightZ input    Z target   Z weight  b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/test_data_set_0/000077500000000000000000000000001511334557700312205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/test_data_set_0/input_0.pb000066400000000000000000000002111511334557700331130ustar00rootroot00000000000000BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/test_data_set_0/input_1.pb000066400000000000000000000001001511334557700331110ustar00rootroot00000000000000BtargetJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/test_data_set_0/input_2.pb000066400000000000000000000000421511334557700331170ustar00rootroot00000000000000BweightJí™<\?N˛?cī?y™q?onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight/test_data_set_0/output_0.pb000066400000000000000000000000161511334557700333170ustar00rootroot00000000000000BlossJëŸŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/000077500000000000000000000000001511334557700300265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/model.onnx000066400000000000000000000052101511334557700320300ustar00rootroot00000000000000  backend-test:ī rONegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_const_zero"Constant* value*: : qNNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_const_one"Constant* value*: : lINegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_axes"Constant* value*: : ļ target INegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_axesTNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_expanded_target" Unsqueeze: × input TNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_expanded_targetYNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_input_gather_element"GatherElements* axis : ˛ YNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_input_gather_elementNNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_NCdd"Neg: š NNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_NCdd ONegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_const_zero NNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_const_one NNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_const_oneNNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_N1dd"Slice: n weight targetRNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_weight_gather"Gather: ü NNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_N1dd INegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_axesTNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_unweighted"Squeeze: € TNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_unweighted RNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_weight_gatherMNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_Ndd"Mul: ŧ MNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_NddMNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_sum" ReduceSum* keepdims : Ę RNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_weight_gatherVNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_weight_gather_sum" ReduceSum* keepdims : ´ MNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_loss_sum VNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_expanded_function_weight_gather_sumloss"Div:!test_nllloss_NCd1_weight_expandedZ input    Z target   Z weight  b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/test_data_set_0/000077500000000000000000000000001511334557700330705ustar00rootroot00000000000000input_0.pb000066400000000000000000000002111511334557700347040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/test_data_set_0BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000001001511334557700347020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/test_data_set_0BtargetJ0input_2.pb000066400000000000000000000000421511334557700347100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/test_data_set_0BweightJí™<\?N˛?cī?y™q?output_0.pb000066400000000000000000000000161511334557700351100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_expanded/test_data_set_0BlossJëŸŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/000077500000000000000000000000001511334557700266375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/model.onnx000066400000000000000000000003701511334557700306430ustar00rootroot00000000000000  backend-test:ß c input target weightloss"NegativeLogLikelihoodLoss* ignore_index * reduction"mean test_nllloss_NCd1_weight_iiZ input    Z target   Z weight  b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/test_data_set_0/000077500000000000000000000000001511334557700317015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/test_data_set_0/input_0.pb000066400000000000000000000002111511334557700335740ustar00rootroot00000000000000BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/test_data_set_0/input_1.pb000066400000000000000000000001001511334557700335720ustar00rootroot00000000000000BtargetJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/test_data_set_0/input_2.pb000066400000000000000000000000421511334557700336000ustar00rootroot00000000000000BweightJí™<\?N˛?cī?y™q?output_0.pb000066400000000000000000000000161511334557700337210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii/test_data_set_0BlossJz( ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/000077500000000000000000000000001511334557700305075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/model.onnx000066400000000000000000000122501511334557700325130ustar00rootroot00000000000000  backend-test:) uRNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero"Constant* value*: : tQNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_one"Constant* value*: : oLNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_axes"Constant* value*: : ŧ target LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_axesWNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target" Unsqueeze: }ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_ignore_index"Constant* value*: : š WNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target WNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target_NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero_target_typed"Sub: Ë WNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target]NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target_int64"Cast* to : ’ ]NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_target_int64 ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_ignore_indexLNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_mask"Equal: ė LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_mask _NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero_target_typed WNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_expanded_targetYNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_transform_targets"Where: ß input YNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_transform_targets\NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_input_gather_element"GatherElements* axis : ~XNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero_float"Constant* value* " : ÷ LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_mask XNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero_float \NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_input_gather_elementfNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_input_gather_element_transform"Where:  fNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_input_gather_element_transformQNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_NCdd"Neg: Š QNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_NCdd RNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero QNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_one QNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_oneQNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_N1dd"Slice: É weight YNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_transform_targetsZNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_temp"Gather: ë LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_mask XNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_const_zero_float ZNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_temp\NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_temp_1"Where: Ž \NegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_temp_1 LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_axesUNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather"Squeeze: … QNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_N1dd LNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_axesWNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_unweighted"Squeeze: ‰ WNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_unweighted UNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gatherPNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_Ndd"Mul:  PNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_NddPNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_sum" ReduceSum* keepdims : Đ UNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gatherYNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_sum" ReduceSum* keepdims : ē PNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_loss_sum YNegativeLogLikelihoodLoss_test_nllloss_NCd1_weight_ii_expanded_function_weight_gather_sumloss"Div:$test_nllloss_NCd1_weight_ii_expandedZ input    Z target   Z weight  b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/test_data_set_0/000077500000000000000000000000001511334557700335515ustar00rootroot00000000000000input_0.pb000066400000000000000000000002111511334557700353650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/test_data_set_0BinputJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000001001511334557700353630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/test_data_set_0BtargetJ0input_2.pb000066400000000000000000000000421511334557700353710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/test_data_set_0BweightJí™<\?N˛?cī?y™q?output_0.pb000066400000000000000000000000161511334557700355710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1_weight_ii_expanded/test_data_set_0BlossJz( ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/000077500000000000000000000000001511334557700250355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/model.onnx000066400000000000000000000003211511334557700270350ustar00rootroot00000000000000  backend-test:¸ F input targetloss"NegativeLogLikelihoodLoss* reduction"none test_nllloss_NCd1d2Z input     Z target    b loss    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/test_data_set_0/000077500000000000000000000000001511334557700300775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/test_data_set_0/input_0.pb000066400000000000000000000042041511334557700320000ustar00rootroot00000000000000BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/test_data_set_0/input_1.pb000066400000000000000000000015631511334557700320060ustar00rootroot00000000000000BtargetJāonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2/test_data_set_0/output_0.pb000066400000000000000000000007011511334557700321770ustar00rootroot00000000000000BlossJ°N˛ŋ 24ŋŗNŋw} ŋːž¨I˜žõ—2ŋžin2ŋĸãËž/GŋhŋČ˙Hŋ3S=ŋ|ĻŊûú‚ŊÉÎmž=zŋĻb`ŋ5“ŋž<:;ŧČDEŋę7žÛŲŋˁˇŊS ,ŋôJčžA] ŋVČĘžîB ŋŊo.ŋųRž1Ō†žI,Šŧ%BŋÕVŋĢ•lŋéĀ\ŋ1"ŲžŋŽ^ŋ•ąpŋrk˛žm™¸žų‡čžn3ŋ0å5ŋMŋ镾āĮužÆĀÕžˆxŋiū€ž,‘ęžŊJŋdÁwŋËĪžžƒėŊndŋËRŋoŊŠoŅž†Ũ&ž}˙ ŋM ûžöI}ŋÁŲž=r„žmūžžwdŋ˙&@ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/000077500000000000000000000000001511334557700267055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/model.onnx000066400000000000000000000030531511334557700307120ustar00rootroot00000000000000  backend-test:’ mJNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_const_zero"Constant* value*: : lINegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_const_one"Constant* value*: : gDNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_axes"Constant* value*: : Ŧ target DNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_axesONegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_expanded_target" Unsqueeze: Í input ONegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_expanded_targetTNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_input_gather_element"GatherElements* axis : ¨ TNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_input_gather_elementINegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_loss_NCdd"Neg:  INegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_loss_NCdd JNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_const_zero INegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_const_one INegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_const_oneINegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_loss_N1dd"Slice: ĸ INegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_loss_N1dd DNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_expanded_function_axesloss"Squeeze:test_nllloss_NCd1d2_expandedZ input     Z target    b loss    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/test_data_set_0/000077500000000000000000000000001511334557700317475ustar00rootroot00000000000000input_0.pb000066400000000000000000000042041511334557700335710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700335770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000007011511334557700337700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_expanded/test_data_set_0BlossJ°N˛ŋ 24ŋŗNŋw} ŋːž¨I˜žõ—2ŋžin2ŋĸãËž/GŋhŋČ˙Hŋ3S=ŋ|ĻŊûú‚ŊÉÎmž=zŋĻb`ŋ5“ŋž<:;ŧČDEŋę7žÛŲŋˁˇŊS ,ŋôJčžA] ŋVČĘžîB ŋŊo.ŋųRž1Ō†žI,Šŧ%BŋÕVŋĢ•lŋéĀ\ŋ1"ŲžŋŽ^ŋ•ąpŋrk˛žm™¸žų‡čžn3ŋ0å5ŋMŋ镾āĮužÆĀÕžˆxŋiū€ž,‘ęžŊJŋdÁwŋËĪžžƒėŊndŋËRŋoŊŠoŅž†Ũ&ž}˙ ŋM ûžöI}ŋÁŲž=r„žmūžžwdŋ˙&@ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii/000077500000000000000000000000001511334557700326355ustar00rootroot00000000000000model.onnx000066400000000000000000000003661511334557700345670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii  backend-test:Ũ [ input targetloss"NegativeLogLikelihoodLoss* ignore_index * reduction"mean /test_nllloss_NCd1d2_no_weight_reduction_mean_iiZ input     Z target    b loss B test_data_set_0/000077500000000000000000000000001511334557700356205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_iiinput_0.pb000066400000000000000000000042041511334557700375210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700375270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700377170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii/test_data_set_0BlossJƒŋtest_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded/000077500000000000000000000000001511334557700344265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000141441511334557700364360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded  backend-test:Ë0 ‰fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero"Constant* value*: : ˆeNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_one"Constant* value*: : ƒ`NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_axes"Constant* value*: : ä target `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_axeskNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_target" Unsqueeze: ‘nNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_ignore_index"Constant* value*: : Ö kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_target kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_targetsNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero_target_typed"Sub: ķ kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_targetqNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_target_int64"Cast* to : Î qNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_target_int64 nNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_ignore_index`NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_mask"Equal: ŧ `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_mask sNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero_target_typed kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_expanded_targetmNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_transform_targets"Where: ‡ input mNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_transform_targetspNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_input_gather_element"GatherElements* axis : ’lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero_float"Constant* value* " : Į `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_mask lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero_float pNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_input_gather_elementzNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_input_gather_element_transform"Where: ę zNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_input_gather_element_transformeNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_NCdd"Neg:  eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_NCdd fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_one eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_oneeNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_N1dd"Slice: š `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_mask `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_axeshNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_squeeze_mask"Squeeze: ‘kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_one_float"Constant* value* "€? : š hNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_squeeze_mask lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_zero_float kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_const_one_floatiNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_weight_gather"Where: Á eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_N1dd `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_axeskNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_unweighted"Squeeze: Å kNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_unweighted iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_weight_gatherdNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_Ndd"Mul: ę dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_NdddNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_sum" ReduceSum* keepdims : ø iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_weight_gathermNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_weight_gather_sum" ReduceSum* keepdims : â dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_loss_sum mNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded_function_weight_gather_sumloss"Div:8test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expandedZ input     Z target    b loss B test_data_set_0/000077500000000000000000000000001511334557700374705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expandedinput_0.pb000066400000000000000000000042041511334557700413710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700413770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700415670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_no_weight_reduction_mean_ii_expanded/test_data_set_0BlossJƒŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/000077500000000000000000000000001511334557700301115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/model.onnx000066400000000000000000000003241511334557700321140ustar00rootroot00000000000000  backend-test:ģ F input targetloss"NegativeLogLikelihoodLoss* reduction"mean "test_nllloss_NCd1d2_reduction_meanZ input     Z target    b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/test_data_set_0/000077500000000000000000000000001511334557700331535ustar00rootroot00000000000000input_0.pb000066400000000000000000000042041511334557700347750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700350030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700351730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean/test_data_set_0BlossJx ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expanded/000077500000000000000000000000001511334557700317615ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expanded/model.onnx000066400000000000000000000037611511334557700337740ustar00rootroot00000000000000  backend-test:Ø |YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_const_zero"Constant* value*: : {XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_const_one"Constant* value*: : vSNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_axes"Constant* value*: : Ę target SNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_axes^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_expanded_target" Unsqueeze: ë input ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_expanded_targetcNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_input_gather_element"GatherElements* axis : Æ cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_input_gather_elementXNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_NCdd"Neg: Ė XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_NCdd YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_const_zero XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_const_one XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_const_oneXNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_N1dd"Slice: “ XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_N1dd SNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_axesWNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_Ndd"Squeeze: ~ WNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_mean_expanded_function_loss_Nddloss" ReduceMean* keepdims :+test_nllloss_NCd1d2_reduction_mean_expandedZ input     Z target    b loss B test_data_set_0/000077500000000000000000000000001511334557700347445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expandedinput_0.pb000066400000000000000000000042041511334557700366450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700366530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expanded/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700370430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_mean_expanded/test_data_set_0BlossJx ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/000077500000000000000000000000001511334557700277755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/model.onnx000066400000000000000000000003221511334557700317760ustar00rootroot00000000000000  backend-test:š E input targetloss"NegativeLogLikelihoodLoss* reduction"sum !test_nllloss_NCd1d2_reduction_sumZ input     Z target    b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/test_data_set_0/000077500000000000000000000000001511334557700330375ustar00rootroot00000000000000input_0.pb000066400000000000000000000042041511334557700346610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700346670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700350570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum/test_data_set_0BlossJZkÂonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expanded/000077500000000000000000000000001511334557700316455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expanded/model.onnx000066400000000000000000000037351511334557700336610ustar00rootroot00000000000000  backend-test:Ä {XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_const_zero"Constant* value*: : zWNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_const_one"Constant* value*: : uRNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_axes"Constant* value*: : Č target RNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_axes]NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_expanded_target" Unsqueeze: é input ]NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_expanded_targetbNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_input_gather_element"GatherElements* axis : Ä bNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_input_gather_elementWNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_NCdd"Neg: Į WNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_NCdd XNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_const_zero WNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_const_one WNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_const_oneWNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_N1dd"Slice:  WNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_N1dd RNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_axesVNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_Ndd"Squeeze: | VNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_reduction_sum_expanded_function_loss_Nddloss" ReduceSum* keepdims :*test_nllloss_NCd1d2_reduction_sum_expandedZ input     Z target    b loss B test_data_set_0/000077500000000000000000000000001511334557700346305ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expandedinput_0.pb000066400000000000000000000042041511334557700365310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? 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MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700365370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expanded/test_data_set_0BtargetJāoutput_0.pb000066400000000000000000000000161511334557700367270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_reduction_sum_expanded/test_data_set_0BlossJZkÂonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/000077500000000000000000000000001511334557700274375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/model.onnx000066400000000000000000000003731511334557700314460ustar00rootroot00000000000000  backend-test:â N input target weightloss"NegativeLogLikelihoodLoss* reduction"none test_nllloss_NCd1d2_with_weightZ input     Z target    Z weight  b loss    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/test_data_set_0/000077500000000000000000000000001511334557700325015ustar00rootroot00000000000000input_0.pb000066400000000000000000000042041511334557700343230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700343310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/test_data_set_0BtargetJāinput_2.pb000066400000000000000000000000421511334557700343210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/test_data_set_0BweightJúY?`ŧo?×p?ÎW=KĄ?output_0.pb000066400000000000000000000007011511334557700345220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight/test_data_set_0BlossJ°ÅŊŋ~ŪÖž ´žÂĸž÷8ˆžâŧH?'ŋ0ŋŠėŋųež˜}ėžjÆ&ŋ9qöž>= ŋĘ üžU Ŋ­åÔžIkŊ.jÜži<ŋÖäŋ3…,ŋ3•ßžün¸ŧEÖŪž‘ŅŊģGĢŊs ŋŌ>#ŋHí4ŧ@ ŊŒšŧâ@ˆžûĘŠžŖjŋЁ›ŧG ž.Ŗšž}kŦž[žĘŋĨ4QŋÚ'ƒž#ļđž­2žôÃÔžMæmžĀOížÜųBŊ¨=ŋīįܾĊ‹ģĒrvŊ#ĘGŧ‡kkŋũSŋfgŗžŠKēBģ%Ŋ´—ÖŊM°ž}+šģ&ŠŊE…‡žG ž?æŊžqŦŖžĐž›æ žsOžxdEŧЇâžĀ|4Ŋí ŋ ŋxFĖžP—ŋë‡:ŊŌ6JŊkĀTžëĒ­ž ÂŲžĒúĐž^åØž(OŋŽã{ŧ¸cžš”ŗŧ^iŋۘqžđ؈žY‡°žž%PŊ2‡cž]|ŨŊ„úĢža*õžrß`Ŋ>!Äž™Cž”Úģžhëžn2mŋÁQ}žŠžĩu ŧ~ĐáŧŪn!Ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_expanded/000077500000000000000000000000001511334557700313075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_expanded/model.onnx000066400000000000000000000042431511334557700333160ustar00rootroot00000000000000  backend-test:Š yVNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_const_zero"Constant* value*: : xUNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_const_one"Constant* value*: : sPNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_axes"Constant* value*: : Ä target PNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_axes[NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_expanded_target" Unsqueeze: å input [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_expanded_target`NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_input_gather_element"GatherElements* axis : Ā `NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_input_gather_elementUNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_NCdd"Neg: Ŋ UNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_NCdd VNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_const_zero UNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_const_one UNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_const_oneUNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_N1dd"Slice: u weight targetYNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_weight_gather"Gather: ‘ UNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_N1dd PNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_axes[NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_unweighted"Squeeze: Å [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_loss_unweighted YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_expanded_function_weight_gatherloss"Mul:(test_nllloss_NCd1d2_with_weight_expandedZ input     Z target    Z weight  b loss    B test_data_set_0/000077500000000000000000000000001511334557700342725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_expandedinput_0.pb000066400000000000000000000042041511334557700361730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? 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ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? 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MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700374050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean/test_data_set_0BtargetJāinput_2.pb000066400000000000000000000000421511334557700373750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean/test_data_set_0BweightJúY?`ŧo?×p?ÎW=KĄ?output_0.pb000066400000000000000000000000161511334557700375750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean/test_data_set_0BlossJNŠ ŋtest_nllloss_NCd1d2_with_weight_reduction_mean_expanded/000077500000000000000000000000001511334557700343045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000063741511334557700363220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean_expanded  backend-test:ã ˆeNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_const_zero"Constant* value*: : ‡dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_const_one"Constant* value*: : ‚_NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_axes"Constant* value*: : â target _NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_axesjNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_expanded_target" Unsqueeze: ƒ input jNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_expanded_targetoNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_input_gather_element"GatherElements* axis : Ū oNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_input_gather_elementdNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_NCdd"Neg: ˆ dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_NCdd eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_const_zero dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_const_one dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_const_onedNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_N1dd"Slice: „ weight targethNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_weight_gather"Gather: ž dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_N1dd _NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_axesjNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_unweighted"Squeeze:  jNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_unweighted hNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_weight_gathercNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_Ndd"Mul: č cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_NddcNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_sum" ReduceSum* keepdims : ö hNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_weight_gatherlNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_weight_gather_sum" ReduceSum* keepdims : ā cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_loss_sum lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_mean_expanded_function_weight_gather_sumloss"Div:7test_nllloss_NCd1d2_with_weight_reduction_mean_expandedZ input     Z target    Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700373465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean_expandedinput_0.pb000066400000000000000000000042041511334557700412470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_mean_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? 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MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700372710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum/test_data_set_0BtargetJāinput_2.pb000066400000000000000000000000421511334557700372610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum/test_data_set_0BweightJúY?`ŧo?×p?ÎW=KĄ?output_0.pb000066400000000000000000000000161511334557700374610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum/test_data_set_0BlossJĪ0 Âtest_nllloss_NCd1d2_with_weight_reduction_sum_expanded/000077500000000000000000000000001511334557700341705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000052521511334557700362000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expanded  backend-test:‘ ‡dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_const_zero"Constant* value*: : †cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_const_one"Constant* value*: : ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_axes"Constant* value*: : ā target ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_axesiNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_expanded_target" Unsqueeze:  input iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_expanded_targetnNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_input_gather_element"GatherElements* axis : Ü nNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_input_gather_elementcNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_NCdd"Neg: ƒ cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_NCdd dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_const_zero cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_const_one cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_const_onecNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_N1dd"Slice: ƒ weight targetgNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_weight_gather"Gather: ģ cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_N1dd ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_axesiNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_unweighted"Squeeze: ŋ iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_unweighted gNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_weight_gatherbNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_Ndd"Mul: ˆ bNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_expanded_function_loss_Nddloss" ReduceSum* keepdims :6test_nllloss_NCd1d2_with_weight_reduction_sum_expandedZ input     Z target    Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700372325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expandedinput_0.pb000066400000000000000000000042041511334557700411330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700411410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expanded/test_data_set_0BtargetJāinput_2.pb000066400000000000000000000000421511334557700411310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expanded/test_data_set_0BweightJúY?`ŧo?×p?ÎW=KĄ?output_0.pb000066400000000000000000000000161511334557700413310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_expanded/test_data_set_0BlossJĪ0 Âonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii/000077500000000000000000000000001511334557700330605ustar00rootroot00000000000000model.onnx000066400000000000000000000004241511334557700350050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii  backend-test:û b input target weightloss"NegativeLogLikelihoodLoss* ignore_index * reduction"sum 0test_nllloss_NCd1d2_with_weight_reduction_sum_iiZ input     Z target    Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700360435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_iiinput_0.pb000066400000000000000000000042041511334557700377440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>ZÅ<‹­=>ãqÍ> æm?ęĖ=H˙q?͖^?ų‡č>WE§>zTn>M?9y=”°<‹Û>lj‹=iū€>xb>Gĸ>X3>”3E<ƒė=šT?Ûhy?@‡}?ŠoŅ>†Ũ&>ä…#?M û>öI}?6ž…= ‚H?ø¨“>q6w>æ™)?ũ÷{>žu*?Un?1"Ų>?kø’>â4?hÔ>m™¸>Ü"T?ĸĘl?)r<=Å5n>!q˛>ĨĄP?+I|?ˆx?˛Ēg?9֗>sô}?øg>SåØ=Ąqs?¸o>§”0?o=Ā;?j¸a?Ų|‹>ŋÂ>ĀŖŋ>–°??žƒs>Bú/>Ž æ>Hã›>ÕV?˜rs>˜œ?)Mq?ŦM"?Ž^?•ąp? 2@?Z3?—Ėw? ‘~?,Uį>-$‘=é•>ß>ÆĀÕ>ļp>w§?k˙Ã>8e?dÁw?Ļ ?­ĩŒ>jœ?$’e?Z?Đ>U ?‹>ú/é>f­Í>`~>v€?<ęž>mūž>wd?˙&@?~ÁĒ>Ģ•l?éĀ\?zoG=oŨ>ākä>&GÖ=rk˛>w=?26.?–T?0å5?„×Q>ķŽ>:-?ˆa?|. ?ž>ī¯÷<ĸØ5?T,<ËĪž>IŅ?l?ëHˇ=Ŧ×Ī>z,Į<ĩj¯>‰J?ūáŽ>ąČV>Ņõė=wŋ?7ũ1?b,?Žčr?input_1.pb000066400000000000000000000015631511334557700377520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii/test_data_set_0BtargetJāinput_2.pb000066400000000000000000000000421511334557700377420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii/test_data_set_0BweightJúY?`ŧo?×p?ÎW=KĄ?output_0.pb000066400000000000000000000000161511334557700401420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii/test_data_set_0BlossJbÚŋÁtest_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded/000077500000000000000000000000001511334557700346515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000133361511334557700366630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded  backend-test:Å- ŠgNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero"Constant* value*: : ‰fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_one"Constant* value*: : „aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_axes"Constant* value*: : æ target aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_axeslNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_target" Unsqueeze: ’oNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_ignore_index"Constant* value*: : Ų lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_target lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_targettNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero_target_typed"Sub: õ lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_targetrNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_target_int64"Cast* to : Ņ rNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_target_int64 oNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_ignore_indexaNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_mask"Equal: Ā aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_mask tNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero_target_typed lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_expanded_targetnNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_transform_targets"Where: ‰ input nNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_transform_targetsqNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_input_gather_element"GatherElements* axis : “mNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero_float"Constant* value* " : Ë aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_mask mNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero_float qNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_input_gather_element{NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_input_gather_element_transform"Where: ė {NegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_input_gather_element_transformfNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_NCdd"Neg: ’ fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_NCdd gNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_one fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_onefNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_N1dd"Slice: ķ weight nNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_transform_targetsoNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gather_temp"Gather: ŋ aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_mask mNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_const_zero_float oNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gather_tempqNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gather_temp_1"Where: Í qNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gather_temp_1 aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_axesjNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gather"Squeeze: Ä fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_N1dd aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_axeslNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_unweighted"Squeeze: Č lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_unweighted jNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_weight_gathereNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_Ndd"Mul: ‹ eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded_function_loss_Nddloss" ReduceSum* keepdims :9test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expandedZ input     Z target    Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700377135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expandedinput_0.pb000066400000000000000000000042041511334557700416140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2_with_weight_reduction_sum_ii_expanded/test_data_set_0BinputJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000461511334557700360640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii/test_data_set_0BtargetJ input_2.pb000066400000000000000000000000421511334557700360610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii/test_data_set_0BweightJ(Šŧ>u?¸> ž^?Á|ō>output_0.pb000066400000000000000000000000161511334557700362610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii/test_data_set_0BlossJШ|ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded/000077500000000000000000000000001511334557700330475ustar00rootroot00000000000000model.onnx000066400000000000000000000125051511334557700347770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded  backend-test:Ŧ* ‚_NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero"Constant* value*: : ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_one"Constant* value*: : |YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_axes"Constant* value*: : Ö target YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_axesdNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_target" Unsqueeze: ŠgNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_ignore_index"Constant* value*:  : Á dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_target dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_targetlNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero_target_typed"Sub: å dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_targetjNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_target_int64"Cast* to : š jNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_target_int64 gNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_ignore_indexYNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_mask"Equal:   YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_mask lNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero_target_typed dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_expanded_targetfNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_transform_targets"Where: ų input fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_transform_targetsiNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_input_gather_element"GatherElements* axis : ‹eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero_float"Constant* value* " : Ģ YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_mask eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero_float iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_input_gather_elementsNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_input_gather_element_transform"Where: Ü sNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_input_gather_element_transform^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_NCdd"Neg: ę ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_NCdd _NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_one ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_one^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_N1dd"Slice: ã weight fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_transform_targetsgNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather_temp"Gather: Ÿ YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_mask eNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_const_zero_float gNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather_tempiNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather_temp_1"Where: ĩ iNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather_temp_1 YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_axesbNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather"Squeeze: Ŧ ^NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_N1dd YNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_axesdNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_unweighted"Squeeze: ° dNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_unweighted bNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_weight_gather]NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_Ndd"Mul: ƒ ]NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded_function_loss_Nddloss" ReduceSum* keepdims :1test_nllloss_NCd1d2d3_sum_weight_high_ii_expandedZ input   Z target  Z weight  b loss B test_data_set_0/000077500000000000000000000000001511334557700360325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expandedinput_0.pb000066400000000000000000000001131511334557700377260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded/test_data_set_0BinputJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000461511334557700377340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded/test_data_set_0BtargetJ input_2.pb000066400000000000000000000000421511334557700377310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded/test_data_set_0BweightJ(Šŧ>u?¸> ž^?Á|ō>output_0.pb000066400000000000000000000000161511334557700401310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3_sum_weight_high_ii_expanded/test_data_set_0BlossJШ|ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight/000077500000000000000000000000001511334557700303145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight/model.onnx000066400000000000000000000004151511334557700323200ustar00rootroot00000000000000  backend-test:ô N input target weightloss"NegativeLogLikelihoodLoss* reduction"mean %test_nllloss_NCd1d2d3d4d5_mean_weightZ+ input"        Z( target       Z weight  b loss B onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight/test_data_set_0/000077500000000000000000000000001511334557700333565ustar00rootroot00000000000000input_0.pb000066400000000000000000003751331511334557700352140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight/test_data_set_0BinputJĀô  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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‰=ĩā?Ë@?ŖoĘ>id>output_0.pb000066400000000000000000000000161511334557700353760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight/test_data_set_0BlossJËpũžonnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight_expanded/000077500000000000000000000000001511334557700321645ustar00rootroot00000000000000model.onnx000066400000000000000000000060241511334557700341130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_nllloss_NCd1d2d3d4d5_mean_weight_expanded  backend-test:û \NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_const_zero"Constant* value*: : ~[NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_const_one"Constant* value*: : yVNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_axes"Constant* value*: : Đ target VNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_axesaNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_expanded_target" Unsqueeze: ņ input aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_expanded_targetfNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_input_gather_element"GatherElements* axis : Ė fNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_input_gather_element[NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_NCdd"Neg: Û [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_NCdd \NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_const_zero [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_const_one [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_const_one[NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_N1dd"Slice: { weight target_NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_weight_gather"Gather: Ŗ [NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_N1dd VNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_axesaNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_unweighted"Squeeze: § aNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_unweighted _NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_weight_gatherZNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_Ndd"Mul: Ö ZNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_NddZNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_sum" ReduceSum* keepdims : ä _NegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_weight_gathercNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_weight_gather_sum" ReduceSum* keepdims : Î ZNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_loss_sum cNegativeLogLikelihoodLoss_test_nllloss_NCd1d2d3d4d5_mean_weight_expanded_function_weight_gather_sumloss"Div:.test_nllloss_NCd1d2d3d4d5_mean_weight_expandedZ+ input"        Z( target       Z weight  b loss B 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_pow_types_int64_int64/test_data_set_0/000077500000000000000000000000001511334557700314575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_pow_types_int64_int64/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700333530ustar00rootroot00000000000000BxJonnx-onnx-bca0315/onnx/backend/test/data/node/test_pow_types_int64_int64/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700333540ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_pow_types_int64_int64/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700335540ustar00rootroot00000000000000BzJ Ųonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/000077500000000000000000000000001511334557700254655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/model.onnx000066400000000000000000000002131511334557700274650ustar00rootroot00000000000000 backend-test:s  x slopey"PRelutest_prelu_broadcastZ x    Z slope  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/test_data_set_0/000077500000000000000000000000001511334557700305275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700324360ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700324240ustar00rootroot00000000000000BslopeJ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700326370ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī?˙<(?˙8s? ü=v6>ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?ÔĶ*>iJ >-ž™ŋÛ?ŒS'?ąK]?ėũŖ?ŠC@o^z?Hm;=aÜ>2Ä?ķŧ?ŠĒ>…žÁ>kÎ8?°×Z@?×|Ŋō >*z?•į™?ŗ++?Úą[Ŋ‚4?Ø´?ā—ą?ŗų?1šŊ1Ԗ>í æ>œ G?ŠN2@9›ŊŠ'?Æ>ųŠÔ>Op@BŲŖģQNÛ>.:ˆ=™Ũš>—)Œ?΃Ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/000077500000000000000000000000001511334557700273355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/model.onnx000066400000000000000000000012441511334557700313420ustar00rootroot00000000000000 backend-test:‹ W1PRelu_test_prelu_broadcast_expanded_function_Zero"Constant* value* "B : y 1PRelu_test_prelu_broadcast_expanded_function_Zero x5PRelu_test_prelu_broadcast_expanded_function_ZeroCast"CastLike: ~ x 5PRelu_test_prelu_broadcast_expanded_function_ZeroCast:PRelu_test_prelu_broadcast_expanded_function_XLessThanZero"Less: I slope x6PRelu_test_prelu_broadcast_expanded_function_SlopeMulX"Mul: ƒ :PRelu_test_prelu_broadcast_expanded_function_XLessThanZero 6PRelu_test_prelu_broadcast_expanded_function_SlopeMulX xy"Where:test_prelu_broadcast_expandedZ x    Z slope  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/test_data_set_0/000077500000000000000000000000001511334557700323775ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700342270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžinput_1.pb000066400000000000000000000000411511334557700342150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/test_data_set_0BslopeJ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>output_0.pb000066400000000000000000000003761511334557700344300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_broadcast_expanded/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī?˙<(?˙8s? ü=v6>ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?ÔĶ*>iJ >-ž™ŋÛ?ŒS'?ąK]?ėũŖ?ŠC@o^z?Hm;=aÜ>2Ä?ķŧ?ŠĒ>…žÁ>kÎ8?°×Z@?×|Ŋō >*z?•į™?ŗ++?Úą[Ŋ‚4?Ø´?ā—ą?ŗų?1šŊ1Ԗ>í æ>œ G?ŠN2@9›ŊŠ'?Æ>ųŠÔ>Op@BŲŖģQNÛ>.:ˆ=™Ũš>—)Œ?΃Ŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/000077500000000000000000000000001511334557700251565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/model.onnx000066400000000000000000000002211511334557700271550ustar00rootroot00000000000000 backend-test:y  x slopey"PRelutest_prelu_exampleZ x    Z slope    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/test_data_set_0/000077500000000000000000000000001511334557700302205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700321270ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/test_data_set_0/input_1.pb000066400000000000000000000004021511334557700321160ustar00rootroot00000000000000BslopeJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700323300ustar00rootroot00000000000000ByJđxĖá?háĖ>“Žz?Ëj@$ ī?ĸ É>˙8s?úsŊēËŋ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?=6ķ=iJ >Ė|DŊ`>@ŒS'?ąK]?Qđ‘?ŠC@Ūw0ĀHm;=ķ =2Ä?ķŧ?ŠĒ>…žÁ>[W=žtž÷ŋnëũŊō >*z?•į™?GXIŊÉāøŊ%ŋüŋÆøô?Ŋ @ŗų?ü?¨øYŋq§?œ G?ĄžFĀCĄž ÖŋÆ>˙<á>MSĀ—~÷;QNÛ>.:ˆ=™Ũš>´oĮžƒFĢžonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/000077500000000000000000000000001511334557700270265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/model.onnx000066400000000000000000000012311511334557700310270ustar00rootroot00000000000000 backend-test:€ U/PRelu_test_prelu_example_expanded_function_Zero"Constant* value* "B : u /PRelu_test_prelu_example_expanded_function_Zero x3PRelu_test_prelu_example_expanded_function_ZeroCast"CastLike: z x 3PRelu_test_prelu_example_expanded_function_ZeroCast8PRelu_test_prelu_example_expanded_function_XLessThanZero"Less: G slope x4PRelu_test_prelu_example_expanded_function_SlopeMulX"Mul:  8PRelu_test_prelu_example_expanded_function_XLessThanZero 4PRelu_test_prelu_example_expanded_function_SlopeMulX xy"Where:test_prelu_example_expandedZ x    Z slope    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/test_data_set_0/000077500000000000000000000000001511334557700320705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700337770ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/test_data_set_0/input_1.pb000066400000000000000000000004021511334557700337660ustar00rootroot00000000000000BslopeJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋ”Čø?ŪÅĶž3Y?ŋ÷"ö?‚Ŋ?, ī?‹ōg?Iy\ŋ}ô?Ŋ7‰žČmM?r?÷ēžN4?Ŋl?output_0.pb000066400000000000000000000003761511334557700341210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_prelu_example_expanded/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī?ĸ É>˙8s?úsŊēËŋ=ø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?=6ķ=iJ >Ė|DŊ`>@ŒS'?ąK]?Qđ‘?ŠC@Ūw0ĀHm;=ķ =2Ä?ķŧ?ŠĒ>…žÁ>[W=žtž÷ŋnëũŊō >*z?•į™?GXIŊÉāøŊ%ŋüŋÆøô?Ŋ @ŗų?ü?¨øYŋq§?œ G?ĄžFĀCĄž ÖŋÆ>˙<á>MSĀ—~÷;QNÛ>.:ˆ=™Ũš>´oĮžƒFĢžonnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/000077500000000000000000000000001511334557700246355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/model.onnx000066400000000000000000000005501511334557700266410ustar00rootroot00000000000000 backend-test:Ī [ x x_scale x_zero_point w w_scale w_zero_point y_scale y_zero_pointy" QLinearConvtest_qlinearconvZ x     Z x_scale Z x_zero_point Z w     Z w_scale  Z w_zero_point  Z y_scale Z y_zero_point b y     B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/000077500000000000000000000000001511334557700276775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_0.pb000066400000000000000000000001001511334557700315670ustar00rootroot00000000000000BxJ1˙Žĸ˨:;í_@8ō™Ũ¨ Ļč˛ēÃíĸíŧ'|MPf+æS)(†˙š\*”÷onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_1.pb000066400000000000000000000000211511334557700315720ustar00rootroot00000000000000Bx_scaleJEöq;onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_2.pb000066400000000000000000000000231511334557700315750ustar00rootroot00000000000000B x_zero_pointJ„onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_3.pb000066400000000000000000000000201511334557700315730ustar00rootroot00000000000000BwJonnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_4.pb000066400000000000000000000000231511334557700315770ustar00rootroot00000000000000Bw_scaleJ=|â:onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_5.pb000066400000000000000000000000251511334557700316020ustar00rootroot00000000000000B w_zero_pointJ˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_6.pb000066400000000000000000000000211511334557700315770ustar00rootroot00000000000000By_scaleJÆ:Õ:onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_7.pb000066400000000000000000000000231511334557700316020ustar00rootroot00000000000000B y_zero_pointJ{onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/output_0.pb000066400000000000000000000001001511334557700317700ustar00rootroot00000000000000ByJ1Q]æ4WÅđÄ ~˙ŋĮ f"WķYME<]C؃˛¯™Ô€ęŦÖ×yeŖrÕkonnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/000077500000000000000000000000001511334557700300125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/model.onnx000066400000000000000000000005641511334557700320230ustar00rootroot00000000000000  backend-test:Û ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul"test_qlinearmatmul_2D_int8_float16Z a   Z a_scale   Z a_zero_point  Z b   Z b_scale   Z b_zero_point  Z y_scale   Z y_zero_point  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0/000077500000000000000000000000001511334557700330545ustar00rootroot00000000000000input_0.pb000066400000000000000000000000231511334557700346710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0BaJQmo„W€žinput_1.pb000066400000000000000000000000211511334557700346700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0 Ba_scaleJÂinput_2.pb000066400000000000000000000000251511334557700346750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0B a_zero_pointJōinput_3.pb000066400000000000000000000000271511334557700347000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0BbJ ´uŊ›€wxinput_4.pb000066400000000000000000000000211511334557700346730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0 Bb_scaleJ8input_5.pb000066400000000000000000000000251511334557700347000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0B b_zero_pointJķinput_6.pb000066400000000000000000000000211511334557700346750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0 By_scaleJz!input_7.pb000066400000000000000000000000251511334557700347020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0B y_zero_pointJ÷output_0.pb000066400000000000000000000000211511334557700350700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float16/test_data_set_0ByJ)ô÷ĩonnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/000077500000000000000000000000001511334557700300105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/model.onnx000066400000000000000000000005641511334557700320210ustar00rootroot00000000000000  backend-test:Û ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul"test_qlinearmatmul_2D_int8_float32Z a   Z a_scale  Z a_zero_point  Z b   Z b_scale  Z b_zero_point  Z y_scale  Z y_zero_point  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0/000077500000000000000000000000001511334557700330525ustar00rootroot00000000000000input_0.pb000066400000000000000000000000231511334557700346670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0BaJQmo„W€žinput_1.pb000066400000000000000000000000231511334557700346700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0Ba_scaleJĐDØ;input_2.pb000066400000000000000000000000251511334557700346730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0B a_zero_pointJōinput_3.pb000066400000000000000000000000271511334557700346760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0BbJ ´uŊ›€wxinput_4.pb000066400000000000000000000000231511334557700346730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0Bb_scaleJ°į;input_5.pb000066400000000000000000000000251511334557700346760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0B b_zero_pointJķinput_6.pb000066400000000000000000000000231511334557700346750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0By_scaleJO/<input_7.pb000066400000000000000000000000251511334557700347000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0B y_zero_pointJ÷output_0.pb000066400000000000000000000000211511334557700350660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_int8_float32/test_data_set_0ByJ)ô÷ĩonnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/000077500000000000000000000000001511334557700301775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/model.onnx000066400000000000000000000005651511334557700322110ustar00rootroot00000000000000  backend-test:Ü ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul#test_qlinearmatmul_2D_uint8_float16Z a   Z a_scale   Z a_zero_point  Z b   Z b_scale   Z b_zero_point  Z y_scale   Z y_zero_point  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0/000077500000000000000000000000001511334557700332415ustar00rootroot00000000000000input_0.pb000066400000000000000000000000231511334557700350560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0BaJĐėîÖ˙input_1.pb000066400000000000000000000000211511334557700350550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0 Ba_scaleJÂinput_2.pb000066400000000000000000000000251511334557700350620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0B a_zero_pointJqinput_3.pb000066400000000000000000000000271511334557700350650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0BbJ ˜3ô<˙öū÷input_4.pb000066400000000000000000000000211511334557700350600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0 Bb_scaleJ8input_5.pb000066400000000000000000000000251511334557700350650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000211511334557700350620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0 By_scaleJz!input_7.pb000066400000000000000000000000251511334557700350670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0B y_zero_pointJvoutput_0.pb000066400000000000000000000000211511334557700352550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float16/test_data_set_0ByJ¨s˙B—onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/000077500000000000000000000000001511334557700301755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/model.onnx000066400000000000000000000005651511334557700322070ustar00rootroot00000000000000  backend-test:Ü ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul#test_qlinearmatmul_2D_uint8_float32Z a   Z a_scale  Z a_zero_point  Z b   Z b_scale  Z b_zero_point  Z y_scale  Z y_zero_point  b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0/000077500000000000000000000000001511334557700332375ustar00rootroot00000000000000input_0.pb000066400000000000000000000000231511334557700350540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0BaJĐėîÖ˙input_1.pb000066400000000000000000000000231511334557700350550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0Ba_scaleJĐDØ;input_2.pb000066400000000000000000000000251511334557700350600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0B a_zero_pointJqinput_3.pb000066400000000000000000000000271511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0BbJ ˜3ô<˙öū÷input_4.pb000066400000000000000000000000231511334557700350600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0Bb_scaleJ°į;input_5.pb000066400000000000000000000000251511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000231511334557700350620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0By_scaleJO/<input_7.pb000066400000000000000000000000251511334557700350650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0B y_zero_pointJvoutput_0.pb000066400000000000000000000000211511334557700352530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_2D_uint8_float32/test_data_set_0ByJ¨s˙B—onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/000077500000000000000000000000001511334557700300135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/model.onnx000066400000000000000000000006001511334557700320130ustar00rootroot00000000000000  backend-test:į ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul"test_qlinearmatmul_3D_int8_float16Z a    Z a_scale   Z a_zero_point  Z b    Z b_scale   Z b_zero_point  Z y_scale   Z y_zero_point  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0/000077500000000000000000000000001511334557700330555ustar00rootroot00000000000000input_0.pb000066400000000000000000000000351511334557700346750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0BaJQmo„W€žQmo„W€žinput_1.pb000066400000000000000000000000211511334557700346710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0 Ba_scaleJÂinput_2.pb000066400000000000000000000000251511334557700346760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0B a_zero_pointJōinput_3.pb000066400000000000000000000000451511334557700347010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0BbJ´uŊ›€wx´uŊ›€wxinput_4.pb000066400000000000000000000000211511334557700346740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0 Bb_scaleJ8input_5.pb000066400000000000000000000000251511334557700347010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000211511334557700346760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0 By_scaleJz!input_7.pb000066400000000000000000000000251511334557700347030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0B y_zero_pointJ÷output_0.pb000066400000000000000000000000311511334557700350720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float16/test_data_set_0ByJ Ētws'‡Ētws'‡onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/000077500000000000000000000000001511334557700300115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/model.onnx000066400000000000000000000006001511334557700320110ustar00rootroot00000000000000  backend-test:į ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul"test_qlinearmatmul_3D_int8_float32Z a    Z a_scale  Z a_zero_point  Z b    Z b_scale  Z b_zero_point  Z y_scale  Z y_zero_point  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0/000077500000000000000000000000001511334557700330535ustar00rootroot00000000000000input_0.pb000066400000000000000000000000351511334557700346730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0BaJQmo„W€žQmo„W€žinput_1.pb000066400000000000000000000000231511334557700346710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0Ba_scaleJĐDØ;input_2.pb000066400000000000000000000000251511334557700346740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0B a_zero_pointJōinput_3.pb000066400000000000000000000000451511334557700346770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0BbJ´uŊ›€wx´uŊ›€wxinput_4.pb000066400000000000000000000000231511334557700346740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0Bb_scaleJ°į;input_5.pb000066400000000000000000000000251511334557700346770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000231511334557700346760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0By_scaleJO/<input_7.pb000066400000000000000000000000251511334557700347010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0B y_zero_pointJ÷output_0.pb000066400000000000000000000000311511334557700350700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_int8_float32/test_data_set_0ByJ Ēuxs'‡Ēuxs'‡onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/000077500000000000000000000000001511334557700302005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/model.onnx000066400000000000000000000006011511334557700322010ustar00rootroot00000000000000  backend-test:č ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul#test_qlinearmatmul_3D_uint8_float16Z a    Z a_scale   Z a_zero_point  Z b    Z b_scale   Z b_zero_point  Z y_scale   Z y_zero_point  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0/000077500000000000000000000000001511334557700332425ustar00rootroot00000000000000input_0.pb000066400000000000000000000000351511334557700350620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0BaJĐėîÖ˙ĐėîÖ˙input_1.pb000066400000000000000000000000211511334557700350560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0 Ba_scaleJÂinput_2.pb000066400000000000000000000000251511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0B a_zero_pointJqinput_3.pb000066400000000000000000000000451511334557700350660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0BbJ˜3ô<˙öū÷˜3ô<˙öū÷input_4.pb000066400000000000000000000000211511334557700350610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0 Bb_scaleJ8input_5.pb000066400000000000000000000000251511334557700350660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000211511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0 By_scaleJz!input_7.pb000066400000000000000000000000251511334557700350700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0B y_zero_pointJvoutput_0.pb000066400000000000000000000000311511334557700352570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float16/test_data_set_0ByJ ¨s˙B—¨s˙B—onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/000077500000000000000000000000001511334557700301765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/model.onnx000066400000000000000000000006011511334557700321770ustar00rootroot00000000000000  backend-test:č ] a a_scale a_zero_point b b_scale b_zero_point y_scale y_zero_pointy" QLinearMatMul#test_qlinearmatmul_3D_uint8_float32Z a    Z a_scale  Z a_zero_point  Z b    Z b_scale  Z b_zero_point  Z y_scale  Z y_zero_point  b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0/000077500000000000000000000000001511334557700332405ustar00rootroot00000000000000input_0.pb000066400000000000000000000000351511334557700350600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0BaJĐėîÖ˙ĐėîÖ˙input_1.pb000066400000000000000000000000231511334557700350560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0Ba_scaleJĐDØ;input_2.pb000066400000000000000000000000251511334557700350610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0B a_zero_pointJqinput_3.pb000066400000000000000000000000451511334557700350640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0BbJ˜3ô<˙öū÷˜3ô<˙öū÷input_4.pb000066400000000000000000000000231511334557700350610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0Bb_scaleJ°į;input_5.pb000066400000000000000000000000251511334557700350640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0B b_zero_pointJrinput_6.pb000066400000000000000000000000231511334557700350630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0By_scaleJO/<input_7.pb000066400000000000000000000000251511334557700350660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0B y_zero_pointJvoutput_0.pb000066400000000000000000000000311511334557700352550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_qlinearmatmul_3D_uint8_float32/test_data_set_0ByJ ¨s˙B—¨s˙B—onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/000077500000000000000000000000001511334557700253475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/model.onnx000066400000000000000000000002521511334557700273520ustar00rootroot00000000000000  backend-test:‘ - x y_scale y_zero_pointy"QuantizeLineartest_quantizelinearZ x  Z y_scale Z y_zero_point b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/test_data_set_0/000077500000000000000000000000001511334557700304115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/test_data_set_0/input_0.pb000066400000000000000000000000411511334557700323050ustar00rootroot00000000000000BxJ@@@zD~ÃzÄonnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/test_data_set_0/input_1.pb000066400000000000000000000000211511334557700323040ustar00rootroot00000000000000By_scaleJ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/test_data_set_0/input_2.pb000066400000000000000000000000231511334557700323070ustar00rootroot00000000000000B y_zero_pointJ€onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear/test_data_set_0/output_0.pb000066400000000000000000000000171511334557700325110ustar00rootroot00000000000000ByJ€‚˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear_axis/000077500000000000000000000000001511334557700263735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear_axis/model.onnx000066400000000000000000000003171511334557700304000ustar00rootroot00000000000000  backend-test:ļ - x y_scale y_zero_pointy"QuantizeLineartest_quantizelinear_axisZ x     Z y_scale  Z y_zero_point  b y     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear_axis/test_data_set_0/000077500000000000000000000000001511334557700314355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_quantizelinear_axis/test_data_set_0/input_0.pb000066400000000000000000000001271511334557700333360ustar00rootroot00000000000000BxJH"à 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test_data_set_0/000077500000000000000000000000001511334557700374505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_example_expandedinput_0.pb000066400000000000000000000001001511334557700413400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_example_expanded/test_data_set_0BdataJ0€?@@@€@ @Ā@ā@AA A0A@Ainput_1.pb000066400000000000000000000000241511334557700413460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_example_expanded/test_data_set_0BaxesJ˙˙˙˙˙˙˙˙output_0.pb000066400000000000000000000000531511334557700415500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_example_expanded/test_data_set_0BreducedJŊ@ @‘íų@*A BWA;‚Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random/000077500000000000000000000000001511334557700324425ustar00rootroot00000000000000model.onnx000066400000000000000000000003111511334557700343620ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random backend-test:° 0 data axesreduced"ReduceL2* keepdims -test_reduce_l2_negative_axes_keep_dims_randomZ data    Z axes  b reduced    B test_data_set_0/000077500000000000000000000000001511334557700354255ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_randominput_0.pb000066400000000000000000000001001511334557700373150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random/test_data_set_0BdataJ0Öėy? ¸‰@‰@IÍe?—qÃŋ•ž:@ØÆŸŋŧú@A_Aâ1Ā;´ē@&ņ?input_1.pb000066400000000000000000000000241511334557700373230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random/test_data_set_0BaxesJ˙˙˙˙˙˙˙˙output_0.pb000066400000000000000000000000531511334557700375250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random/test_data_set_0BreducedJV8@5‰@ÄR@‚åũ@ ũAžģ@test_reduce_l2_negative_axes_keep_dims_random_expanded/000077500000000000000000000000001511334557700342335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000017351511334557700362450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random_expanded backend-test:Ä i data dataTReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_data_square"Mul: ė TReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_data_square axesSReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sum_square" ReduceSum* keepdims * noop_with_empty_axes : Á SReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sum_squareWReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sum_square_dbl"Cast* to : ° WReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sum_square_dblMReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sqrt"Sqrt: j MReduceL2_test_reduce_l2_negative_axes_keep_dims_random_expanded_function_sqrt datareduced"CastLike:6test_reduce_l2_negative_axes_keep_dims_random_expandedZ data    Z axes  b reduced    B test_data_set_0/000077500000000000000000000000001511334557700372755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random_expandedinput_0.pb000066400000000000000000000001001511334557700411650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random_expanded/test_data_set_0BdataJ0Öėy? ¸‰@‰@IÍe?—qÃŋ•ž:@ØÆŸŋŧú@A_Aâ1Ā;´ē@&ņ?input_1.pb000066400000000000000000000000241511334557700411730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random_expanded/test_data_set_0BaxesJ˙˙˙˙˙˙˙˙output_0.pb000066400000000000000000000000531511334557700413750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_l2_negative_axes_keep_dims_random_expanded/test_data_set_0BreducedJV8@5‰@ÄR@‚åũ@ ũAžģ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_asc_axes/000077500000000000000000000000001511334557700271565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_asc_axes/model.onnx000066400000000000000000000002641511334557700311640ustar00rootroot00000000000000 backend-test:› 4 data axesreduced" ReduceLogSum* keepdims test_reduce_log_sum_asc_axesZ data    Z axes  b reduced  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_asc_axes/test_data_set_0/000077500000000000000000000000001511334557700322205ustar00rootroot00000000000000input_0.pb000066400000000000000000000004011511334557700340350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_asc_axes/test_data_set_0BdataJđėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_default/test_data_set_0/000077500000000000000000000000001511334557700320565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_default/test_data_set_0/input_0.pb000066400000000000000000000004011511334557700337520ustar00rootroot00000000000000BdataJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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test_data_set_0/000077500000000000000000000000001511334557700341615ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_desc_axes_expandedinput_0.pb000066400000000000000000000004011511334557700360550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_desc_axes_expanded/test_data_set_0BdataJđ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>input_1.pb000066400000000000000000000000341511334557700360600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_desc_axes_expanded/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000000331511334557700362570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_desc_axes_expanded/test_data_set_0BreducedJ ! 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#@onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_do_not_keepdims_example/000077500000000000000000000000001511334557700331225ustar00rootroot00000000000000model.onnx000066400000000000000000000003161511334557700350470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_do_not_keepdims_example backend-test:ĩ 7 data axesreduced"ReduceLogSumExp* keepdims /test_reduce_log_sum_exp_do_not_keepdims_exampleZ data     Z axes  b reduced    B test_data_set_0/000077500000000000000000000000001511334557700361055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_do_not_keepdims_exampleinput_0.pb000066400000000000000000000001601511334557700400030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_do_not_keepdims_example/test_data_set_0 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VReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_data_doubleSReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_data_exp"Exp: î SReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_data_exp axesVReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_reduced_sum" ReduceSum* keepdims * noop_with_empty_axes : ē VReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_reduced_sumYReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_reduced_double"Log: v YReduceLogSumExp_test_reduce_log_sum_exp_keepdims_example_expanded_function_reduced_double datareduced"CastLike:1test_reduce_log_sum_exp_keepdims_example_expandedZ data     Z axes  b reduced     B test_data_set_0/000077500000000000000000000000001511334557700364135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_keepdims_example_expandedinput_0.pb000066400000000000000000000001601511334557700403110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_keepdims_example_expanded/test_data_set_0 BdataJ`@đ?4@@>@đ?D@@€K@đ?N@@input_1.pb000066400000000000000000000000241511334557700403110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_keepdims_example_expanded/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000001031511334557700405070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_keepdims_example_expanded/test_data_set_0 BreducedJ0×!4@$Ø÷W@ĘdÕ|D@$Ø÷W@-m 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1ä?test_reduce_log_sum_exp_negative_axes_keepdims_example/000077500000000000000000000000001511334557700344035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000003311511334557700364040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_negative_axes_keepdims_example backend-test:Ā 7 data axesreduced"ReduceLogSumExp* keepdims 6test_reduce_log_sum_exp_negative_axes_keepdims_exampleZ data     Z axes  b reduced     B test_data_set_0/000077500000000000000000000000001511334557700374455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_negative_axes_keepdims_exampleinput_0.pb000066400000000000000000000001601511334557700413430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_log_sum_exp_negative_axes_keepdims_example/test_data_set_0 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BreducedJonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdim_example/000077500000000000000000000000001511334557700323655ustar00rootroot00000000000000model.onnx000066400000000000000000000002571511334557700343160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdim_example backend-test:– + datareduced" ReduceMax* keepdims ,test_reduce_max_default_axes_keepdim_exampleZ data    b reduced    B test_data_set_0/000077500000000000000000000000001511334557700353505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdim_exampleinput_0.pb000066400000000000000000000001001511334557700372400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdim_example/test_data_set_0BdataJ0 @€? A@đA€? B@\B€?pB@output_0.pb000066400000000000000000000000271511334557700374510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdim_example/test_data_set_0BreducedJpBonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdims_random/000077500000000000000000000000001511334557700323755ustar00rootroot00000000000000model.onnx000066400000000000000000000002571511334557700343260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdims_random backend-test:– + datareduced" ReduceMax* keepdims ,test_reduce_max_default_axes_keepdims_randomZ data    b reduced    B test_data_set_0/000077500000000000000000000000001511334557700353605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdims_randominput_0.pb000066400000000000000000000001001511334557700372500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdims_random/test_data_set_0BdataJ0Öėy? ¸‰@‰@IÍe?—qÃŋ•ž:@ØÆŸŋŧú@A_Aâ1Ā;´ē@&ņ?output_0.pb000066400000000000000000000000271511334557700374610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_default_axes_keepdims_random/test_data_set_0BreducedJA_Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_example/000077500000000000000000000000001511334557700313665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_example/model.onnx000066400000000000000000000003001511334557700333630ustar00rootroot00000000000000 backend-test:§ 1 data axesreduced" ReduceMax* keepdims 'test_reduce_max_do_not_keepdims_exampleZ data    Z axes  b reduced   B test_data_set_0/000077500000000000000000000000001511334557700343515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_exampleinput_0.pb000066400000000000000000000001001511334557700362410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_example/test_data_set_0BdataJ0 @€? A@đA€? B@\B€?pB@input_1.pb000066400000000000000000000000241511334557700362470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_example/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000000511511334557700364470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_example/test_data_set_0BreducedJ A@ B@pB@onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_random/000077500000000000000000000000001511334557700312135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_random/model.onnx000066400000000000000000000002771511334557700332250ustar00rootroot00000000000000 backend-test:Ļ 1 data axesreduced" ReduceMax* keepdims &test_reduce_max_do_not_keepdims_randomZ data    Z axes  b reduced   B test_data_set_0/000077500000000000000000000000001511334557700341765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_randominput_0.pb000066400000000000000000000001001511334557700360660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_random/test_data_set_0BdataJ0Öėy? ¸‰@‰@IÍe?—qÃŋ•ž:@ØÆŸŋŧú@A_Aâ1Ā;´ē@&ņ?input_1.pb000066400000000000000000000000241511334557700360740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_random/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000000511511334557700362740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_do_not_keepdims_random/test_data_set_0BreducedJ‰@ ¸‰@ØÆŸŋŧú@A_A&ņ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/000077500000000000000000000000001511334557700265215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/model.onnx000066400000000000000000000002661511334557700305310ustar00rootroot00000000000000  backend-test: 1 data axesreduced" ReduceMax* keepdims test_reduce_max_empty_setZ data    Z axes  b reduced    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/test_data_set_0/000077500000000000000000000000001511334557700315635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/test_data_set_0/input_0.pb000066400000000000000000000000201511334557700334540ustar00rootroot00000000000000BdataJonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/test_data_set_0/input_1.pb000066400000000000000000000000241511334557700334610ustar00rootroot00000000000000BaxesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_empty_set/test_data_set_0/output_0.pb000066400000000000000000000000631511334557700336640ustar00rootroot00000000000000BreducedJ €˙€˙€˙€˙€˙€˙€˙€˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/000077500000000000000000000000001511334557700300245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/model.onnx000066400000000000000000000002751511334557700320340ustar00rootroot00000000000000 backend-test:¤ 1 data axesreduced" ReduceMax* keepdims  test_reduce_max_keepdims_exampleZ data    Z axes  b reduced    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/test_data_set_0/000077500000000000000000000000001511334557700330665ustar00rootroot00000000000000input_0.pb000066400000000000000000000001001511334557700346770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/test_data_set_0BdataJ0 @€? A@đA€? B@\B€?pB@input_1.pb000066400000000000000000000000241511334557700347050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000000531511334557700351070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_example/test_data_set_0BreducedJ A@ B@pB@onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/000077500000000000000000000000001511334557700276515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/model.onnx000066400000000000000000000002741511334557700316600ustar00rootroot00000000000000 backend-test:Ŗ 1 data axesreduced" ReduceMax* keepdims test_reduce_max_keepdims_randomZ data    Z axes  b reduced    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/test_data_set_0/000077500000000000000000000000001511334557700327135ustar00rootroot00000000000000input_0.pb000066400000000000000000000001001511334557700345240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/test_data_set_0BdataJ0Öėy? ¸‰@‰@IÍe?—qÃŋ•ž:@ØÆŸŋŧú@A_Aâ1Ā;´ē@&ņ?input_1.pb000066400000000000000000000000241511334557700345320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/test_data_set_0BaxesJoutput_0.pb000066400000000000000000000000531511334557700347340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_keepdims_random/test_data_set_0BreducedJ‰@ ¸‰@ØÆŸŋŧú@A_A&ņ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_negative_axes_keepdims_example/000077500000000000000000000000001511334557700327265ustar00rootroot00000000000000model.onnx000066400000000000000000000003131511334557700346500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_negative_axes_keepdims_example backend-test:˛ 1 data axesreduced" ReduceMax* keepdims .test_reduce_max_negative_axes_keepdims_exampleZ data    Z axes  b reduced    B test_data_set_0/000077500000000000000000000000001511334557700357115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_negative_axes_keepdims_exampleinput_0.pb000066400000000000000000000001001511334557700376010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_reduce_max_negative_axes_keepdims_example/test_data_set_0BdataJ0 @€? A@đA€? 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A`A@@ā@0ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_reversesequence_time/test_data_set_0/input_1.pb000066400000000000000000000000651511334557700335030ustar00rootroot00000000000000B sequence_lensJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_reversesequence_time/test_data_set_0/output_0.pb000066400000000000000000000001131511334557700336750ustar00rootroot00000000000000ByJ@@@Ā@A@A@ @APA€?€@ A`Aā@0ApAonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/000077500000000000000000000000001511334557700275545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/model.onnx000066400000000000000000000002431511334557700315570ustar00rootroot00000000000000  backend-test:Š ( X WY"RMSNormalization* axis test_rms_normalization_2d_axis0Z X   Z W   b Y   B 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Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋoutput_0.pb000066400000000000000000000000731511334557700346410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/test_data_set_0BYJ0§?jā'=Î;ģ>Ķ!!?ēR@Ķ,>ß1€>éÜŪ=ˆ%c>‘Ug>rŠÖ=.–hŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/000077500000000000000000000000001511334557700314245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/model.onnx000066400000000000000000000054651511334557700334420ustar00rootroot00000000000000  backend-test:œ sORMSNormalization_test_rms_normalization_2d_axis0_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ° ORMSNormalization_test_rms_normalization_2d_axis0_expanded_function_FloatEpsilonJRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_Epsilon"Cast* to : W XIRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XShape"Shape: œ IRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XShapeGRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_Rank"Size: hGRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_Axis"Constant* value*: : Ą GRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_AxisJRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_PosAxis"Identity: gFRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_One"Constant* value*: : ĩ JRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_PosAxis GRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_Rank FRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_OneMRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_ReduceAxes"Range: ] XERMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XU"Cast* to : â ERMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XU ERMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XUKRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XSquared"Mul: û KRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XSquared MRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_ReduceAxesORMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XSquaredMean" ReduceMean: ú ORMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XSquaredMean JRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_EpsilonTRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_MeanSquareEpsilon"Add: Ļ TRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_MeanSquareEpsilonFRMSNormalization_test_rms_normalization_2d_axis0_expanded_function_RMS"Sqrt: å ERMSNormalization_test_rms_normalization_2d_axis0_expanded_function_XU 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@ž1例Üũ?uԁŋĪĸĸžHÆ!=äî*žQ"&?i2–ŊÔđŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis1_expanded/000077500000000000000000000000001511334557700314255ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis1_expanded/model.onnx000066400000000000000000000054611511334557700334370ustar00rootroot00000000000000  backend-test:˜ sORMSNormalization_test_rms_normalization_2d_axis1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : ° ORMSNormalization_test_rms_normalization_2d_axis1_expanded_function_FloatEpsilonJRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_Epsilon"Cast* to : W XIRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XShape"Shape: œ IRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XShapeGRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_Rank"Size: hGRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_Axis"Constant* value*: : Ą GRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_AxisJRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_PosAxis"Identity: gFRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_One"Constant* value*: : ĩ JRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_PosAxis GRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_Rank FRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_OneMRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_ReduceAxes"Range: ] XERMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XU"Cast* to : â ERMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XU ERMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XUKRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XSquared"Mul: û KRMSNormalization_test_rms_normalization_2d_axis1_expanded_function_XSquared 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ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000311511334557700364550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/test_data_set_0BWJü6†ŋ&ÃĩŋgÚŋŗų?output_0.pb000066400000000000000000000000731511334557700366630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/test_data_set_0BYJ0/Ž›ŋûLŋžŽŒŋˆö7@{\ØŋvT™?įŗŋ~‚žÜŽ>‘4DŋãiĨžÕĒn@onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/000077500000000000000000000000001511334557700334465ustar00rootroot00000000000000model.onnx000066400000000000000000000063221511334557700353760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded  backend-test:š }YRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ä YRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_FloatEpsilonTRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Epsilon"Cast* to : a XSRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XShape"Shape: ° SRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XShapeQRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Rank"Size: {QRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Axis"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ƒ QRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Rank QRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_AxisTRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_PosAxis"Add: qPRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_One"Constant* value*: : Ũ TRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_PosAxis QRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Rank PRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_OneWRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_ReduceAxes"Range: g XORMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XU"Cast* to : € ORMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XU ORMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XUURMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XSquared"Mul: ™ URMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XSquared WRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_ReduceAxesYRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XSquaredMean" ReduceMean: ˜ YRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XSquaredMean TRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Epsilon^RMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_MeanSquareEpsilon"Add: ē ^RMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_MeanSquareEpsilonPRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_RMS"Sqrt: ƒ ORMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_XU PRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_RMSWRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_Normalized"Div: Æ WRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_NormalizedXRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_NormalizedT"Cast* to : g XRMSNormalization_test_rms_normalization_2d_axis_negative_1_expanded_function_NormalizedT WY"Mul:2test_rms_normalization_2d_axis_negative_1_expandedZ X   Z W  b Y   B test_data_set_0/000077500000000000000000000000001511334557700364315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expandedinput_0.pb000066400000000000000000000000731511334557700403320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000311511334557700403250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/test_data_set_0BWJü6†ŋ&ÃĩŋgÚŋŗų?output_0.pb000066400000000000000000000000731511334557700405330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/test_data_set_0BYJ0/Ž›ŋûLŋžŽŒŋˆö7@{\ØŋvT™?įŗŋ~‚žÜŽ>‘4DŋãiĨžÕĒn@onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/000077500000000000000000000000001511334557700315775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/model.onnx000066400000000000000000000002661511334557700336070ustar00rootroot00000000000000  backend-test: 1 X WY"RMSNormalization* axisū˙˙˙˙˙˙˙˙ )test_rms_normalization_2d_axis_negative_2Z X   Z W   b Y   B test_data_set_0/000077500000000000000000000000001511334557700345625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2input_0.pb000066400000000000000000000000731511334557700364630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000731511334557700364640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/test_data_set_0BWJ0ŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >output_0.pb000066400000000000000000000000731511334557700366640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/test_data_set_0BYJ0‹ĩW@qĶúžâj=vČ´ž…7@ššŋŒĘũ=čYEŊ@ú=ÜC/ŋŒĘ,ŊūC>onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded/000077500000000000000000000000001511334557700334475ustar00rootroot00000000000000model.onnx000066400000000000000000000063261511334557700354030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded  backend-test:Ŋ }YRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_FloatEpsilon"Constant* value*"ŦÅ'7 : Ä YRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_FloatEpsilonTRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Epsilon"Cast* to : a XSRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XShape"Shape: ° SRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XShapeQRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Rank"Size: {QRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Axis"Constant* value*: ū˙˙˙˙˙˙˙˙ : ƒ QRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Rank QRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_AxisTRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_PosAxis"Add: qPRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_One"Constant* value*: : Ũ TRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_PosAxis QRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Rank PRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_OneWRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_ReduceAxes"Range: g XORMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XU"Cast* to : € ORMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XU ORMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XUURMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XSquared"Mul: ™ URMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XSquared WRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_ReduceAxesYRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XSquaredMean" ReduceMean: ˜ YRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XSquaredMean TRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Epsilon^RMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_MeanSquareEpsilon"Add: ē ^RMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_MeanSquareEpsilonPRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_RMS"Sqrt: ƒ ORMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_XU PRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_RMSWRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_Normalized"Div: Æ WRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_NormalizedXRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_NormalizedT"Cast* to : g XRMSNormalization_test_rms_normalization_2d_axis_negative_2_expanded_function_NormalizedT WY"Mul:2test_rms_normalization_2d_axis_negative_2_expandedZ X   Z W   b Y   B test_data_set_0/000077500000000000000000000000001511334557700364325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expandedinput_0.pb000066400000000000000000000000731511334557700403330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded/test_data_set_0BXJ0xĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?input_1.pb000066400000000000000000000000731511334557700403340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded/test_data_set_0BWJ0ŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō 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test_data_set_0/000077500000000000000000000000001511334557700342715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsiloninput_0.pb000066400000000000000000000002051511334557700361670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000002051511334557700361700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon/test_data_set_0BWJxŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000002051511334557700363700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon/test_data_set_0BYJxT!g>áę˙=¯7ŋû•jĀâZ ŋ4ž“w?gãžĸ9=ëŅŊŽn˙ŊEGÚŋA‰ŋŅĩH>LI?ž5÷ŊBØÅŋស՞Ĩ>čŖņ?ÄÖU>k°ēžb;9?ę[XŊͰŋ^¸$;ž€?Ŋ‰MŋUYážonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon_expanded/000077500000000000000000000000001511334557700331565ustar00rootroot00000000000000model.onnx000066400000000000000000000061111511334557700351020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon_expanded  backend-test:° {WRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ā WRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_FloatEpsilonRRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_Epsilon"Cast* to : _ XQRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_XShape"Shape: Ŧ QRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_XShapeORMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_Rank"Size: pORMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_Axis"Constant* value*: : ą ORMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_AxisRRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_PosAxis"Identity: oNRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_One"Constant* value*: : Õ RRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_PosAxis ORMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_Rank NRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_OneURMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_ReduceAxes"Range: e 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\RMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_MeanSquareEpsilonNRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_RMS"Sqrt: ũ MRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_XU NRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_RMSURMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_Normalized"Div:  URMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_NormalizedVRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_NormalizedT"Cast* to : e VRMSNormalization_test_rms_normalization_3d_axis0_epsilon_expanded_function_NormalizedT WY"Mul:0test_rms_normalization_3d_axis0_epsilon_expandedZ X    Z W    b Y    B 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Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000002051511334557700402400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon_expanded/test_data_set_0BYJxT!g>áę˙=¯7ŋû•jĀâZ ŋ4ž“w?gãžĸ9=ëŅŊŽn˙ŊEGÚŋA‰ŋŅĩH>LI?ž5÷ŊBØÅŋស՞Ĩ>čŖņ?ÄÖU>k°ēžb;9?ę[XŊͰŋ^¸$;ž€?Ŋ‰MŋUYážonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon/000077500000000000000000000000001511334557700313075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon/model.onnx000066400000000000000000000003061511334557700333120ustar00rootroot00000000000000  backend-test:­ ; X WY"RMSNormalization* axis * epsilonÍĖĖ= 'test_rms_normalization_3d_axis1_epsilonZ X    Z W   b Y    B test_data_set_0/000077500000000000000000000000001511334557700342725ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsiloninput_0.pb000066400000000000000000000002051511334557700361700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000001071511334557700361720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon/test_data_set_0BWJ<5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋoutput_0.pb000066400000000000000000000002051511334557700363710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon/test_data_set_0BYJxŲŌ ŋf9Ũ>u`8>Zp÷?÷}?/$ŋļ^<Ē|tžš“=ŧ•J>öXu>ęŨŋeĸZŋ_Õ={|뾔hÖŊOîĩ?g3ŊRĻs>+Œrž%¯ŗŋ¯;xƙ?ŗ–ŊŖÅ5?ÖgĀJ”DŊ}‚=>”?°ŗĢŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expanded/000077500000000000000000000000001511334557700331575ustar00rootroot00000000000000model.onnx000066400000000000000000000061051511334557700351060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expanded  backend-test:Ŧ {WRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ā WRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_FloatEpsilonRRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Epsilon"Cast* to : _ XQRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XShape"Shape: Ŧ QRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XShapeORMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Rank"Size: pORMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Axis"Constant* value*: : ą ORMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_AxisRRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_PosAxis"Identity: oNRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_One"Constant* value*: : Õ RRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_PosAxis ORMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Rank NRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_OneURMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_ReduceAxes"Range: e XMRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XU"Cast* to : ú MRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XU MRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XUSRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XSquared"Mul: “ SRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XSquared URMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_ReduceAxesWRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XSquaredMean" ReduceMean: ’ WRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XSquaredMean RRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Epsilon\RMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_MeanSquareEpsilon"Add: ļ \RMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_MeanSquareEpsilonNRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_RMS"Sqrt: ũ MRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_XU NRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_RMSURMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_Normalized"Div:  URMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_NormalizedVRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_NormalizedT"Cast* to : e VRMSNormalization_test_rms_normalization_3d_axis1_epsilon_expanded_function_NormalizedT WY"Mul:0test_rms_normalization_3d_axis1_epsilon_expandedZ X    Z W   b Y    B test_data_set_0/000077500000000000000000000000001511334557700361425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expandedinput_0.pb000066400000000000000000000002051511334557700400400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expanded/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000001071511334557700400420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expanded/test_data_set_0BWJ<5mΞyœ? FU>z?¨uļ>ûá4?G,õ>ŅÍ> ņ?^ƒŦŋAŸĸŋb*x?č(–ŋoutput_0.pb000066400000000000000000000002051511334557700402410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis1_epsilon_expanded/test_data_set_0BYJxŲŌ ŋf9Ũ>u`8>Zp÷?÷}?/$ŋļ^<Ē|tžš“=ŧ•J>öXu>ęŨŋeĸZŋ_Õ={|뾔hÖŊOîĩ?g3ŊRĻs>+Œrž%¯ŗŋ¯;xƙ?ŗ–ŊŖÅ5?ÖgĀJ”DŊ}‚=>”?°ŗĢŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/000077500000000000000000000000001511334557700313105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/model.onnx000066400000000000000000000003021511334557700333070ustar00rootroot00000000000000  backend-test:Š ; X WY"RMSNormalization* axis * epsilonÍĖĖ= 'test_rms_normalization_3d_axis2_epsilonZ X    Z W  b Y    B test_data_set_0/000077500000000000000000000000001511334557700342735ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsiloninput_0.pb000066400000000000000000000002051511334557700361710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000000351511334557700361730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/test_data_set_0BWJŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋoutput_0.pb000066400000000000000000000002051511334557700363720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/test_data_set_0BYJxīĐ>YlŠžB°7>Îé?&LŋâŋÍÚēŋË3ŊMîCž‹Ėžb#†="5÷ŋđ_Œ>ŗ G>ĨĢžžå?>fúķŋåh‘Ŋ&ļö>:0?h”ŋ´Üž]>Ī+ŋ8rŋû8ęž:/,Ŋ‚?ŊũsŲ?KQZŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/000077500000000000000000000000001511334557700331605ustar00rootroot00000000000000model.onnx000066400000000000000000000061011511334557700351030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded  backend-test:¨ {WRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ā WRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_FloatEpsilonRRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Epsilon"Cast* to : _ XQRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XShape"Shape: Ŧ QRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XShapeORMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Rank"Size: pORMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Axis"Constant* value*: : ą ORMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_AxisRRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_PosAxis"Identity: oNRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_One"Constant* value*: : Õ RRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_PosAxis ORMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Rank NRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_OneURMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_ReduceAxes"Range: e XMRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XU"Cast* to : ú MRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XU MRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XUSRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XSquared"Mul: “ SRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XSquared URMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_ReduceAxesWRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XSquaredMean" ReduceMean: ’ WRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XSquaredMean RRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Epsilon\RMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_MeanSquareEpsilon"Add: ļ \RMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_MeanSquareEpsilonNRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_RMS"Sqrt: ũ MRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_XU NRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_RMSURMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_Normalized"Div:  URMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_NormalizedVRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_NormalizedT"Cast* to : e VRMSNormalization_test_rms_normalization_3d_axis2_epsilon_expanded_function_NormalizedT WY"Mul:0test_rms_normalization_3d_axis2_epsilon_expandedZ X    Z W  b Y    B test_data_set_0/000077500000000000000000000000001511334557700361435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expandedinput_0.pb000066400000000000000000000002051511334557700400410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000000351511334557700400430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/test_data_set_0BWJŲēĀ>*šŒŋ­˛˜>ĮŠ?3Ī1ŋoutput_0.pb000066400000000000000000000002051511334557700402420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/test_data_set_0BYJxīĐ>YlŠžB°7>Îé?&LŋâŋÍÚēŋË3ŊMîCž‹Ėžb#†="5÷ŋđ_Œ>ŗ G>ĨĢžžå?>fúķŋåh‘Ŋ&ļö>:0?h”ŋ´Üž]>Ī+ŋ8rŋû8ęž:/,Ŋ‚?ŊũsŲ?KQZŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/000077500000000000000000000000001511334557700333305ustar00rootroot00000000000000model.onnx000066400000000000000000000003251511334557700352550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon  backend-test:ŧ D X WY"RMSNormalization* axis˙˙˙˙˙˙˙˙˙ * epsilonÍĖĖ= 1test_rms_normalization_3d_axis_negative_1_epsilonZ X    Z W  b Y    B test_data_set_0/000077500000000000000000000000001511334557700363135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsiloninput_0.pb000066400000000000000000000002051511334557700402110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?input_1.pb000066400000000000000000000000351511334557700402130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/test_data_set_0BWJĖ9žrĖŪž­´ė?‚,?֞Đ>output_0.pb000066400000000000000000000002051511334557700404120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/test_data_set_0BYJx%&ž5(ÛŊ}_Ž?wm?Ānī>4GQ>öęŋĒHČž°žÆŊ no>–IÕŧŋąCŋVšŲ?^Ę=Îĩ_>žPmŊJ#Aŋlhážōz>ÃΞŠj>Œļ.žī|u?ī>™ž]?ŗ6:>úMˆŧ””žũo\?đ?test_rms_normalization_3d_axis_negative_1_epsilon_expanded/000077500000000000000000000000001511334557700351215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000067541511334557700371410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded  backend-test:Ķ …aRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ô aRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_FloatEpsilon\RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Epsilon"Cast* to : i X[RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XShape"Shape: Ā [RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XShapeYRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Rank"Size: ƒYRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Axis"Constant* value*: ˙˙˙˙˙˙˙˙˙ : › YRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Rank YRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Axis\RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_PosAxis"Add: yXRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_One"Constant* value*: : ũ \RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_PosAxis YRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Rank XRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_One_RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_ReduceAxes"Range: o XWRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XU"Cast* to : ˜ WRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XU WRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XU]RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XSquared"Mul: ą ]RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XSquared _RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_ReduceAxesaRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XSquaredMean" ReduceMean: ° aRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XSquaredMean \RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_EpsilonfRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_MeanSquareEpsilon"Add: Ę fRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_MeanSquareEpsilonXRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_RMS"Sqrt: › WRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_XU XRMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_RMS_RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Normalized"Div: Ö _RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_Normalized`RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_NormalizedT"Cast* to : o `RMSNormalization_test_rms_normalization_3d_axis_negative_1_epsilon_expanded_function_NormalizedT WY"Mul::test_rms_normalization_3d_axis_negative_1_epsilon_expandedZ X    Z W  b Y    B test_data_set_0/000077500000000000000000000000001511334557700401635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expandedinput_0.pb000066400000000000000000000002051511334557700420610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded/test_data_set_0BXJxxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= 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ž?Ŧō?@â–?7>8žl‰ŋFø†?output_0.pb000066400000000000000000000002051511334557700404140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon/test_data_set_0BYJxĸÅzŋåRķŊ>(ŋ›qLĀPŒ>ķĻ>ÅĩŖŋ]ëlŊ?až=uH<œą=õœ>O7?—ūŊ>ƒ8AžÃ„‰ŋÖČ=ûķ¤ŊA"Ŋú4@ãđø>ŧ0Ē>Nq?†2@šĀ6q6=fÔã<āy­ŋAÄŖ?test_rms_normalization_3d_axis_negative_3_epsilon_expanded/000077500000000000000000000000001511334557700351235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000067641511334557700371440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon_expanded  backend-test:Û …aRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_FloatEpsilon"Constant* value*"ÍĖĖ= : Ô aRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_FloatEpsilon\RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Epsilon"Cast* to : i X[RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XShape"Shape: Ā [RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XShapeYRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Rank"Size: ƒYRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Axis"Constant* value*: ũ˙˙˙˙˙˙˙˙ : › YRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Rank YRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Axis\RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_PosAxis"Add: yXRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_One"Constant* value*: : ũ \RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_PosAxis YRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Rank XRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_One_RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_ReduceAxes"Range: o XWRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XU"Cast* to : ˜ WRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XU WRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XU]RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XSquared"Mul: ą ]RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XSquared _RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_ReduceAxesaRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XSquaredMean" ReduceMean: ° aRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XSquaredMean \RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_EpsilonfRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_MeanSquareEpsilon"Add: Ę fRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_MeanSquareEpsilonXRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_RMS"Sqrt: › WRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_XU XRMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_RMS_RMSNormalization_test_rms_normalization_3d_axis_negative_3_epsilon_expanded_function_Normalized"Div: Ö 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U=Î[?bĀ>(ũ‰=×z—ŧ‡$žIÄOž†.?ŊNĶ=× ?¤ö>B™Ā>V×ËŊĨĢŊ wŋŧöķ‰?Ū?>œXÚ=ŧŽ*?CįˆžÛ?Ŋ¨ŗ†žZK‰>7°T?ģÜ=ã;>ļM¸> Ą‰)­2?Hŧ?æ<˜?æ/m?Mqí=’¤??&ŗö>š)>ĄĮ>į:vŊßc?œK=á3X?ũ¨j>,Ų@>qûL?T?iÆ˛ŊÖ˛¨>ŪUH?MN)? \l>r˛>0B ?¨3ė>hãÜ>¤Æ?A×>XIí> Ŗô>onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_3d_input_expanded/000077500000000000000000000000001511334557700313075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_3d_input_expanded/model.onnx000066400000000000000000000156111511334557700333170ustar00rootroot00000000000000  backend-test:đ6 jGRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Zero1D"Constant* value*: : lIRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NumHeads"Constant* value*: : sGRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ˆ GRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Zero1D GRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Zero1D IRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NumHeads GRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NegOneIRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NewShape"Concat* axis : Ŗ input IRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NewShapeDRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XIn"Reshape: ´ DRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XInIRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_HeadSize"Shape* start * end : ¨ IRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_HeadSizeORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDim"Identity: iFRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1D"Constant* value*: : ô IRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_HeadSize ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NoRotateLength"Sub: Ž ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDim ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_NoRotateLengthSRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotateSplitLengths"Concat* axis : Ō DRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XIn SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotateSplitLengthsJRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XToRotateJRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : t cos_cache position_idsORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheGather"Gather: t sin_cache position_idsORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheGather"Gather: õ ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDim FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1DSRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimHalf"Div: Ā SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimHalfVRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimHalfInt"Cast* to : ” ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheGather GRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Zero1D VRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimHalfInt FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1DORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheSliced"Slice: ” ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheGather GRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Zero1D VRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_RotaryEmbedDimHalfInt FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1DORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheSliced"Slice: û ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheSliced FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1DSRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheUnsqueezed" Unsqueeze: û ORotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheSliced FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Two1DSRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheUnsqueezed" Unsqueeze: ‰ JRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XToRotateCRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X1CRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X2"Split* axis˙˙˙˙˙˙˙˙˙ * num_outputs : é SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheUnsqueezed CRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X1FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosX1"Mul: é SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheUnsqueezed CRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X2FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinX2"Mul: Ū FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosX1 FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinX2ERotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Real"Sub: é SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinCacheUnsqueezed CRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X1FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinX1"Mul: é SRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosCacheUnsqueezed CRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_X2FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosX2"Mul: ã FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_SinX1 FRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_CosX2JRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Imaginary"Add: ū ERotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_Real JRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_ImaginaryIRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XRotated"Concat* axis˙˙˙˙˙˙˙˙˙ :  IRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XRotated JRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XNoRotateHRotaryEmbedding_test_rotary_embedding_3d_input_expanded_function_XConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : ¤ 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;RotaryEmbedding_test_rotary_embedding_expanded_function_XIn@RotaryEmbedding_test_rotary_embedding_expanded_function_HeadSize"Shape* start * end : – @RotaryEmbedding_test_rotary_embedding_expanded_function_HeadSizeFRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDim"Identity: `=RotaryEmbedding_test_rotary_embedding_expanded_function_Two1D"Constant* value*: : Ų @RotaryEmbedding_test_rotary_embedding_expanded_function_HeadSize FRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimFRotaryEmbedding_test_rotary_embedding_expanded_function_NoRotateLength"Sub: ķ FRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDim FRotaryEmbedding_test_rotary_embedding_expanded_function_NoRotateLengthJRotaryEmbedding_test_rotary_embedding_expanded_function_RotateSplitLengths"Concat* axis : Ž ;RotaryEmbedding_test_rotary_embedding_expanded_function_XIn JRotaryEmbedding_test_rotary_embedding_expanded_function_RotateSplitLengthsARotaryEmbedding_test_rotary_embedding_expanded_function_XToRotateARotaryEmbedding_test_rotary_embedding_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : k cos_cache position_idsFRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheGather"Gather: k sin_cache position_idsFRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheGather"Gather: Ú FRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDim =RotaryEmbedding_test_rotary_embedding_expanded_function_Two1DJRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimHalf"Div: Ž JRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimHalfMRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimHalfInt"Cast* to : į FRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheGather >RotaryEmbedding_test_rotary_embedding_expanded_function_Zero1D MRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimHalfInt =RotaryEmbedding_test_rotary_embedding_expanded_function_Two1DFRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheSliced"Slice: į FRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheGather >RotaryEmbedding_test_rotary_embedding_expanded_function_Zero1D MRotaryEmbedding_test_rotary_embedding_expanded_function_RotaryEmbedDimHalfInt =RotaryEmbedding_test_rotary_embedding_expanded_function_Two1DFRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheSliced"Slice: ā FRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheSliced =RotaryEmbedding_test_rotary_embedding_expanded_function_Two1DJRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheUnsqueezed" Unsqueeze: ā FRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheSliced =RotaryEmbedding_test_rotary_embedding_expanded_function_Two1DJRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheUnsqueezed" Unsqueeze: î ARotaryEmbedding_test_rotary_embedding_expanded_function_XToRotate:RotaryEmbedding_test_rotary_embedding_expanded_function_X1:RotaryEmbedding_test_rotary_embedding_expanded_function_X2"Split* axis˙˙˙˙˙˙˙˙˙ * num_outputs : Î JRotaryEmbedding_test_rotary_embedding_expanded_function_CosCacheUnsqueezed :RotaryEmbedding_test_rotary_embedding_expanded_function_X1=RotaryEmbedding_test_rotary_embedding_expanded_function_CosX1"Mul: Î JRotaryEmbedding_test_rotary_embedding_expanded_function_SinCacheUnsqueezed :RotaryEmbedding_test_rotary_embedding_expanded_function_X2=RotaryEmbedding_test_rotary_embedding_expanded_function_SinX2"Mul: à =RotaryEmbedding_test_rotary_embedding_expanded_function_CosX1 =RotaryEmbedding_test_rotary_embedding_expanded_function_SinX2RotaryEmbedding_test_rotary_embedding_expanded_function_XShape"Shape: ˜ 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GRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XInLRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_HeadSize"Shape* start * end : Ž LRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_HeadSizeRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDim"Identity: lIRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1D"Constant* value*: : ũ LRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_HeadSize RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_NoRotateLength"Sub: — RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDim RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_NoRotateLengthVRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotateSplitLengths"Concat* axis : Ū GRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XIn VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotateSplitLengthsMRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XToRotateMRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : w cos_cache position_idsRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheGather"Gather: w sin_cache position_idsRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheGather"Gather: ū RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDim IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimHalf"Div: Æ VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimHalfYRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimHalfInt"Cast* to : Ŗ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheGather JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Zero1D YRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimHalfInt IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheSliced"Slice: Ŗ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheGather JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Zero1D YRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimHalfInt IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheSliced"Slice: „ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheSliced IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheUnsqueezed" Unsqueeze: „ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheSliced IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheUnsqueezed" Unsqueeze: lIRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_One1D"Constant* value*: : tQRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_AxesRotaryDim"Constant* value*: : ƒ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDim IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_One1D[RotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimInclusive"Add: Ū MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XToRotate JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Zero1D RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDim QRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_AxesRotaryDim IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DFRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X1"Slice: æ MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XToRotate IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_One1D [RotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RotaryEmbedDimInclusive QRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_AxesRotaryDim IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Two1DFRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X2"Slice: ō VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheUnsqueezed FRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X1IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosX1"Mul: ō VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheUnsqueezed FRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X2IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinX2"Mul: į IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosX1 IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinX2HRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Real"Sub: ō VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinCacheUnsqueezed FRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X1IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinX1"Mul: ō VRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosCacheUnsqueezed FRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_X2IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosX2"Mul: ė IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_SinX1 IRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_CosX2MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Imaginary"Add: ÷ HRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Real JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_NegOneRRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RealInterleave" Unsqueeze:  MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_Imaginary JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_NegOneWRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_ImaginaryInterleave" Unsqueeze: Ŧ RRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_RealInterleave WRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_ImaginaryInterleave]RotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotatedInterleavedConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Ģ MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XToRotateQRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotatedShape"Shape: ‹ ]RotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotatedInterleavedConcat QRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotatedShapeLRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotated"Reshape: Š LRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XRotated MRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XNoRotateKRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : ž KRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XConcatORotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_YTransposed" Transpose* perm@@@@ : \ inputJRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XShape"Shape: ° ORotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_YTransposed JRotaryEmbedding_test_rotary_embedding_interleaved_expanded_function_XShapeoutput"Reshape:*test_rotary_embedding_interleaved_expandedZ input     Z cos_cache  2 Z sin_cache  2 Z position_ids   b output     B 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inputKRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XIn" Transpose* perm@@@@ :  KRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XInPRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_HeadSize"Shape* start * end : ļ PRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_HeadSizeVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDim"Identity: pMRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1D"Constant* value*: : ‰ PRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_HeadSize VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_NoRotateLength"Sub: Ŗ VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDim VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_NoRotateLengthZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotateSplitLengths"Concat* axis : î KRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XIn ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotateSplitLengthsQRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XToRotateQRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : o cos_cacheVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheGather"Identity: o sin_cacheVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheGather"Identity: Š VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDim MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimHalf"Div: Î ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimHalf]RotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimHalfInt"Cast* to : ˇ VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheGather NRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Zero1D ]RotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimHalfInt MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheSliced"Slice: ˇ VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheGather NRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Zero1D ]RotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_RotaryEmbedDimHalfInt MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheSliced"Slice:  VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheSliced MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheUnsqueezed" Unsqueeze:  VRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheSliced MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheUnsqueezed" Unsqueeze: ž QRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_XToRotateJRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X1JRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X2"Split* axis˙˙˙˙˙˙˙˙˙ * num_outputs : ū ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X1MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosX1"Mul: ū ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X2MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinX2"Mul: ķ MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosX1 MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinX2LRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Real"Sub: ū ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X1MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinX1"Mul: ū ZRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_X2MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosX2"Mul: ø MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_SinX1 MRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_CosX2QRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Imaginary"Add: “ LRotaryEmbedding_test_rotary_embedding_no_position_ids_expanded_function_Real 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bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotaryEmbedDimbRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_NoRotateLength"Sub: Į bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotaryEmbedDim bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_NoRotateLengthfRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotateSplitLengths"Concat* axis : ž WRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XIn fRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotateSplitLengths]RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XToRotate]RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : { 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YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Two1DfRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosCacheUnsqueezed" Unsqueeze: ´ bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinCacheSliced YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Two1DfRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinCacheUnsqueezed" Unsqueeze: |YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_One1D"Constant* value*: : „aRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_AxesRotaryDim"Constant* value*: : ŗ bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotaryEmbedDim 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kRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RotaryEmbedDimInclusive aRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_AxesRotaryDim YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_X2"Slice: ĸ fRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_X1YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosX1"Mul: ĸ fRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_X2YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinX2"Mul: — YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosX1 YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinX2XRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Real"Sub: ĸ fRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_X1YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinX1"Mul: ĸ fRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_X2YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosX2"Mul: œ YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_SinX1 YRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_CosX2]RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Imaginary"Add: § XRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Real ZRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_NegOnebRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RealInterleave" Unsqueeze: ą ]RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_Imaginary ZRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_NegOnegRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_ImaginaryInterleave" Unsqueeze: Ü bRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_RealInterleave gRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_ImaginaryInterleavemRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotatedInterleavedConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Ë ]RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XToRotateaRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotatedShape"Shape: ģ mRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotatedInterleavedConcat aRotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotatedShape\RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotated"Reshape: ē \RotaryEmbedding_test_rotary_embedding_no_position_ids_interleaved_expanded_function_XRotated 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XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Two1DeRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_RotaryEmbedDimHalf"Div: ä eRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_RotaryEmbedDimHalfhRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_RotaryEmbedDimHalfInt"Cast* to : î aRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosCacheGather YRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Zero1D hRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_RotaryEmbedDimHalfInt XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Two1DaRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosCacheSliced"Slice: î aRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_SinCacheGather YRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Zero1D hRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_RotaryEmbedDimHalfInt XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Two1DaRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_SinCacheSliced"Slice: ą aRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosCacheSliced XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Two1DeRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosCacheUnsqueezed" Unsqueeze: ą aRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_SinCacheSliced 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URotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_X2XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosX2"Mul: ™ XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_SinX1 XRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_CosX2\RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Imaginary"Add: ´ WRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Real \RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_Imaginary[RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XRotated"Concat* axis˙˙˙˙˙˙˙˙˙ : ˇ [RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XRotated \RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XNoRotateZRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Ü ZRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XConcat^RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_YTransposed" Transpose* perm@@@@ : k inputYRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XShape"Shape: Î ^RotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_YTransposed YRotaryEmbedding_test_rotary_embedding_no_position_ids_rotary_dim_expanded_function_XShapeoutput"Reshape:9test_rotary_embedding_no_position_ids_rotary_dim_expandedZ input     Z cos_cache    Z sin_cache    b output     B 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ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000001051511334557700420730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_no_position_ids_rotary_dim_expanded/test_data_set_0B cos_cacheJ0 +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?input_2.pb000066400000000000000000000001051511334557700420740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_no_position_ids_rotary_dim_expanded/test_data_set_0B sin_cacheJ0aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7>output_0.pb000066400000000000000000000014251511334557700423010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_no_position_ids_rotary_dim_expanded/test_data_set_0BoutputJ€ ĘŊÖŊī=ĖH6?HéØ>QY%?n ā>^k?įķl?Z{‘=Ųp˛=+*žœy?Ķ"Ø>úė}?ģ†z?¨•L?Gė>ÃĐG?•žÂŧŸ]YŋwbŅ<`ø*?ڗ?’NÔ>Õs‡>.4F?…Ž—>`o8ŊāAŗ>u?N˛?cī?y™q?Ƌ.?ĖL =ÚËĀ>ڄé>Ų )>9ą*?ėŽ+?‡nW>A>Ļ|ÉŊ~ŒĮž]•=zöž>}?ßüĐ=ĘãU>R.%>LuĘ=`ĄĸģŦĀL?hÄp>ėČ">] â=7(?Õ >ŗXĻŊuĸœ>Špø>429>ƒV?>ĪÄ=?ųy? ķī>ę‡čŊđ^p;ŽéL>Öæ?ː>)ö=°Ÿ—>…'ķ=4¤,>ÎQžÍˇŽ>#q?Ë ?"á‡>–ķ?âcĀ=œEr>ūÛ)?Ā”š>ČļU?ö>8a7? -”>„–;>įa?Ü#;ßZ?ÍŠ>­5ŦH ?ƒod>\įs?žíä>=ŽX?ũ˙•ž|>Üã>ƒOĩ>ûa?MÎ?iša?ĀI1?Ģ)?!/ž˛*}?jˇ>‡Ų>–<?;<hš>ęîz>Ō„cžš?Q:Ą>ˇš >v¸˜>8é?pC?¤pž’õÕ>õōk>ô”ã>„e?1ŧ>°)ß>Ud? įJ?Œ°|žŨ‹>ŗU?…Ø6?p´?ú>‚=^?ē˜="YÛžô>’å&?uŦN?–°?TzĐ>m§=#G„ž?ß@>†Ž>•=?Įģy?î[?nė?< O¸>øT1?)_<ęđ?gÕ°=äËL>ģ—<Æ/K?ƒLe>a<Œc>‹#?÷–'?–Ĩ(>5?AÅ?-šs>Öo;ž}&¸>GYē>ŪÁ?Gé:?AˇŸ>ĸãË>HáV>onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/000077500000000000000000000000001511334557700335005ustar00rootroot00000000000000model.onnx000066400000000000000000000005401511334557700354240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim  backend-test:Į u input cos_cache sin_cache position_idsoutput"RotaryEmbedding* interleaved * rotary_embedding_dim 1test_rotary_embedding_with_interleaved_rotary_dimZ input     Z cos_cache  2 Z sin_cache  2 Z position_ids   b output     B test_data_set_0/000077500000000000000000000000001511334557700364635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_diminput_0.pb000066400000000000000000000014241511334557700403650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/test_data_set_0BinputJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000006441511334557700403710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/test_data_set_02B cos_cacheJ@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>input_2.pb000066400000000000000000000006441511334557700403720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/test_data_set_02B sin_cacheJü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?input_3.pb000066400000000000000000000001061511334557700403640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/test_data_set_0B position_idsJ0 /$ output_0.pb000066400000000000000000000014251511334557700405670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim/test_data_set_0BoutputJ€€Lü:ęķ>!>3ë?HéØ>QY%?n ā>ÃĐG?ŒÄž*>Ÿž+Ũ?ڗ?’NÔ>Õs‡>.4F?.Ú˛žVĨ?=xĪŊœ(?N˛?cī?y™q?Ƌ.?Ēä3ž•<(?h4>F—!?9ą*?ėŽ+?‡nW>A>,…<Ņū>…6>í>}?ßüĐ=ĘãU>R.%>8ë Ŋt:?>ķ˛>€”—>ėČ">] â=7(?Õ >A>džüvæ>v\<>…]@?ƒV?>ĪÄ=?ųy? ķī>)k^>#?™EË>JSœ>ː>)ö=°Ÿ—>…'ķ=÷A„žäļŧ>Œ˛Ŋ Ą?Ë ?"á‡>–ķ?âcĀ=]dŋ4H›?†öžzķū>ö>8a7? -”>„–;>{û•>i"¯>`Ɓ>Š >b…-?|>Š>­5"Ķמé…?ƒod>\įs?žíä>=ŽX?›.ŊtËT>c‘ŊoßD?ûa?MÎ?iša?ĀI1?”ž=ؐ+?Vƒ3>láž>‡Ų>–<?;<hš>`Ā‚ģĀü>å÷•žö~?ˇš >v¸˜>8é?pC?hfžDéE>Z)žĀˆ&?„e?1ŧ>°)ß>Ud?`3Ļ<Ä/R? žؙ>…Ø6?p´?ú>‚=^?dž¯žū†‰>¯F9ŋŧŅ`>uŦN?–°?TzĐ>m§=ėüŽŊt]>Jōž hV?Įģy?î[?nė?< O¸>3;>ōu?s>ʡå=äËL>ģ—<Æ/K?ƒLe>%ûžĢī>ŋĮ„=Öo?–Ĩ(>5?AÅ?-šs>žîŊ6?”>ZSžžîų?Gé:?AˇŸ>ĸãË>HáV>test_rotary_embedding_with_interleaved_rotary_dim_expanded/000077500000000000000000000000001511334557700352715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000244761511334557700373120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded  backend-test:ĨR }ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Zero1D"Constant* value*: : \RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NumHeads"Constant* value*: : †ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : € inputWRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XIn" Transpose* perm@@@@ : Ú WRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XIn\RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_HeadSize"Shape* start * end : …bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDim"Constant* value*: : |YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1D"Constant* value*: : ­ \RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_HeadSize bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimbRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NoRotateLength"Sub: Į bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDim bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NoRotateLengthfRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotateSplitLengths"Concat* axis : ž WRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XIn fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotateSplitLengths]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XToRotate]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : ‡ cos_cache position_idsbRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheGather"Gather: ‡ sin_cache position_idsbRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheGather"Gather: Ž bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDim YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DfRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimHalf"Div: æ fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimHalfiRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimHalfInt"Cast* to : ķ bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheGather ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Zero1D iRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimHalfInt YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DbRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheSliced"Slice: ķ bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheGather ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Zero1D iRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimHalfInt YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DbRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheSliced"Slice: ´ bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheSliced YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DfRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheUnsqueezed" Unsqueeze: ´ bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheSliced YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DfRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheUnsqueezed" Unsqueeze: |YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_One1D"Constant* value*: : „aRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_AxesRotaryDim"Constant* value*: : ŗ bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDim YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_One1DkRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimInclusive"Add: ž ]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XToRotate ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Zero1D bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDim aRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_AxesRotaryDim YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X1"Slice: Æ ]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XToRotate YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_One1D kRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RotaryEmbedDimInclusive aRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_AxesRotaryDim YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X2"Slice: ĸ fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X1YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosX1"Mul: ĸ fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X2YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinX2"Mul: — YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosX1 YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinX2XRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Real"Sub: ĸ fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X1YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinX1"Mul: ĸ fRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosCacheUnsqueezed VRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_X2YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosX2"Mul: œ YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_SinX1 YRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_CosX2]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Imaginary"Add: § XRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Real ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NegOnebRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RealInterleave" Unsqueeze: ą ]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_Imaginary ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_NegOnegRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_ImaginaryInterleave" Unsqueeze: Ü bRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_RealInterleave gRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_ImaginaryInterleavemRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotatedInterleavedConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Ë ]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XToRotateaRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotatedShape"Shape: ģ mRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotatedInterleavedConcat aRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotatedShape\RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotated"Reshape: ē \RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XRotated ]RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XNoRotate[RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Ū [RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XConcat_RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_YTransposed" Transpose* perm@@@@ : l inputZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XShape"Shape: Đ _RotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_YTransposed ZRotaryEmbedding_test_rotary_embedding_with_interleaved_rotary_dim_expanded_function_XShapeoutput"Reshape::test_rotary_embedding_with_interleaved_rotary_dim_expandedZ input     Z cos_cache  2 Z sin_cache  2 Z position_ids   b output     B test_data_set_0/000077500000000000000000000000001511334557700403335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expandedinput_0.pb000066400000000000000000000014241511334557700422350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded/test_data_set_0BinputJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000006441511334557700422410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded/test_data_set_02B cos_cacheJ@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>input_2.pb000066400000000000000000000006441511334557700422420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded/test_data_set_02B sin_cacheJü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?input_3.pb000066400000000000000000000001061511334557700422340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded/test_data_set_0B position_idsJ0 /$ output_0.pb000066400000000000000000000014251511334557700424370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_interleaved_rotary_dim_expanded/test_data_set_0BoutputJ€€Lü:ęķ>!>3ë?HéØ>QY%?n ā>ÃĐG?ŒÄž*>Ÿž+Ũ?ڗ?’NÔ>Õs‡>.4F?.Ú˛žVĨ?=xĪŊœ(?N˛?cī?y™q?Ƌ.?Ēä3ž•<(?h4>F—!?9ą*?ėŽ+?‡nW>A>,…<Ņū>…6>í>}?ßüĐ=ĘãU>R.%>8ë Ŋt:?>ķ˛>€”—>ėČ">] â=7(?Õ >A>džüvæ>v\<>…]@?ƒV?>ĪÄ=?ųy? ķī>)k^>#?™EË>JSœ>ː>)ö=°Ÿ—>…'ķ=÷A„žäļŧ>Œ˛Ŋ Ą?Ë ?"á‡>–ķ?âcĀ=]dŋ4H›?†öžzķū>ö>8a7? -”>„–;>{û•>i"¯>`Ɓ>Š >b…-?|>Š>­5"Ķמé…?ƒod>\įs?žíä>=ŽX?›.ŊtËT>c‘ŊoßD?ûa?MÎ?iša?ĀI1?”ž=ؐ+?Vƒ3>láž>‡Ų>–<?;<hš>`Ā‚ģĀü>å÷•žö~?ˇš >v¸˜>8é?pC?hfžDéE>Z)žĀˆ&?„e?1ŧ>°)ß>Ud?`3Ļ<Ä/R? žؙ>…Ø6?p´?ú>‚=^?dž¯žū†‰>¯F9ŋŧŅ`>uŦN?–°?TzĐ>m§=ėüŽŊt]>Jōž hV?Įģy?î[?nė?< O¸>3;>ōu?s>ʡå=äËL>ģ—<Æ/K?ƒLe>%ûžĢī>ŋĮ„=Öo?–Ĩ(>5?AÅ?-šs>žîŊ6?”>ZSžžîų?Gé:?AˇŸ>ĸãË>HáV>onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/000077500000000000000000000000001511334557700311165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/model.onnx000066400000000000000000000005001511334557700331150ustar00rootroot00000000000000  backend-test:§ a input cos_cache sin_cache position_idsoutput"RotaryEmbedding* rotary_embedding_dim %test_rotary_embedding_with_rotary_dimZ input     Z cos_cache  2 Z sin_cache  2 Z position_ids   b output     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_0/000077500000000000000000000000001511334557700341605ustar00rootroot00000000000000input_0.pb000066400000000000000000000014241511334557700360030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_0BinputJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000006441511334557700360070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_02B cos_cacheJ@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>input_2.pb000066400000000000000000000006441511334557700360100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_02B sin_cacheJü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?input_3.pb000066400000000000000000000001061511334557700360020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_0B position_idsJ0 /$ output_0.pb000066400000000000000000000014251511334557700362050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim/test_data_set_0BoutputJ€P@=Ë;B>ĩÛ>‹ô?HéØ>QY%?n ā>ē>?Η?^k?įķl?Z{‘=Ųp˛=’?ŋsēúž n?Čč?ģ†z?¨•L?Gė>ÃĐG?°3};@ŠļģLûČ=BœE?ڗ?’NÔ>Õs‡>.4F?wŌ=+ģ>N~Â>6ß!?N˛?cī?y™q?Ƌ.?ã^Üž°6š=+éV?ˆ;Î>9ą*?ėŽ+?‡nW>A>āLŊŦr =…tŽ>֒Ä>}?ßüĐ=ĘãU>R.%>@[žŊ˛,>ËU(?É߂>ėČ">] â=7(?Õ >\<*ŋŽX=.ƒV?>ĪÄ=?ųy? ķī>Ā^2> é¤>¸›?@ī>ː>)ö=°Ÿ—>…'ķ=žÔņ<Œa>ËWŽ>#Ĩ*?Ë ?"á‡>–ķ?âcĀ=|ģ=Ā•žM.I?ÄA„?ö>8a7? -”>„–;>ĻW(žŪķ­;o˙B?e÷§;b…-?|>Š>­5xų?ƒod>\įs?žíä>=ŽX?¨I<ž:*hžør>´Âļ>ûa?MÎ?iša?ĀI1?ô>(ž =ž÷g?Î4”>‡Ų>–<?;<hš>ĨMWž{ Ŧžß?`xž>ˇš >v¸˜>8é?pC?´ž”ŌžVŲE>'Â&?„e?1ŧ>°)ß>Ud?%š>ØMB=Õ?¯}Ō>…Ø6?p´?ú>‚=^?€ Ŋoš(ŋđî > &?uŦN?–°?TzĐ>m§=Ô´"žôû ŋ4Ll>/?Įģy?î[?nė?< O¸>XQŦ=Ėä.=œŪ.?!E=äËL>ģ—<Æ/K?ƒLe>  ŗž0Á=ŽļĐ>ZgP?–Ĩ(>5?AÅ?-šs>~ÂŊĀ:’žĢ’>Đú*?Gé:?AˇŸ>ĸãË>HáV>onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/000077500000000000000000000000001511334557700327665ustar00rootroot00000000000000model.onnx000066400000000000000000000156621511334557700347250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded  backend-test:™7 qNRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Zero1D"Constant* value*: : sPRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_NumHeads"Constant* value*: : zNRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_NegOne"Constant* value*: ˙˙˙˙˙˙˙˙˙ : t inputKRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XIn" Transpose* perm@@@@ :  KRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XInPRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_HeadSize"Shape* start * end : yVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDim"Constant* value*: : pMRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1D"Constant* value*: : ‰ PRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_HeadSize VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_NoRotateLength"Sub: Ŗ VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDim VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_NoRotateLengthZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotateSplitLengths"Concat* axis : î KRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XIn ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotateSplitLengthsQRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XToRotateQRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XNoRotate"Split* axis˙˙˙˙˙˙˙˙˙ : { cos_cache position_idsVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheGather"Gather: { sin_cache position_idsVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheGather"Gather: Š VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDim MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimHalf"Div: Î ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimHalf]RotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimHalfInt"Cast* to : ˇ VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheGather NRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Zero1D ]RotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimHalfInt MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheSliced"Slice: ˇ VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheGather NRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Zero1D ]RotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_RotaryEmbedDimHalfInt MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1DVRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheSliced"Slice:  VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheSliced MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheUnsqueezed" Unsqueeze:  VRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheSliced MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Two1DZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheUnsqueezed" Unsqueeze: ž QRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XToRotateJRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X1JRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X2"Split* axis˙˙˙˙˙˙˙˙˙ * num_outputs : ū ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X1MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosX1"Mul: ū ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X2MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinX2"Mul: ķ MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosX1 MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinX2LRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Real"Sub: ū ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X1MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinX1"Mul: ū ZRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosCacheUnsqueezed JRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_X2MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosX2"Mul: ø MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_SinX1 MRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_CosX2QRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Imaginary"Add: “ LRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_Real QRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_ImaginaryPRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XRotated"Concat* axis˙˙˙˙˙˙˙˙˙ : – PRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XRotated QRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XNoRotateORotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XConcat"Concat* axis˙˙˙˙˙˙˙˙˙ : Æ ORotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XConcatSRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_YTransposed" Transpose* perm@@@@ : ` inputNRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XShape"Shape: ¸ SRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_YTransposed NRotaryEmbedding_test_rotary_embedding_with_rotary_dim_expanded_function_XShapeoutput"Reshape:.test_rotary_embedding_with_rotary_dim_expandedZ input     Z cos_cache  2 Z sin_cache  2 Z position_ids   b output     B test_data_set_0/000077500000000000000000000000001511334557700357515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expandedinput_0.pb000066400000000000000000000014241511334557700376530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/test_data_set_0BinputJ€  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>input_1.pb000066400000000000000000000006441511334557700376570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/test_data_set_02B cos_cacheJ@y?Cųu?h?÷'F?’Ē>|Ļ=ęĐ>ÉÎm>×Ē>vÖZ=Ā9?<:;<ČDE?.y>yÜĸ=ˁˇ=S ,?‹A{>ķP×>š¯?M\?“:?khŠ>mŖ>/Đb=#kš>Z4†>B‹é>‡ë.?‚2?b)‘>ɅÂ>Ŗ9>ŪI?‡Ųh=in2?•XG?/G? Ķ„>pdŋ>îl?P¯‹>kāŊ>™ČI>;rë>cģ6=lŋL?W›=aŌ?7>ÛŲ?lu?D%?4Ø=ĩ]Ü>w?îB ?Ŋo.?!Ž>ô>É É>×t?>Ÿ?>~kg?Ū6 ?Kđé>wÍa?#Îę> c9? MĖ>tog?{Ĩ0?n3?øĘ§>?ŧA?æÔ"?āĮu>Jd$>PāK?ņ‹u?,‘ę>ŊJ?ļ“[?1ę> Žs?nd?ËR?ûŠh?+ÆP?Œ=#>}˙ ?“˙Ë>Ļo€=ÁŲ>=r„>“ZY?oj=äu?čōĩ>Iĸļ>input_2.pb000066400000000000000000000006441511334557700376600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/test_data_set_02B sin_cacheJü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?چ}?>^>~ŋ)?1Ō†>I,Š<%B?JŲŖ>aUÄ>ķ›?—ŋT?ô!? f_?´ Œ>ĖLL?Y>>'ęs?;˙/? Ž\>á†r?^;?¤‚>tnZ>ͨ?Ÿ:Ō< sT>`pŲ>5“ŋ>ÂYí>Z%Ž>€7?¤%]?‹´đ=õr?Ũ<>„7?VČĘ>sŋ?ļ­;>ųR>ƒâų>ßļ>&Āp?[ėC?k¨??-Zg?bŲĒ=|\ ?9 ?vAv?\”•>Ō›v>éfÍ=n—†<ĸõm?§+?Č˙H?î>>ú?ûú‚=.¤ø>=z?Ļb`?,#­>v)v?1Cm>s?!ūp?Š˜L? e!?VŅ_?Á–>]TY?+-?hßX<—Čą>=˛>,Y{?ôėô>Ēū>y´#?ˇŧ>–/ >OvR?~gB>Íå?]ŗe>°bČ=•¸\?input_3.pb000066400000000000000000000001061511334557700376520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/test_data_set_0B position_idsJ0 /$ output_0.pb000066400000000000000000000014251511334557700400550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_rotary_embedding_with_rotary_dim_expanded/test_data_set_0BoutputJ€P@=Ë;B>ĩÛ>‹ô?HéØ>QY%?n ā>ē>?Η?^k?įķl?Z{‘=Ųp˛=’?ŋsēúž n?Čč?ģ†z?¨•L?Gė>ÃĐG?°3};@ŠļģLûČ=BœE?ڗ?’NÔ>Õs‡>.4F?wŌ=+ģ>N~Â>6ß!?N˛?cī?y™q?Ƌ.?ã^Üž°6š=+éV?ˆ;Î>9ą*?ėŽ+?‡nW>A>āLŊŦr =…tŽ>֒Ä>}?ßüĐ=ĘãU>R.%>@[žŊ˛,>ËU(?É߂>ėČ">] â=7(?Õ >\<*ŋŽX=.ƒV?>ĪÄ=?ųy? ķī>Ā^2> é¤>¸›?@ī>ː>)ö=°Ÿ—>…'ķ=žÔņ<Œa>ËWŽ>#Ĩ*?Ë ?"á‡>–ķ?âcĀ=|ģ=Ā•žM.I?ÄA„?ö>8a7? -”>„–;>ĻW(žŪķ­;o˙B?e÷§;b…-?|>Š>­5xų?ƒod>\įs?žíä>=ŽX?¨I<ž:*hžør>´Âļ>ûa?MÎ?iša?ĀI1?ô>(ž =ž÷g?Î4”>‡Ų>–<?;<hš>ĨMWž{ Ŧžß?`xž>ˇš >v¸˜>8é?pC?´ž”ŌžVŲE>'Â&?„e?1ŧ>°)ß>Ud?%š>ØMB=Õ?¯}Ō>…Ø6?p´?ú>‚=^?€ Ŋoš(ŋđî > &?uŦN?–°?TzĐ>m§=Ô´"žôû ŋ4Ll>/?Įģy?î[?nė?< O¸>XQŦ=Ėä.=œŪ.?!E=äËL>ģ—<Æ/K?ƒLe>  ŗž0Á=ŽļĐ>ZgP?–Ĩ(>5?AÅ?-šs>~ÂŊĀ:’žĢ’>Đú*?Gé:?AˇŸ>ĸãË>HáV>onnx-onnx-bca0315/onnx/backend/test/data/node/test_round/000077500000000000000000000000001511334557700234435ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_round/model.onnx000066400000000000000000000001251511334557700254450ustar00rootroot00000000000000  backend-test:= xy"Round test_roundZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_round/test_data_set_0/000077500000000000000000000000001511334557700265055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_round/test_data_set_0/input_0.pb000066400000000000000000000001051511334557700304020ustar00rootroot00000000000000BxJ<ÍĖĖ=?fff?š™™?Ā?ffæ?33@ @ÍĖ,@ÍˌŋĀŋ33ķŋÍĖ Ā Ā333Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_round/test_data_set_0/output_0.pb000066400000000000000000000001051511334557700306030ustar00rootroot00000000000000ByJ<€?€?@@@@@@€ŋĀĀĀĀ@Āonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/000077500000000000000000000000001511334557700242155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/model.onnx000066400000000000000000000005361511334557700262250ustar00rootroot00000000000000 backend-test:Å ā initial xyz"Scan*­ body2Ą  sum_in nextsum_out"Add  sum_outscan_out"Identity scan_bodyZ sum_in  Z next  b sum_out  b scan_out   * num_scan_inputs test_scan9_sumZ initial  Z x   b y  b z   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/test_data_set_0/000077500000000000000000000000001511334557700272575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/test_data_set_0/input_0.pb000066400000000000000000000000271511334557700311570ustar00rootroot00000000000000BinitialJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/test_data_set_0/input_1.pb000066400000000000000000000000431511334557700311560ustar00rootroot00000000000000BxJ€?@@@€@ @Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/test_data_set_0/output_0.pb000066400000000000000000000000211511334557700313520ustar00rootroot00000000000000ByJA@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan9_sum/test_data_set_0/output_1.pb000066400000000000000000000000431511334557700313570ustar00rootroot00000000000000BzJ€?@€@Ā@A@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/000077500000000000000000000000001511334557700241245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/model.onnx000066400000000000000000000005571511334557700261370ustar00rootroot00000000000000 backend-test:Ö â initial xyz"Scan*­ body2Ą  sum_in nextsum_out"Add  sum_outscan_out"Identity scan_bodyZ sum_in  Z next  b sum_out  b scan_out   * num_scan_inputs  test_scan_sumZ initial   Z x    b y   b z    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/test_data_set_0/000077500000000000000000000000001511334557700271665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/test_data_set_0/input_0.pb000066400000000000000000000000311511334557700310610ustar00rootroot00000000000000BinitialJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/test_data_set_0/input_1.pb000066400000000000000000000000451511334557700310670ustar00rootroot00000000000000BxJ€?@@@€@ @Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/test_data_set_0/output_0.pb000066400000000000000000000000231511334557700312630ustar00rootroot00000000000000ByJA@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scan_sum/test_data_set_0/output_1.pb000066400000000000000000000000451511334557700312700ustar00rootroot00000000000000BzJ€?@€@Ā@A@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/000077500000000000000000000000001511334557700277345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/model.onnx000066400000000000000000000003301511334557700317340ustar00rootroot00000000000000 backend-test:ŋ 9 data indices updatesy"ScatterElements* axis test_scatter_elements_with_axisZ data   Z indices   Z updates   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/test_data_set_0/000077500000000000000000000000001511334557700327765ustar00rootroot00000000000000input_0.pb000066400000000000000000000000421511334557700346140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/test_data_set_0BdataJ€?@@@€@ @input_1.pb000066400000000000000000000000411511334557700346140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/test_data_set_0BindicesJinput_2.pb000066400000000000000000000000311511334557700346140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/test_data_set_0BupdatesJÍˌ?ff@output_0.pb000066400000000000000000000000371511334557700350210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_axis/test_data_set_0ByJ€?Íˌ?@@ff@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices/000077500000000000000000000000001511334557700324405ustar00rootroot00000000000000model.onnx000066400000000000000000000003721511334557700343670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices backend-test:á N data indices updatesy"ScatterElements* axis * reduction"add ,test_scatter_elements_with_duplicate_indicesZ data   Z indices   Z updates   b y   B test_data_set_0/000077500000000000000000000000001511334557700354235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indicesinput_0.pb000066400000000000000000000000421511334557700373200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices/test_data_set_0BdataJ€?@@@€@ @input_1.pb000066400000000000000000000000411511334557700373200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices/test_data_set_0BindicesJinput_2.pb000066400000000000000000000000311511334557700373200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices/test_data_set_0BupdatesJÍˌ?ff@output_0.pb000066400000000000000000000000371511334557700375250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_duplicate_indices/test_data_set_0ByJ€?ffĻ@@@€@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/000077500000000000000000000000001511334557700322705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/model.onnx000066400000000000000000000003441511334557700342750ustar00rootroot00000000000000 backend-test:Ë 9 data indices updatesy"ScatterElements* axis +test_scatter_elements_with_negative_indicesZ data   Z indices   Z updates   b y   B test_data_set_0/000077500000000000000000000000001511334557700352535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indicesinput_0.pb000066400000000000000000000000421511334557700371500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/test_data_set_0BdataJ€?@@@€@ @input_1.pb000066400000000000000000000000411511334557700371500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/test_data_set_0BindicesJũ˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000311511334557700371500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/test_data_set_0BupdatesJÍˌ?ff@output_0.pb000066400000000000000000000000371511334557700373550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_negative_indices/test_data_set_0ByJ€?Íˌ?ff@€@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/000077500000000000000000000000001511334557700316315ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/model.onnx000066400000000000000000000003661511334557700336420ustar00rootroot00000000000000 backend-test:Ũ N data indices updatesy"ScatterElements* axis * reduction"max (test_scatter_elements_with_reduction_maxZ data   Z indices   Z updates   b y   B test_data_set_0/000077500000000000000000000000001511334557700346145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_maxinput_0.pb000066400000000000000000000000421511334557700365110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/test_data_set_0BdataJ€?@@@€@ @input_1.pb000066400000000000000000000000411511334557700365110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/test_data_set_0BindicesJinput_2.pb000066400000000000000000000000311511334557700365110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/test_data_set_0BupdatesJÍˌ?ff@output_0.pb000066400000000000000000000000371511334557700367160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_max/test_data_set_0ByJ€?ff@@@€@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/000077500000000000000000000000001511334557700316275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/model.onnx000066400000000000000000000003661511334557700336400ustar00rootroot00000000000000 backend-test:Ũ N data indices updatesy"ScatterElements* axis * reduction"min (test_scatter_elements_with_reduction_minZ data   Z indices   Z updates   b y   B test_data_set_0/000077500000000000000000000000001511334557700346125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_mininput_0.pb000066400000000000000000000000421511334557700365070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/test_data_set_0BdataJ€?@@@€@ @input_1.pb000066400000000000000000000000411511334557700365070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/test_data_set_0BindicesJinput_2.pb000066400000000000000000000000311511334557700365070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/test_data_set_0BupdatesJÍˌ?ff@output_0.pb000066400000000000000000000000371511334557700367140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_with_reduction_min/test_data_set_0ByJ€?Íˌ?@@€@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/000077500000000000000000000000001511334557700304645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/model.onnx000066400000000000000000000003161511334557700324700ustar00rootroot00000000000000 backend-test:ĩ , data indices updatesy"ScatterElements"test_scatter_elements_without_axisZ data   Z indices   Z updates   b y   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/test_data_set_0/000077500000000000000000000000001511334557700335265ustar00rootroot00000000000000input_0.pb000066400000000000000000000000621511334557700353460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/test_data_set_0BdataJ$input_1.pb000066400000000000000000000001011511334557700353410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/test_data_set_0BindicesJ0input_2.pb000066400000000000000000000000511511334557700353460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/test_data_set_0BupdatesJ€?Íˌ?š™™?@ff@ÍĖ @output_0.pb000066400000000000000000000000571511334557700355530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_elements_without_axis/test_data_set_0ByJ$@Íˌ?€?ÍĖ @ff@š™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/000077500000000000000000000000001511334557700260405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/model.onnx000066400000000000000000000003071511334557700300440ustar00rootroot00000000000000 backend-test:Ž 1 data indices updatesy"Scatter* axis test_scatter_with_axisZ data   Z indices   Z updates   b y   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/test_data_set_0/000077500000000000000000000000001511334557700311025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/test_data_set_0/input_0.pb000066400000000000000000000000421511334557700327770ustar00rootroot00000000000000BdataJ€?@@@€@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700327770ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/test_data_set_0/input_2.pb000066400000000000000000000000311511334557700327770ustar00rootroot00000000000000BupdatesJÍˌ?ff@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_with_axis/test_data_set_0/output_0.pb000066400000000000000000000000371511334557700332040ustar00rootroot00000000000000ByJ€?Íˌ?@@ff@ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/000077500000000000000000000000001511334557700265705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/model.onnx000066400000000000000000000002751511334557700306000ustar00rootroot00000000000000 backend-test:¤ $ data indices updatesy"Scattertest_scatter_without_axisZ data   Z indices   Z updates   b y   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/test_data_set_0/000077500000000000000000000000001511334557700316325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/test_data_set_0/input_0.pb000066400000000000000000000000621511334557700335310ustar00rootroot00000000000000BdataJ$onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/test_data_set_0/input_1.pb000066400000000000000000000001011511334557700335240ustar00rootroot00000000000000BindicesJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/test_data_set_0/input_2.pb000066400000000000000000000000511511334557700335310ustar00rootroot00000000000000BupdatesJ€?Íˌ?š™™?@ff@ÍĖ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatter_without_axis/test_data_set_0/output_0.pb000066400000000000000000000000571511334557700337360ustar00rootroot00000000000000ByJ$@Íˌ?€?ÍĖ @ff@š™™?onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/000077500000000000000000000000001511334557700243035ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/model.onnx000066400000000000000000000003001511334557700263000ustar00rootroot00000000000000 backend-test:§ & data indices updatesy" ScatterNDtest_scatterndZ data    Z indices   Z updates    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/test_data_set_0/000077500000000000000000000000001511334557700273455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/test_data_set_0/input_0.pb000066400000000000000000000004211511334557700312430ustar00rootroot00000000000000BdataJ€€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700312420ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/test_data_set_0/input_2.pb000066400000000000000000000002241511334557700312460ustar00rootroot00000000000000BupdatesJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?€?€?€?@@@@@@@@@@@@€@€@€@€@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd/test_data_set_0/output_0.pb000066400000000000000000000004161511334557700314500ustar00rootroot00000000000000ByJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?€?€?€?@@@@@@@@@@@@€@€@€@€@Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/000077500000000000000000000000001511334557700251135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/model.onnx000066400000000000000000000003311511334557700271140ustar00rootroot00000000000000 backend-test:Ā ; data indices updatesy" ScatterND* reduction"add test_scatternd_addZ data    Z indices   Z updates    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/test_data_set_0/000077500000000000000000000000001511334557700301555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/test_data_set_0/input_0.pb000066400000000000000000000004211511334557700320530ustar00rootroot00000000000000BdataJ€€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700320520ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/test_data_set_0/input_2.pb000066400000000000000000000002241511334557700320560ustar00rootroot00000000000000BupdatesJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?€?€?€?@@@@@@@@@@@@€@€@€@€@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_add/test_data_set_0/output_0.pb000066400000000000000000000004161511334557700322600ustar00rootroot00000000000000ByJ€ā@AA APA`ApA€AAˆA€ApA€ApA`APA€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/000077500000000000000000000000001511334557700251505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/model.onnx000066400000000000000000000003311511334557700271510ustar00rootroot00000000000000 backend-test:Ā ; data indices updatesy" ScatterND* reduction"max test_scatternd_maxZ data    Z indices   Z updates    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/test_data_set_0/000077500000000000000000000000001511334557700302125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/test_data_set_0/input_0.pb000066400000000000000000000004211511334557700321100ustar00rootroot00000000000000BdataJ€€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700321070ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/test_data_set_0/input_2.pb000066400000000000000000000002241511334557700321130ustar00rootroot00000000000000BupdatesJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?€?€?€?@@@@@@@@@@@@€@€@€@€@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_max/test_data_set_0/output_0.pb000066400000000000000000000004161511334557700323150ustar00rootroot00000000000000ByJ€ @ @ @ @Ā@Ā@ā@AAā@ā@ā@AAAA€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/000077500000000000000000000000001511334557700251465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/model.onnx000066400000000000000000000003311511334557700271470ustar00rootroot00000000000000 backend-test:Ā ; data indices updatesy" ScatterND* reduction"min test_scatternd_minZ data    Z indices   Z updates    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/test_data_set_0/000077500000000000000000000000001511334557700302105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/test_data_set_0/input_0.pb000066400000000000000000000004211511334557700321060ustar00rootroot00000000000000BdataJ€€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700321050ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/test_data_set_0/input_2.pb000066400000000000000000000002241511334557700321110ustar00rootroot00000000000000BupdatesJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?€?€?€?@@@@@@@@@@@@€@€@€@€@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_min/test_data_set_0/output_0.pb000066400000000000000000000004161511334557700323130ustar00rootroot00000000000000ByJ€€?€?€?€?@@@@@@@@@@@@€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/000077500000000000000000000000001511334557700262425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/model.onnx000066400000000000000000000003361511334557700302500ustar00rootroot00000000000000 backend-test:Å ; data indices updatesy" ScatterND* reduction"mul test_scatternd_multiplyZ data    Z indices   Z updates    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/test_data_set_0/000077500000000000000000000000001511334557700313045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/test_data_set_0/input_0.pb000066400000000000000000000004211511334557700332020ustar00rootroot00000000000000BdataJ€€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700332010ustar00rootroot00000000000000BindicesJonnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/test_data_set_0/input_2.pb000066400000000000000000000002241511334557700332050ustar00rootroot00000000000000BupdatesJ€ @ @ @ @Ā@Ā@Ā@Ā@ā@ā@ā@ā@AAAA€?€?€?€?@@@@@@@@@@@@€@€@€@€@onnx-onnx-bca0315/onnx/backend/test/data/node/test_scatternd_multiply/test_data_set_0/output_0.pb000066400000000000000000000004161511334557700334070ustar00rootroot00000000000000ByJ€ @ ApA ApBB¨BĀB(CCüBŌBCĀB€BB€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?Aā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@AAā@Ā@ @€@@@@€?€?@@@€@ @Ā@ā@Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/000077500000000000000000000000001511334557700306055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/model.onnx000066400000000000000000000003471511334557700326150ustar00rootroot00000000000000 backend-test:Î Y x y wz"SoftmaxCrossEntropyLoss* ignore_index˙˙˙˙˙˙˙˙˙ * reduction"mean %test_sce_NCd1_mean_weight_negative_iiZ x    Z y   Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/test_data_set_0/000077500000000000000000000000001511334557700336475ustar00rootroot00000000000000input_0.pb000066400000000000000000000005661511334557700355000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/test_data_set_0BxJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?input_1.pb000066400000000000000000000002341511334557700354710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/test_data_set_0ByJ˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000351511334557700354710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/test_data_set_0BwJ/ŗ“>ī×Ũ>5A?eÍĘ>Æbe?output_0.pb000066400000000000000000000000131511334557700356640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii/test_data_set_0BzJ(ģÖ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded/000077500000000000000000000000001511334557700324555ustar00rootroot00000000000000model.onnx000066400000000000000000000032101511334557700343760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded backend-test:ī …WSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ž x WSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_Shape3DUSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_NCD"Reshape: Ė USoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_NCDUSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_NDC" Transpose* perm@@@ : Ë USoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_NDCWSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_LogSM" LogSoftmax* axis : Ô WSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_LogSM[SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : e xWSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_shape"Shape: ˜ [SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_LogSM_NCD WSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_shapeUSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_Log"Reshape: ą USoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index˙˙˙˙˙˙˙˙˙ :.test_sce_NCd1_mean_weight_negative_ii_expandedZ x    Z y   Z w  b z B  test_data_set_0/000077500000000000000000000000001511334557700354405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expandedinput_0.pb000066400000000000000000000005661511334557700373500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BxJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?input_1.pb000066400000000000000000000002341511334557700373410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded/test_data_set_0ByJ˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000351511334557700373410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BwJ/ŗ“>ī×Ũ>5A?eÍĘ>Æbe?output_0.pb000066400000000000000000000000131511334557700375340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_expanded/test_data_set_0BzJ(ģÖ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/000077500000000000000000000000001511334557700324705ustar00rootroot00000000000000model.onnx000066400000000000000000000004321511334557700344140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob backend-test: c x y wzlog_prob"SoftmaxCrossEntropyLoss* ignore_index˙˙˙˙˙˙˙˙˙ * reduction"mean .test_sce_NCd1_mean_weight_negative_ii_log_probZ x    Z y   Z w  b z b log_prob    B  test_data_set_0/000077500000000000000000000000001511334557700354535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_probinput_0.pb000066400000000000000000000005661511334557700373630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/test_data_set_0BxJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?input_1.pb000066400000000000000000000002341511334557700373540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/test_data_set_0ByJ˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000351511334557700373540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/test_data_set_0BwJ/ŗ“>ī×Ũ>5A?eÍĘ>Æbe?output_0.pb000066400000000000000000000000131511334557700375470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/test_data_set_0BzJ(ģÖ?output_1.pb000066400000000000000000000005751511334557700375650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob/test_data_set_0Blog_probJčÔ^ÉŋŠšÚŋĘËĐŋ"œÔŋ^ÔŋŧÖÎŋ€›×ŋ@Äŋ֙ĸŋBFéŋ%AĨŋĐŨŋĢčÆŋęĘŋŋļm Āč™ĀđĀđļŋļŦŋNčÆŋů ŋ ´ŋ¤†Īŋ›Ŋŋ^=Āđ[äŋŪĖĀõoĄŋxĖĮŋĀoėŋeäŋä#ĸŋâ‘éŋēÉŋdĀ䥨ŋÔčˇŋJFļŋI(Ģŋ7ģŋ!žÛŋčŋŋŋöŦŋƒˆũŋiœÎŋˆĨŧŋĒ´îŋę/įŋâŨŋč¯ÖŋĐøÚŋįXÚŋt‹ŋÄ ęŋ…ëŋ1˜đŋø[Đŋ#ōŋhōÍŋ;gØŋÎč¤áŋ~ŊĘŋí…ŅŋļņŋĀåŋjŗŋ™ÂâŋR~ŗŋõčÖŋãŋĸõØŋ6$ŸŋÖÄĄŋčĀŋĖ1ŪŋžŦæŋæĮĀĖBöŋōũßŋm öŋV/ŽŋŌTĀyOŧŋR¤Ķŋ"8ÍŋOÆÛŋŠ/×ŋ'Áŋ8˙ŋtest_sce_NCd1_mean_weight_negative_ii_log_prob_expanded/000077500000000000000000000000001511334557700342615ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000036471511334557700362770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded backend-test:Ž Ž`SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Đ x `SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_Shape3D^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_NCD"Reshape: Ū ^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_NCD^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Ũ ^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_NDC`SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : æ `SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_LogSMdSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : n x`SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_shape"Shape: ŗ dSoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_LogSM_NCD `SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_shape^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_Log"Reshape: v ^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_Loglog_prob"Identity: ē ^SoftmaxCrossEntropyLoss_test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index˙˙˙˙˙˙˙˙˙ :7test_sce_NCd1_mean_weight_negative_ii_log_prob_expandedZ x    Z y   Z w  b z b log_prob    B  test_data_set_0/000077500000000000000000000000001511334557700373235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob_expandedinput_0.pb000066400000000000000000000005661511334557700412330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1_mean_weight_negative_ii_log_prob_expanded/test_data_set_0BxJč  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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eSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_NCDeSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : ë eSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_NDCgSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : ô gSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_LogSMkSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : u xgSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_shape"Shape: Č kSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_LogSM_NCD gSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_shapeeSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_Log"Reshape: } eSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_Loglog_prob"Identity: ž eSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"none * ignore_indexû˙˙˙˙˙˙˙˙ :>test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob_expandedZ x      Z y     b z     b& log_prob      B  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„VSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ŧ x VSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_Shape3DTSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_NCD"Reshape: Ę TSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_NCDTSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_NDC" Transpose* perm@@@ : É TSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_NDCVSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_LogSM" LogSoftmax* axis : Ō VSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_LogSMZSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : d xVSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3_sum_weight_high_ii_expanded_function_X_shape"Shape: • 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ZSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_NDC\SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : Ū \SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_LogSM`SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : j x\SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_shape"Shape: § `SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_LogSM_NCD \SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_shapeZSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_Log"Reshape: r ZSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_mean_weight_log_prob_expanded_function_X_Loglog_prob"Identity: ˜ 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Öŋtest_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded/000077500000000000000000000000001511334557700335675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000036551511334557700356040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded backend-test:” _SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Î x _SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_Shape3D]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_NCD"Reshape: Ü ]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_NCD]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Û ]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_NDC_SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : ä _SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_LogSMcSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : m x_SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_shape"Shape: ° cSoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_LogSM_NCD _SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_shape]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_Log"Reshape: u ]SoftmaxCrossEntropyLoss_test_sce_NCd1d2d3d4d5_none_no_weight_log_prob_expanded_function_X_Loglog_prob"Identity: ˜ 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d/test_data_set_0/000077500000000000000000000000001511334557700275165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700314140ustar00rootroot00000000000000BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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@SoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_NCD@SoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_NDC" Transpose* perm@@@ : Ą @SoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_NDCBSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_LogSM" LogSoftmax* axis : Ē BSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_LogSMFSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : P xBSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_shape"Shape: Ų FSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_LogSM_NCD BSoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_shape@SoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_Log"Reshape: { @SoftmaxCrossEntropyLoss_test_sce_mean_3d_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean :test_sce_mean_3d_expandedZ x    Z y   b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_expanded/test_data_set_0/000077500000000000000000000000001511334557700313665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_expanded/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700332640ustar00rootroot00000000000000BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_expanded/test_data_set_0/input_1.pb000066400000000000000000000000731511334557700332700ustar00rootroot00000000000000ByJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_expanded/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700334620ustar00rootroot00000000000000BzJëĻØ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/000077500000000000000000000000001511334557700263375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/model.onnx000066400000000000000000000003231511334557700303410ustar00rootroot00000000000000 backend-test:ē B x yzlog_prob"SoftmaxCrossEntropyLoss* reduction"mean test_sce_mean_3d_log_probZ x    Z y   b z b log_prob    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/test_data_set_0/000077500000000000000000000000001511334557700314015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700332770ustar00rootroot00000000000000BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/test_data_set_0/input_1.pb000066400000000000000000000000731511334557700333030ustar00rootroot00000000000000ByJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700334750ustar00rootroot00000000000000BzJëĻØ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob/test_data_set_0/output_1.pb000066400000000000000000000002141511334557700335010ustar00rootroot00000000000000Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/000077500000000000000000000000001511334557700302075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/model.onnx000066400000000000000000000030441511334557700322140ustar00rootroot00000000000000 backend-test:‹ yKSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ļ x KSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_Shape3DISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_NCD"Reshape: ´ ISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_NCDISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : ŗ ISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_NDCKSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : ŧ KSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_LogSMOSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : Y xKSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_shape"Shape: ô OSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_LogSM_NCD KSoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_shapeISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_Log"Reshape: a ISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_Loglog_prob"Identity: „ ISoftmaxCrossEntropyLoss_test_sce_mean_3d_log_prob_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean :"test_sce_mean_3d_log_prob_expandedZ x    Z y   b z b log_prob    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/test_data_set_0/000077500000000000000000000000001511334557700332515ustar00rootroot00000000000000input_0.pb000066400000000000000000000002051511334557700350700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700350740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/test_data_set_0ByJ0output_0.pb000066400000000000000000000000131511334557700352660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/test_data_set_0BzJëĻØ?output_1.pb000066400000000000000000000002141511334557700352720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_3d_log_prob_expanded/test_data_set_0Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/000077500000000000000000000000001511334557700257365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/model.onnx000066400000000000000000000023441511334557700277450ustar00rootroot00000000000000 backend-test:Ë m?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ž x ?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_Shape3D=SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_NCD"Reshape: œ =SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_NCD=SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_NDC" Transpose* perm@@@ : › =SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_NDC?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_LogSM" LogSoftmax* axis : ¤ ?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_LogSMCSoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : M x?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_shape"Shape: Đ CSoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_LogSM_NCD ?SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_shape=SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_Log"Reshape: x =SoftmaxCrossEntropyLoss_test_sce_mean_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean :test_sce_mean_expandedZ x   Z y  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/test_data_set_0/000077500000000000000000000000001511334557700310005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700326770ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700326750ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_expanded/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700330740ustar00rootroot00000000000000BzJuÛĮ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/000077500000000000000000000000001511334557700257515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/model.onnx000066400000000000000000000003041511334557700277520ustar00rootroot00000000000000 backend-test:Ģ B x yzlog_prob"SoftmaxCrossEntropyLoss* reduction"mean test_sce_mean_log_probZ x   Z y  b z b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/test_data_set_0/000077500000000000000000000000001511334557700310135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700327120ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700327100ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700331070ustar00rootroot00000000000000BzJuÛĮ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob/test_data_set_0/output_1.pb000066400000000000000000000001161511334557700331140ustar00rootroot00000000000000Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700345010ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob_expanded/test_data_set_0ByJoutput_0.pb000066400000000000000000000000131511334557700347000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob_expanded/test_data_set_0BzJuÛĮ?output_1.pb000066400000000000000000000001161511334557700347050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700335510ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700337500ustar00rootroot00000000000000BzJŪųÍ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/000077500000000000000000000000001511334557700272005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/model.onnx000066400000000000000000000003021511334557700311770ustar00rootroot00000000000000 backend-test:Š M x yz"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean test_sce_mean_no_weight_ii_3dZ x    Z y   b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/test_data_set_0/000077500000000000000000000000001511334557700322425ustar00rootroot00000000000000input_0.pb000066400000000000000000000002051511334557700340610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700340650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/test_data_set_0ByJ0output_0.pb000066400000000000000000000000131511334557700342570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d/test_data_set_0BzJÔCš?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expanded/000077500000000000000000000000001511334557700310505ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expanded/model.onnx000066400000000000000000000027621511334557700330630ustar00rootroot00000000000000 backend-test:Ų }OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ž x OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_Shape3DMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_NCD"Reshape: ŧ MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_NCDMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_NDC" Transpose* perm@@@ : ģ MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_NDCOSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_LogSM" LogSoftmax* axis : Ä OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_LogSMSSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : ] xOSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_shape"Shape: € SSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_LogSM_NCD OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_shapeMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_Log"Reshape:  MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :&test_sce_mean_no_weight_ii_3d_expandedZ x    Z y   b z B  test_data_set_0/000077500000000000000000000000001511334557700340335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expandedinput_0.pb000066400000000000000000000002051511334557700357310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expanded/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700357350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expanded/test_data_set_0ByJ0output_0.pb000066400000000000000000000000131511334557700361270ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_expanded/test_data_set_0BzJÔCš?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/000077500000000000000000000000001511334557700310635ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/model.onnx000066400000000000000000000003651511334557700330730ustar00rootroot00000000000000 backend-test:Ü W x yzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean &test_sce_mean_no_weight_ii_3d_log_probZ x    Z y   b z b log_prob    B  test_data_set_0/000077500000000000000000000000001511334557700340465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_probinput_0.pb000066400000000000000000000002051511334557700357440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700357500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/test_data_set_0ByJ0output_0.pb000066400000000000000000000000131511334557700361420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/test_data_set_0BzJÔCš?output_1.pb000066400000000000000000000002141511334557700361460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob/test_data_set_0Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded/000077500000000000000000000000001511334557700327335ustar00rootroot00000000000000model.onnx000066400000000000000000000034121511334557700346600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded backend-test:ņ †XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ā x XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_Shape3DVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_NCD"Reshape: Î VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_NCDVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Í VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_NDCXSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : Ö XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_LogSM\SoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : f xXSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_shape"Shape: › \SoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_LogSM_NCD XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_shapeVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_Log"Reshape: n VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_Loglog_prob"Identity: Ļ VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_3d_log_prob_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :/test_sce_mean_no_weight_ii_3d_log_prob_expandedZ x    Z y   b z b log_prob    B  test_data_set_0/000077500000000000000000000000001511334557700357165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expandedinput_0.pb000066400000000000000000000002051511334557700376140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700376200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded/test_data_set_0ByJ0output_0.pb000066400000000000000000000000131511334557700400120ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded/test_data_set_0BzJÔCš?output_1.pb000066400000000000000000000002141511334557700400160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_3d_log_prob_expanded/test_data_set_0Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/000077500000000000000000000000001511334557700272015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/model.onnx000066400000000000000000000003121511334557700312010ustar00rootroot00000000000000 backend-test:ą M x yz"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean test_sce_mean_no_weight_ii_4dZ x     Z y    b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/test_data_set_0/000077500000000000000000000000001511334557700322435ustar00rootroot00000000000000input_0.pb000066400000000000000000000015301511334557700340640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700340720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/test_data_set_0ByJĐoutput_0.pb000066400000000000000000000000131511334557700342600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d/test_data_set_0BzJhë×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expanded/000077500000000000000000000000001511334557700310515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expanded/model.onnx000066400000000000000000000027721511334557700330650ustar00rootroot00000000000000 backend-test:á }OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ž x OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_Shape3DMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_NCD"Reshape: ŧ MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_NCDMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_NDC" Transpose* perm@@@ : ģ MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_NDCOSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_LogSM" LogSoftmax* axis : Ä OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_LogSMSSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : ] xOSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_shape"Shape: € SSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_LogSM_NCD OSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_shapeMSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_Log"Reshape:  MSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :&test_sce_mean_no_weight_ii_4d_expandedZ x     Z y    b z B  test_data_set_0/000077500000000000000000000000001511334557700340345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expandedinput_0.pb000066400000000000000000000015301511334557700357340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expanded/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700357420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expanded/test_data_set_0ByJĐoutput_0.pb000066400000000000000000000000131511334557700361300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_expanded/test_data_set_0BzJhë×?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/000077500000000000000000000000001511334557700310645ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/model.onnx000066400000000000000000000004011511334557700330630ustar00rootroot00000000000000 backend-test:č W x yzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean &test_sce_mean_no_weight_ii_4d_log_probZ x     Z y    b z b" log_prob     B  test_data_set_0/000077500000000000000000000000001511334557700340475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_probinput_0.pb000066400000000000000000000015301511334557700357470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700357550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/test_data_set_0ByJĐoutput_0.pb000066400000000000000000000000131511334557700361430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/test_data_set_0BzJhë×?output_1.pb000066400000000000000000000015371511334557700361600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob/test_data_set_0Blog_probJČzĘŋY‡ŋJ˙ˇŋhŲŋ_ßÎŋžmŧŋŋSÚŋT“¨ŋØnžŋš×āŋ2ųËŋąįÂŋ…ÆŋŋĝĄŋîĐŸëíŋHĀy“´ŋ ĄŋØŊŸŋ>•ŋn´ŋdļŪŋÔŽŋiĀ—ą´ŋÍ"öŋĪ,ŸŋĨíÍŋ˙Ãŋ¯IãŋĢ ŧŋļĘŋöWÆŋt÷ĀCĒËŋūnËŋ…ôÂŋŗƒ¸ŋĸT¯ŋŲwÚŋ'āŋ—mˇŋ4]ņŋίŋNOÉŋā+ęŋž™ūŋ§öéŋß*ėŋđËĐŋČŲŋgͲŋˇŠųŋēŋíŋ÷¸Ā„ ŊŋĐĻØŋ’vÉŋkŨ˙ŋ”ĀđŋŅ|ĀTžŋ{„ĀDOĀÔšâŋ!:Čŋĩ,úŋm:ŋbåĀ/`ĻŋëĻĩŋg°ŋŗËŦŋNũĘŋáĒ ĀœwŪŋCûũŋFæŋŸĀÖĀæŋRæŋßņ˙ŋŧŋiÖÚŋfĢĪŋęŋ 1îŋáäßŋ¯fŠŋÂãŲŋ)đˇŋ-OûŋŪŋ´ŋ@ięŋ ÖĀŊŋ“V ĀBšŋŌ ņŋ9EÖŋ¨§×ŋŠ‚ËŋŨ0Ĩŋ{ĶâŋXžÃŋæeĀŋî0Įŋ‘æōŋ6+ĄŋuėÎŋęč¨ŋDÔÉŋE‘ËŋbŨÄŋ”vĮŋÕ¸ŋļōÕŋë͑ŋũ¸´ŋĖX¯ŋEĐŋŽ•ŋž°ČŋúæŅŋŊĄĮŋņsĀŽ ËŋY‡ØŋÕŋ^‚Úŋ÷wéŋiSņŋĀ/įŋR9ÃŋÃÎÄŋņÅŋĪ‚Įŋž¯´ŋPŪŋËĸŋ‘GĶŋ‹âŋĸŋ ”ŖŋĄŅŽŋ(4Āc˒ŋ"1¯ŋøíĨŋÂ'ĀEļŋ@dįŋv¯ÍŋĀ|ĀMȕŋK˙˛ŋObËŋĶĻÖŋĀ3÷ŊŋNoÎŋ1Žŋ’âļŋÁ’œŋ¸ŋų°úŋ\fîŋ€bˇŋß[ėŋ2¤×ŋŖĀë+ņŋŨE¤ąŋnĶëŋéhŪŋŅüŽŋdFŋŋ]ÅĀ įŋ„íĖŋ5đĘŋāãŋ2Áĸŋ+¤ÅŋË5Æŋ{hŊŋįĮšŋ-āŋ|¤×ŋvĀŧÍĀÕŧŦŋ8‡ŋÃ˛Ũŋéļ÷ŋÅáŋt ĀĸŸÜŋ{ÜâŋŨȝŋ_âæŋŊņŋ\ēĀF–ĀLzÂŋJ‹ëŋ1‚ˇŋūŗŠŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded/000077500000000000000000000000001511334557700327345ustar00rootroot00000000000000model.onnx000066400000000000000000000034261511334557700346660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded backend-test:ũ †XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ā x XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_Shape3DVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_NCD"Reshape: Î VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_NCDVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Í VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_NDCXSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : Ö XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_LogSM\SoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : f xXSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_shape"Shape: › \SoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_LogSM_NCD XSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_shapeVSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_Log"Reshape: n VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_Loglog_prob"Identity: Ļ VSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_4d_log_prob_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :/test_sce_mean_no_weight_ii_4d_log_prob_expandedZ x     Z y    b z b" log_prob     B  test_data_set_0/000077500000000000000000000000001511334557700357175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expandedinput_0.pb000066400000000000000000000015301511334557700376170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700376250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded/test_data_set_0ByJĐoutput_0.pb000066400000000000000000000000131511334557700400130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded/test_data_set_0BzJhë×?output_1.pb000066400000000000000000000015371511334557700400300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_4d_log_prob_expanded/test_data_set_0Blog_probJČzĘŋY‡ŋJ˙ˇŋhŲŋ_ßÎŋžmŧŋŋSÚŋT“¨ŋØnžŋš×āŋ2ųËŋąįÂŋ…ÆŋŋĝĄŋîĐŸëíŋHĀy“´ŋ ĄŋØŊŸŋ>•ŋn´ŋdļŪŋÔŽŋiĀ—ą´ŋÍ"öŋĪ,ŸŋĨíÍŋ˙Ãŋ¯IãŋĢ ŧŋļĘŋöWÆŋt÷ĀCĒËŋūnËŋ…ôÂŋŗƒ¸ŋĸT¯ŋŲwÚŋ'āŋ—mˇŋ4]ņŋίŋNOÉŋā+ęŋž™ūŋ§öéŋß*ėŋđËĐŋČŲŋgͲŋˇŠųŋēŋíŋ÷¸Ā„ ŊŋĐĻØŋ’vÉŋkŨ˙ŋ”ĀđŋŅ|ĀTžŋ{„ĀDOĀÔšâŋ!:Čŋĩ,úŋm:ŋbåĀ/`ĻŋëĻĩŋg°ŋŗËŦŋNũĘŋáĒ ĀœwŪŋCûũŋFæŋŸĀÖĀæŋRæŋßņ˙ŋŧŋiÖÚŋfĢĪŋęŋ 1îŋáäßŋ¯fŠŋÂãŲŋ)đˇŋ-OûŋŪŋ´ŋ@ięŋ ÖĀŊŋ“V ĀBšŋŌ ņŋ9EÖŋ¨§×ŋŠ‚ËŋŨ0Ĩŋ{ĶâŋXžÃŋæeĀŋî0Įŋ‘æōŋ6+ĄŋuėÎŋęč¨ŋDÔÉŋE‘ËŋbŨÄŋ”vĮŋÕ¸ŋļōÕŋë͑ŋũ¸´ŋĖX¯ŋEĐŋŽ•ŋž°ČŋúæŅŋŊĄĮŋņsĀŽ ËŋY‡ØŋÕŋ^‚Úŋ÷wéŋiSņŋĀ/įŋR9ÃŋÃÎÄŋņÅŋĪ‚Įŋž¯´ŋPŪŋËĸŋ‘GĶŋ‹âŋĸŋ ”ŖŋĄŅŽŋ(4Āc˒ŋ"1¯ŋøíĨŋÂ'ĀEļŋ@dįŋv¯ÍŋĀ|ĀMȕŋK˙˛ŋObËŋĶĻÖŋĀ3÷ŊŋNoÎŋ1Žŋ’âļŋÁ’œŋ¸ŋų°úŋ\fîŋ€bˇŋß[ėŋ2¤×ŋŖĀë+ņŋŨE¤ąŋnĶëŋéhŪŋŅüŽŋdFŋŋ]ÅĀ įŋ„íĖŋ5đĘŋāãŋ2Áĸŋ+¤ÅŋË5Æŋ{hŊŋįĮšŋ-āŋ|¤×ŋvĀŧÍĀÕŧŦŋ8‡ŋÃ˛Ũŋéļ÷ŋÅáŋt ĀĸŸÜŋ{ÜâŋŨȝŋ_âæŋŊņŋ\ēĀF–ĀLzÂŋJ‹ëŋ1‚ˇŋūŗŠŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/000077500000000000000000000000001511334557700304625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/model.onnx000066400000000000000000000026751511334557700325000ustar00rootroot00000000000000 backend-test:¤ zLSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ¨ x LSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_Shape3DJSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_NCD"Reshape: ļ JSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_NCDJSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_NDC" Transpose* perm@@@ : ĩ JSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_NDCLSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_LogSM" LogSoftmax* axis : ž LSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_LogSMPSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : Z xLSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_shape"Shape: ÷ PSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_LogSM_NCD LSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_shapeJSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_Log"Reshape: š JSoftmaxCrossEntropyLoss_test_sce_mean_no_weight_ii_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :#test_sce_mean_no_weight_ii_expandedZ x   Z y  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/test_data_set_0/000077500000000000000000000000001511334557700335245ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700353440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700353420ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/test_data_set_0ByJoutput_0.pb000066400000000000000000000000131511334557700355410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_expanded/test_data_set_0BzJŪųÍ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/000077500000000000000000000000001511334557700304755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/model.onnx000066400000000000000000000003461511334557700325040ustar00rootroot00000000000000 backend-test:Í W x yzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean #test_sce_mean_no_weight_ii_log_probZ x   Z y  b z b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/test_data_set_0/000077500000000000000000000000001511334557700335375ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700353570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700353550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/test_data_set_0ByJoutput_0.pb000066400000000000000000000000131511334557700355540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/test_data_set_0BzJŪųÍ?output_1.pb000066400000000000000000000001161511334557700355610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700372250ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob_expanded/test_data_set_0ByJoutput_0.pb000066400000000000000000000000131511334557700374240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob_expanded/test_data_set_0BzJŪųÍ?output_1.pb000066400000000000000000000001161511334557700374310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_no_weight_ii_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700323740ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight/test_data_set_0/input_2.pb000066400000000000000000000000351511334557700324000ustar00rootroot00000000000000BwJfff?333?ÍĖL?fff?fff?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700325730ustar00rootroot00000000000000BzJÖČ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/000077500000000000000000000000001511334557700273055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/model.onnx000066400000000000000000000025421511334557700313140ustar00rootroot00000000000000 backend-test:É tFSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : œ x FSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_Shape3DDSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_NCD"Reshape: Ē DSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_NCDDSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_NDC" Transpose* perm@@@ : Š DSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_NDCFSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_LogSM" LogSoftmax* axis : ˛ FSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_LogSMJSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : T xFSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_shape"Shape: å JSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_LogSM_NCD FSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_shapeDSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_Log"Reshape: ‚ DSoftmaxCrossEntropyLoss_test_sce_mean_weight_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean :test_sce_mean_weight_expandedZ x   Z y  Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/test_data_set_0/000077500000000000000000000000001511334557700323475ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700341670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700341650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700341710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700343640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_expanded/test_data_set_0BzJÖČ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/000077500000000000000000000000001511334557700261165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/model.onnx000066400000000000000000000003101511334557700301140ustar00rootroot00000000000000 backend-test:¯ P x y wz"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean test_sce_mean_weight_iiZ x   Z y  Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/test_data_set_0/000077500000000000000000000000001511334557700311605ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700330570ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700330550ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/test_data_set_0/input_2.pb000066400000000000000000000000351511334557700330610ustar00rootroot00000000000000BwJfff?333?ÍĖL?fff?fff?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700332540ustar00rootroot00000000000000BzJßųÍ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/000077500000000000000000000000001511334557700265045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/model.onnx000066400000000000000000000003231511334557700305060ustar00rootroot00000000000000 backend-test:ē P x y wz"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean test_sce_mean_weight_ii_3dZ x    Z y   Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/test_data_set_0/000077500000000000000000000000001511334557700315465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/test_data_set_0/input_0.pb000066400000000000000000000002051511334557700334440ustar00rootroot00000000000000BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/test_data_set_0/input_1.pb000066400000000000000000000000731511334557700334500ustar00rootroot00000000000000ByJ0onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/test_data_set_0/input_2.pb000066400000000000000000000000351511334557700334470ustar00rootroot00000000000000BwJÍĖL>š™™>š™?ÍĖĖ=?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700336420ustar00rootroot00000000000000BzJAéä?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/000077500000000000000000000000001511334557700303545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/model.onnx000066400000000000000000000027311511334557700323630ustar00rootroot00000000000000 backend-test:Ā zLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ¨ x LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_Shape3DJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_NCD"Reshape: ļ JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_NCDJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_NDC" Transpose* perm@@@ : ĩ JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_NDCLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_LogSM" LogSoftmax* axis : ž LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_LogSMPSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : Z xLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_shape"Shape: ÷ PSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_LogSM_NCD LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_shapeJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_Log"Reshape:  JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :#test_sce_mean_weight_ii_3d_expandedZ x    Z y   Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/test_data_set_0/000077500000000000000000000000001511334557700334165ustar00rootroot00000000000000input_0.pb000066400000000000000000000002051511334557700352350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700352410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/test_data_set_0ByJ0input_2.pb000066400000000000000000000000351511334557700352400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700354330ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_expanded/test_data_set_0BzJAéä?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/000077500000000000000000000000001511334557700303675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/model.onnx000066400000000000000000000004061511334557700323730ustar00rootroot00000000000000 backend-test:í Z x y wzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean #test_sce_mean_weight_ii_3d_log_probZ x    Z y   Z w  b z b log_prob    B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0/000077500000000000000000000000001511334557700334315ustar00rootroot00000000000000input_0.pb000066400000000000000000000002051511334557700352500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700352540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0ByJ0input_2.pb000066400000000000000000000000351511334557700352530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700354460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0BzJAéä?output_1.pb000066400000000000000000000002141511334557700354520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob/test_data_set_0Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/000077500000000000000000000000001511334557700322375ustar00rootroot00000000000000model.onnx000066400000000000000000000033561511334557700341730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded backend-test:Õ ƒUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ē x USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_Shape3DSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_NCD"Reshape: Č SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_NCDSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Į SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_NDCUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : Đ USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_LogSMYSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : c xUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_shape"Shape: ’ YSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_LogSM_NCD USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_shapeSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_Log"Reshape: k SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_Loglog_prob"Identity: Ļ SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_3d_log_prob_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :,test_sce_mean_weight_ii_3d_log_prob_expandedZ x    Z y   Z w  b z b log_prob    B  test_data_set_0/000077500000000000000000000000001511334557700352225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expandedinput_0.pb000066400000000000000000000002051511334557700371200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/test_data_set_0BxJx  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000731511334557700371240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/test_data_set_0ByJ0input_2.pb000066400000000000000000000000351511334557700371230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700373160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/test_data_set_0BzJAéä?output_1.pb000066400000000000000000000002141511334557700373220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_3d_log_prob_expanded/test_data_set_0Blog_probJxŽ–Öŋ–ŊÅŋÛŽĪŋ.ŠÛŋâ›æŋAœÎŋXĶäŋL#¯ŋį|ĄŋM4đŋqǍŋâãŋLÅŋÁ9°ŋũtĀTÆ Ā–ĩĀk ŧŋ gĒŋ%Wˇŋí@ŋ QÅŋŠrŌŋ~ŗĮŋ­`ūŋņ˛Ųŋä*ûŋö°˛ŋ]¸Ęŋ<ˆöŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/000077500000000000000000000000001511334557700265055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/model.onnx000066400000000000000000000003331511334557700305100ustar00rootroot00000000000000 backend-test: P x y wz"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean test_sce_mean_weight_ii_4dZ x     Z y    Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/test_data_set_0/000077500000000000000000000000001511334557700315475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/test_data_set_0/input_0.pb000066400000000000000000000015301511334557700334470ustar00rootroot00000000000000BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/test_data_set_0/input_1.pb000066400000000000000000000005361511334557700334550ustar00rootroot00000000000000ByJĐonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/test_data_set_0/input_2.pb000066400000000000000000000000351511334557700334500ustar00rootroot00000000000000BwJÍĖL>š™™>š™?ÍĖĖ=?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700336430ustar00rootroot00000000000000BzJŧ+Ü?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/000077500000000000000000000000001511334557700303555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/model.onnx000066400000000000000000000027411511334557700323650ustar00rootroot00000000000000 backend-test:Č zLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ¨ x LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_Shape3DJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_NCD"Reshape: ļ JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_NCDJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_NDC" Transpose* perm@@@ : ĩ JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_NDCLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_LogSM" LogSoftmax* axis : ž LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_LogSMPSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : Z xLSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_shape"Shape: ÷ PSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_LogSM_NCD LSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_shapeJSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_Log"Reshape:  JSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :#test_sce_mean_weight_ii_4d_expandedZ x     Z y    Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/test_data_set_0/000077500000000000000000000000001511334557700334175ustar00rootroot00000000000000input_0.pb000066400000000000000000000015301511334557700352400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700352460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/test_data_set_0ByJĐinput_2.pb000066400000000000000000000000351511334557700352410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700354340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_expanded/test_data_set_0BzJŧ+Ü?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/000077500000000000000000000000001511334557700303705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/model.onnx000066400000000000000000000004221511334557700323720ustar00rootroot00000000000000 backend-test:ų Z x y wzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean #test_sce_mean_weight_ii_4d_log_probZ x     Z y    Z w  b z b" log_prob     B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0/000077500000000000000000000000001511334557700334325ustar00rootroot00000000000000input_0.pb000066400000000000000000000015301511334557700352530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? ŨÆ=ƒV?>ĪÄ=?ųy? ķī> z?(×?a@=?aƒ =ː>)ö=°Ÿ—>…'ķ=ąÎĸ>GÔ>Ã_ƒ=ÚE1?Ë ?"á‡>–ķ?âcĀ=;q?[æm?xŖ>hÛ*?ö>8a7? -”>„–;>ļ%?•¸¤Š>­5 ???ƒod>\įs?žíä>=ŽX?3?¨I˜>UP?ÍË>ûa?MÎ?iša?ĀI1?DĒ9?ËV?æÁt?‹Ü$?‡Ų>–<?;<hš>")? …”>B6?’‡Û>ˇš >v¸˜>8é?pC?û?+8'?=đ&?áâÜ>„e?1ŧ>°)ß>Ud?ģbN? 24?ÁCÍ=6ck?…Ø6?p´?ú>‚=^?Œd&>P•?U•ũ=Y?uŦN?–°?TzĐ>m§=ąŠ2?ŋ6č>ŖØ8?;Ë]?Įģy?î[?nė?< O¸>Šā:?ąŋ/>¨b?ƒ‘^=äËL>ģ—<Æ/K?ƒLe>īҰ>ŧ–m?T4?Ši=–Ĩ(>5?AÅ?-šs>Ļ(o?ß,?; ?W?Gé:?AˇŸ>ĸãË>HáV>aŠ>>dÂq?3S=?kû>aßh>;‚>ü¯m=ÛkŪ>犯>‘C2?°hÁ>ę7> +Ę<-ē‰=¯ė-?ôJč>A] ?@Œe?input_1.pb000066400000000000000000000005361511334557700352610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0ByJĐinput_2.pb000066400000000000000000000000351511334557700352540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700354470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0BzJŧ+Ü?output_1.pb000066400000000000000000000015371511334557700354640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob/test_data_set_0Blog_probJČzĘŋY‡ŋJ˙ˇŋhŲŋ_ßÎŋžmŧŋŋSÚŋT“¨ŋØnžŋš×āŋ2ųËŋąįÂŋ…ÆŋŋĝĄŋîĐŸëíŋHĀy“´ŋ ĄŋØŊŸŋ>•ŋn´ŋdļŪŋÔŽŋiĀ—ą´ŋÍ"öŋĪ,ŸŋĨíÍŋ˙Ãŋ¯IãŋĢ ŧŋļĘŋöWÆŋt÷ĀCĒËŋūnËŋ…ôÂŋŗƒ¸ŋĸT¯ŋŲwÚŋ'āŋ—mˇŋ4]ņŋίŋNOÉŋā+ęŋž™ūŋ§öéŋß*ėŋđËĐŋČŲŋgͲŋˇŠųŋēŋíŋ÷¸Ā„ ŊŋĐĻØŋ’vÉŋkŨ˙ŋ”ĀđŋŅ|ĀTžŋ{„ĀDOĀÔšâŋ!:Čŋĩ,úŋm:ŋbåĀ/`ĻŋëĻĩŋg°ŋŗËŦŋNũĘŋáĒ ĀœwŪŋCûũŋFæŋŸĀÖĀæŋRæŋßņ˙ŋŧŋiÖÚŋfĢĪŋęŋ 1îŋáäßŋ¯fŠŋÂãŲŋ)đˇŋ-OûŋŪŋ´ŋ@ięŋ ÖĀŊŋ“V ĀBšŋŌ ņŋ9EÖŋ¨§×ŋŠ‚ËŋŨ0Ĩŋ{ĶâŋXžÃŋæeĀŋî0Įŋ‘æōŋ6+ĄŋuėÎŋęč¨ŋDÔÉŋE‘ËŋbŨÄŋ”vĮŋÕ¸ŋļōÕŋë͑ŋũ¸´ŋĖX¯ŋEĐŋŽ•ŋž°ČŋúæŅŋŊĄĮŋņsĀŽ ËŋY‡ØŋÕŋ^‚Úŋ÷wéŋiSņŋĀ/įŋR9ÃŋÃÎÄŋņÅŋĪ‚Įŋž¯´ŋPŪŋËĸŋ‘GĶŋ‹âŋĸŋ ”ŖŋĄŅŽŋ(4Āc˒ŋ"1¯ŋøíĨŋÂ'ĀEļŋ@dįŋv¯ÍŋĀ|ĀMȕŋK˙˛ŋObËŋĶĻÖŋĀ3÷ŊŋNoÎŋ1Žŋ’âļŋÁ’œŋ¸ŋų°úŋ\fîŋ€bˇŋß[ėŋ2¤×ŋŖĀë+ņŋŨE¤ąŋnĶëŋéhŪŋŅüŽŋdFŋŋ]ÅĀ įŋ„íĖŋ5đĘŋāãŋ2Áĸŋ+¤ÅŋË5Æŋ{hŊŋįĮšŋ-āŋ|¤×ŋvĀŧÍĀÕŧŦŋ8‡ŋÃ˛Ũŋéļ÷ŋÅáŋt ĀĸŸÜŋ{ÜâŋŨȝŋ_âæŋŊņŋ\ēĀF–ĀLzÂŋJ‹ëŋ1‚ˇŋūŗŠŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/000077500000000000000000000000001511334557700322405ustar00rootroot00000000000000model.onnx000066400000000000000000000033721511334557700341720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded backend-test:á ƒUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ē x USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_Shape3DSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_NCD"Reshape: Č SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_NCDSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_NDC" Transpose* perm@@@ : Į SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_NDCUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_LogSM" LogSoftmax* axis : Đ USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_LogSMYSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : c xUSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_shape"Shape: ’ YSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_LogSM_NCD USoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_shapeSSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_Log"Reshape: k SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_Loglog_prob"Identity: Ļ SSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_4d_log_prob_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index :,test_sce_mean_weight_ii_4d_log_prob_expandedZ x     Z y    Z w  b z b" log_prob     B  test_data_set_0/000077500000000000000000000000001511334557700352235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expandedinput_0.pb000066400000000000000000000015301511334557700371230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/test_data_set_0BxJČ  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?k¸>§Âß>õ—2?üŽv=9ą*?ėŽ+?‡nW>A>ĶĄ>L8ē>jø?aā>}?ßüĐ=ĘãU>R.%>2'?p¯>IĀî>´Jz>ėČ">] â=7(?Õ >ãLI>ŒÉŧ>,R? 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?@Œe?input_1.pb000066400000000000000000000005361511334557700371310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/test_data_set_0ByJĐinput_2.pb000066400000000000000000000000351511334557700371240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/test_data_set_0BwJÍĖL>š™™>š™?ÍĖĖ=?output_0.pb000066400000000000000000000000131511334557700373170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/test_data_set_0BzJŧ+Ü?output_1.pb000066400000000000000000000015371511334557700373340ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_4d_log_prob_expanded/test_data_set_0Blog_probJČzĘŋY‡ŋJ˙ˇŋhŲŋ_ßÎŋžmŧŋŋSÚŋT“¨ŋØnžŋš×āŋ2ųËŋąįÂŋ…ÆŋŋĝĄŋîĐŸëíŋHĀy“´ŋ ĄŋØŊŸŋ>•ŋn´ŋdļŪŋÔŽŋiĀ—ą´ŋÍ"öŋĪ,ŸŋĨíÍŋ˙Ãŋ¯IãŋĢ 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wISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ĸ x ISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_Shape3DGSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_NCD"Reshape: ° GSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_NCDGSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_NDC" Transpose* perm@@@ : ¯ GSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_NDCISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_LogSM" LogSoftmax* axis : ¸ ISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_LogSMMSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : W xISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_shape"Shape: î MSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_LogSM_NCD ISoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_shapeGSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_Log"Reshape: š GSoftmaxCrossEntropyLoss_test_sce_mean_weight_ii_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"mean * ignore_index : test_sce_mean_weight_ii_expandedZ x   Z y  Z w  b z B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_expanded/test_data_set_0/000077500000000000000000000000001511334557700330305ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700346500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_expanded/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700346460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700346520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700350450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_expanded/test_data_set_0BzJßųÍ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/000077500000000000000000000000001511334557700300015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/model.onnx000066400000000000000000000003671511334557700320130ustar00rootroot00000000000000 backend-test:Ū Z x y wzlog_prob"SoftmaxCrossEntropyLoss* ignore_index * reduction"mean  test_sce_mean_weight_ii_log_probZ x   Z y  Z w  b z b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0/000077500000000000000000000000001511334557700330435ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700346630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700346610ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700346650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700350600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0BzJßųÍ?output_1.pb000066400000000000000000000001161511334557700350650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700365310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700365350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700367300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob_expanded/test_data_set_0BzJßųÍ?output_1.pb000066400000000000000000000001161511334557700367350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_ii_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700342000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700342040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700343770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob/test_data_set_0BzJÖČ?output_1.pb000066400000000000000000000001161511334557700344040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700360500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700360540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000131511334557700362470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob_expanded/test_data_set_0BzJÖČ?output_1.pb000066400000000000000000000001161511334557700362540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_mean_weight_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700310440ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700312460ustar00rootroot00000000000000BzJ ĸžģ?_tõ?^Ļ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/000077500000000000000000000000001511334557700257555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/model.onnx000066400000000000000000000023501511334557700277610ustar00rootroot00000000000000 backend-test:Ī m?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Ž x ?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_Shape3D=SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_NCD"Reshape: œ =SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_NCD=SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_NDC" Transpose* perm@@@ : › =SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_NDC?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_LogSM" LogSoftmax* axis : ¤ ?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_LogSMCSoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : M x?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_shape"Shape: Đ CSoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_LogSM_NCD ?SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_shape=SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_Log"Reshape: x =SoftmaxCrossEntropyLoss_test_sce_none_expanded_function_X_Log yz"NegativeLogLikelihoodLoss* reduction"none :test_sce_none_expandedZ x   Z y  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/test_data_set_0/000077500000000000000000000000001511334557700310175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700327160ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700327140ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_expanded/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700331160ustar00rootroot00000000000000BzJ ĸžģ?_tõ?^Ļ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/000077500000000000000000000000001511334557700257705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/model.onnx000066400000000000000000000003101511334557700277660ustar00rootroot00000000000000 backend-test:¯ B x yzlog_prob"SoftmaxCrossEntropyLoss* reduction"none test_sce_none_log_probZ x   Z y  b z  b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/test_data_set_0/000077500000000000000000000000001511334557700310325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700327310ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700327270ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700331310ustar00rootroot00000000000000BzJ ĸžģ?_tõ?^Ļ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob/test_data_set_0/output_1.pb000066400000000000000000000001161511334557700331330ustar00rootroot00000000000000Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700345200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob_expanded/test_data_set_0ByJoutput_0.pb000066400000000000000000000000251511334557700347220ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob_expanded/test_data_set_0BzJ ĸžģ?_tõ?^Ļ?output_1.pb000066400000000000000000000001161511334557700347240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700325760ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights/test_data_set_0/input_2.pb000066400000000000000000000000351511334557700326020ustar00rootroot00000000000000BwJfff?333?ÍĖL?fff?fff?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700330000ustar00rootroot00000000000000BzJ qUƒ?ŧčÜ?Ų•?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/000077500000000000000000000000001511334557700275075ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/model.onnx000066400000000000000000000025651511334557700315230ustar00rootroot00000000000000 backend-test:Ü uGSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : ž x GSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_Shape3DESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_NCD"Reshape: Ŧ ESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_NCDESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_NDC" Transpose* perm@@@ : Ģ ESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_NDCGSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_LogSM" LogSoftmax* axis : ´ GSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_LogSMKSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : U xGSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_shape"Shape: č KSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_LogSM_NCD GSoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_shapeESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_Log"Reshape: ƒ ESoftmaxCrossEntropyLoss_test_sce_none_weights_expanded_function_X_Log y wz"NegativeLogLikelihoodLoss* reduction"none :test_sce_none_weights_expandedZ x   Z y  Z w  b z  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/test_data_set_0/000077500000000000000000000000001511334557700325515ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700343710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700343670ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700343730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000251511334557700345710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_expanded/test_data_set_0BzJ qUƒ?ŧčÜ?Ų•?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/000077500000000000000000000000001511334557700275225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/model.onnx000066400000000000000000000003441511334557700315270ustar00rootroot00000000000000 backend-test:Ë E x y wzlog_prob"SoftmaxCrossEntropyLoss* reduction"none test_sce_none_weights_log_probZ x   Z y  Z w  b z  b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0/000077500000000000000000000000001511334557700325645ustar00rootroot00000000000000input_0.pb000066400000000000000000000001071511334557700344040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700344020ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700344060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000251511334557700346040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0BzJ qUƒ?ŧčÜ?Ų•?output_1.pb000066400000000000000000000001161511334557700346060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700362520ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob_expanded/test_data_set_0ByJinput_2.pb000066400000000000000000000000351511334557700362560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob_expanded/test_data_set_0BwJfff?333?ÍĖL?fff?fff?output_0.pb000066400000000000000000000000251511334557700364540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob_expanded/test_data_set_0BzJ qUƒ?ŧčÜ?Ų•?output_1.pb000066400000000000000000000001161511334557700364560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_none_weights_log_prob_expanded/test_data_set_0Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700307110ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700311100ustar00rootroot00000000000000BzJ˜ä•@onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_expanded/000077500000000000000000000000001511334557700256225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_expanded/model.onnx000066400000000000000000000023241511334557700276270ustar00rootroot00000000000000 backend-test:ģ l>SoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_Shape3D"Constant* value*: ˙˙˙˙˙˙˙˙˙ : Œ x >SoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_Shape3DSoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_LogSM" LogSoftmax* axis : ĸ >SoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_LogSMBSoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_LogSM_NCD" Transpose* perm@@@ : L x>SoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_shape"Shape: Í BSoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_LogSM_NCD >SoftmaxCrossEntropyLoss_test_sce_sum_expanded_function_X_shapeQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_expanded/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700325610ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_expanded/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700327600ustar00rootroot00000000000000BzJ˜ä•@onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/000077500000000000000000000000001511334557700256355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/model.onnx000066400000000000000000000003021511334557700276340ustar00rootroot00000000000000 backend-test:Š A x yzlog_prob"SoftmaxCrossEntropyLoss* reduction"sum test_sce_sum_log_probZ x   Z y  b z b log_prob   B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/test_data_set_0/000077500000000000000000000000001511334557700306775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/test_data_set_0/input_0.pb000066400000000000000000000001071511334557700325760ustar00rootroot00000000000000BxJ<  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/test_data_set_0/input_1.pb000066400000000000000000000000411511334557700325740ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/test_data_set_0/output_0.pb000066400000000000000000000000131511334557700327730ustar00rootroot00000000000000BzJ˜ä•@onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob/test_data_set_0/output_1.pb000066400000000000000000000001161511334557700330000ustar00rootroot00000000000000Blog_probJQY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=input_1.pb000066400000000000000000000000411511334557700343650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob_expanded/test_data_set_0ByJoutput_0.pb000066400000000000000000000000131511334557700345640ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob_expanded/test_data_set_0BzJ˜ä•@output_1.pb000066400000000000000000000001161511334557700345710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sce_sum_log_prob_expanded/test_data_set_0Blog_probJ“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700304360ustar00rootroot00000000000000ByJđZYŠ@Š™?îę;@0 ×@Iŗ@Ú|oĀŋj6@äžWŋäĸŋzĢ?<@Ũ>:œ‹@†@äē>ˆqĒ?†!€?}n@.sŽŋžop?%‹\Ā– ąĀRũú?Åø%@:/IĀ~åŲ@ (“Āö‘ >ļƒŋ•%“@ö@Ī˙í>ä6‘?jõaĀƒĨĀ!Ģáŋë&đ>?7l@`Ûf@ã öŋ;\ČŋémyĀ|—‘ĀŪ$ĀBFģ@XTœ6Ā%‰Ā5G@XřĀÁ,“ŋú*cĀ’”?d˜Ā. …Āđģ*žŊz¤?EWL>fLh?æ]4ĀŊĻéŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default/000077500000000000000000000000001511334557700247705ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default/model.onnx000066400000000000000000000001531511334557700267730ustar00rootroot00000000000000  backend-test:S xy"Selutest_selu_defaultZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default/test_data_set_0/000077500000000000000000000000001511334557700300325ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700317410ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700321420ustar00rootroot00000000000000ByJđ7?í?ĨD×>VĄƒ?E°@Ų*û?YŒŋå?CŪ|žCŽ0ž˜âÜ>Ŗú>š•Ã?´L?Žé>ģĮî>ʀŗ>4đČ?ôõĻžæj¨>î>ŋ˜„ĪŋZĪ/?ũƒh?1ÍkŋĄ@ĐzŦŋûíD=šŠ™ž $Î?Å?ņĩ&>˜oË>Tk„ŋĻũÁŋĖ?ŋú7(>"vĨ?.ĩĄ?NˆŋäÕ꾂,’ŋϤĒŋå.¸ŋü-@Yļ3ŋĒĻŋž ŋ!Q?Ę:´ŋ§Ŧžž …ŋ>#Đ>4ŋqî›ŋ™HŊĘlæ>V"=Ŧˇĸ>ŨfSŋkíŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default_expanded_ver18/000077500000000000000000000000001511334557700276655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default_expanded_ver18/model.onnx000066400000000000000000000032201511334557700316660ustar00rootroot00000000000000 backend-test:÷ S.Selu_test_selu_default_expanded_function_Alpha"Constant* value_float}-Ö? : s .Selu_test_selu_default_expanded_function_Alpha x2Selu_test_selu_default_expanded_function_AlphaCast"CastLike: S.Selu_test_selu_default_expanded_function_Gamma"Constant* value_float_}†? : s .Selu_test_selu_default_expanded_function_Gamma x2Selu_test_selu_default_expanded_function_GammaCast"CastLike: S-Selu_test_selu_default_expanded_function_Zero"Constant* value* "B : q -Selu_test_selu_default_expanded_function_Zero x1Selu_test_selu_default_expanded_function_ZeroCast"CastLike: 9 x-Selu_test_selu_default_expanded_function_ExpX"Exp: Ą 2Selu_test_selu_default_expanded_function_AlphaCast -Selu_test_selu_default_expanded_function_ExpX5Selu_test_selu_default_expanded_function_AlphaMulExpX"Mul: ą 5Selu_test_selu_default_expanded_function_AlphaMulExpX 2Selu_test_selu_default_expanded_function_AlphaCast=Selu_test_selu_default_expanded_function_AlphaMulExpXSubAlpha"Sub: ¨ 2Selu_test_selu_default_expanded_function_GammaCast =Selu_test_selu_default_expanded_function_AlphaMulExpXSubAlpha,Selu_test_selu_default_expanded_function_Neg"Mul: l 2Selu_test_selu_default_expanded_function_GammaCast x,Selu_test_selu_default_expanded_function_Pos"Mul: v x 1Selu_test_selu_default_expanded_function_ZeroCast6Selu_test_selu_default_expanded_function_XLessThanZero"Less:   6Selu_test_selu_default_expanded_function_XLessThanZero ,Selu_test_selu_default_expanded_function_Neg ,Selu_test_selu_default_expanded_function_Posy"Where: test_selu_default_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700327275ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700345570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700347600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_default_expanded_ver18/test_data_set_0ByJđ7?í?ĨD×>VĄƒ?E°@Ų*û?YŒŋå?CŪ|žCŽ0ž˜âÜ>Ŗú>š•Ã?´L?Žé>ģĮî>ʀŗ>4đČ?ôõĻžæj¨>î>ŋ˜„ĪŋZĪ/?ũƒh?1ÍkŋĄ@ĐzŦŋûíD=šŠ™ž $Î?Å?ņĩ&>˜oË>Tk„ŋĻũÁŋĖ?ŋú7(>"vĨ?.ĩĄ?NˆŋäÕ꾂,’ŋϤĒŋå.¸ŋü-@Yļ3ŋĒĻŋž ŋ!Q?Ę:´ŋ§Ŧžž …ŋ>#Đ>4ŋqî›ŋ™HŊĘlæ>V"=Ŧˇĸ>ŨfSŋkíŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example/000077500000000000000000000000001511334557700247775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example/model.onnx000066400000000000000000000001751511334557700270060ustar00rootroot00000000000000  backend-test:e . xy"Selu* alpha@ * gamma@@ test_selu_exampleZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example/test_data_set_0/000077500000000000000000000000001511334557700300415ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example/test_data_set_0/input_0.pb000066400000000000000000000000251511334557700317370ustar00rootroot00000000000000BxJ €ŋ€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example/test_data_set_0/output_0.pb000066400000000000000000000000251511334557700321400ustar00rootroot00000000000000ByJ üģrĀ@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example_expanded_ver18/000077500000000000000000000000001511334557700276745ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example_expanded_ver18/model.onnx000066400000000000000000000032001511334557700316730ustar00rootroot00000000000000 backend-test:į S.Selu_test_selu_example_expanded_function_Alpha"Constant* value_float@ : s .Selu_test_selu_example_expanded_function_Alpha x2Selu_test_selu_example_expanded_function_AlphaCast"CastLike: S.Selu_test_selu_example_expanded_function_Gamma"Constant* value_float@@ : s .Selu_test_selu_example_expanded_function_Gamma x2Selu_test_selu_example_expanded_function_GammaCast"CastLike: S-Selu_test_selu_example_expanded_function_Zero"Constant* value* "B : q -Selu_test_selu_example_expanded_function_Zero x1Selu_test_selu_example_expanded_function_ZeroCast"CastLike: 9 x-Selu_test_selu_example_expanded_function_ExpX"Exp: Ą 2Selu_test_selu_example_expanded_function_AlphaCast -Selu_test_selu_example_expanded_function_ExpX5Selu_test_selu_example_expanded_function_AlphaMulExpX"Mul: ą 5Selu_test_selu_example_expanded_function_AlphaMulExpX 2Selu_test_selu_example_expanded_function_AlphaCast=Selu_test_selu_example_expanded_function_AlphaMulExpXSubAlpha"Sub: ¨ 2Selu_test_selu_example_expanded_function_GammaCast =Selu_test_selu_example_expanded_function_AlphaMulExpXSubAlpha,Selu_test_selu_example_expanded_function_Neg"Mul: l 2Selu_test_selu_example_expanded_function_GammaCast x,Selu_test_selu_example_expanded_function_Pos"Mul: v x 1Selu_test_selu_example_expanded_function_ZeroCast6Selu_test_selu_example_expanded_function_XLessThanZero"Less:   6Selu_test_selu_example_expanded_function_XLessThanZero ,Selu_test_selu_example_expanded_function_Neg ,Selu_test_selu_example_expanded_function_Posy"Where: test_selu_example_expanded_ver18Z x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700327365ustar00rootroot00000000000000input_0.pb000066400000000000000000000000251511334557700345550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example_expanded_ver18/test_data_set_0BxJ €ŋ€?output_0.pb000066400000000000000000000000251511334557700347560ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_example_expanded_ver18/test_data_set_0ByJ üģrĀ@@onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_expanded_ver18/000077500000000000000000000000001511334557700261615ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_expanded_ver18/model.onnx000066400000000000000000000026701511334557700301720ustar00rootroot00000000000000 backend-test:Ÿ K&Selu_test_selu_expanded_function_Alpha"Constant* value_float@ : c &Selu_test_selu_expanded_function_Alpha x*Selu_test_selu_expanded_function_AlphaCast"CastLike: K&Selu_test_selu_expanded_function_Gamma"Constant* value_float@@ : c &Selu_test_selu_expanded_function_Gamma x*Selu_test_selu_expanded_function_GammaCast"CastLike: K%Selu_test_selu_expanded_function_Zero"Constant* value* "B : a %Selu_test_selu_expanded_function_Zero x)Selu_test_selu_expanded_function_ZeroCast"CastLike: 1 x%Selu_test_selu_expanded_function_ExpX"Exp: ‰ *Selu_test_selu_expanded_function_AlphaCast %Selu_test_selu_expanded_function_ExpX-Selu_test_selu_expanded_function_AlphaMulExpX"Mul: ™ -Selu_test_selu_expanded_function_AlphaMulExpX *Selu_test_selu_expanded_function_AlphaCast5Selu_test_selu_expanded_function_AlphaMulExpXSubAlpha"Sub:  *Selu_test_selu_expanded_function_GammaCast 5Selu_test_selu_expanded_function_AlphaMulExpXSubAlpha$Selu_test_selu_expanded_function_Neg"Mul: \ *Selu_test_selu_expanded_function_GammaCast x$Selu_test_selu_expanded_function_Pos"Mul: f x )Selu_test_selu_expanded_function_ZeroCast.Selu_test_selu_expanded_function_XLessThanZero"Less: ˆ .Selu_test_selu_expanded_function_XLessThanZero $Selu_test_selu_expanded_function_Neg $Selu_test_selu_expanded_function_Posy"Where:test_selu_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700312235ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_expanded_ver18/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700331320ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_selu_expanded_ver18/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700333330ustar00rootroot00000000000000ByJđZYŠ@Š™?îę;@0 ×@Iŗ@Ú|oĀŋj6@äžWŋäĸŋzĢ?<@Ũ>:œ‹@†@äē>ˆqĒ?†!€?}n@.sŽŋžop?%‹\Ā– ąĀRũú?Åø%@:/IĀ~åŲ@ (“Āö‘ >ļƒŋ•%“@ö@Ī˙í>ä6‘?jõaĀƒĨĀ!Ģáŋë&đ>?7l@`Ûf@ã öŋ;\ČŋémyĀ|—‘ĀŪ$ĀBFģ@XTœ6Ā%‰Ā5G@XřĀÁ,“ŋú*cĀ’”?d˜Ā. …Āđģ*žŊz¤?EWL>fLh?æ]4ĀŊĻéŋonnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/000077500000000000000000000000001511334557700271545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/model.onnx000066400000000000000000000002651511334557700311630ustar00rootroot00000000000000 backend-test:œ 3 sequence tensoroutput_sequence"SequenceInserttest_sequence_insert_at_backZ sequence"  Z tensor  b output_sequence"  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/test_data_set_0/000077500000000000000000000000001511334557700322165ustar00rootroot00000000000000input_0.pb000066400000000000000000000001541511334557700340400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/test_data_set_0 sequence&J JJ input_1.pb000066400000000000000000000000461511334557700340410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/test_data_set_0BtensorJ output_0.pb000066400000000000000000000002231511334557700342360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_back/test_data_set_0 output_sequence&J JJ J onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/000077500000000000000000000000001511334557700274045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/model.onnx000066400000000000000000000003301511334557700314040ustar00rootroot00000000000000 backend-test:ŋ = sequence tensor positionoutput_sequence"SequenceInserttest_sequence_insert_at_frontZ sequence"  Z tensor  Z position  b output_sequence"  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/test_data_set_0/000077500000000000000000000000001511334557700324465ustar00rootroot00000000000000input_0.pb000066400000000000000000000001541511334557700342700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/test_data_set_0 sequence&J JJ input_1.pb000066400000000000000000000000461511334557700342710ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/test_data_set_0BtensorJū˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙input_2.pb000066400000000000000000000000301511334557700342630ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/test_data_set_0BpositionJoutput_0.pb000066400000000000000000000002231511334557700344660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_insert_at_front/test_data_set_0 output_sequenceJū˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙&J JJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor/000077500000000000000000000000001511334557700315135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor/model.onnx000066400000000000000000000004201511334557700335130ustar00rootroot00000000000000 backend-test:÷ ˆ x0 x1y0" SequenceMap*m body2b  in0 in1out0"Add seq_map_bodyZ in0  NZ in1  Nb out0  N )test_sequence_map_add_1_sequence_1_tensorZ x0"  NZ x1  Nb y0"  NB test_data_set_0/000077500000000000000000000000001511334557700344765ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensorinput_0.pb000066400000000000000000000002261511334557700363770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor/test_data_set_0 x0. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000621511334557700363760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor/test_data_set_0 Bx1J(Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?output_0.pb000066400000000000000000000002261511334557700366000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor/test_data_set_0 y0. J(ö8P?jĨž?|Ї?-Ž?ˆâ>VģĄ?\†?PÁ? &ô?€Zˆ?. J(44‡?ëĖĻ?Ņƒ?e<ŋ?žöˇ=wk4?Vß!?ûŠš?`gÜ?rĸÆ?. J(S Ÿ?ëdÉ?Čéj?ĶĒŦ?‘Z >œ÷ ?eA?šâĮ?ǘģ?ˆYŒ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expanded/000077500000000000000000000000001511334557700333635ustar00rootroot00000000000000model.onnx000066400000000000000000000050271511334557700353140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expanded backend-test:ū  x0iSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: ˆgSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : ŒkSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : č iSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_seqlen gSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_cond kSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_0_seqemptyy0"Loop*˜ body2Œ Û eSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_infSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_out"Identity: Č x0 gSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_itercountKSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_in0" SequenceAt: ] x1KSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_in1"Identity: í KSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_in0 KSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_in1LSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_out0"Add ¯ eSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_in LSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_out0fSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_out"SequenceInsert:SequenceMap_loop_bodyZq gSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_itercount Zo eSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_in  Zx eSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_in"  Nbp fSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_out  by fSequenceMap_test_sequence_map_add_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_out"  N :2test_sequence_map_add_1_sequence_1_tensor_expandedZ x0"  NZ x1  Nb y0"  NB test_data_set_0/000077500000000000000000000000001511334557700363465ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expandedinput_0.pb000066400000000000000000000002261511334557700402470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expanded/test_data_set_0 x0. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>input_1.pb000066400000000000000000000000621511334557700402460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expanded/test_data_set_0 Bx1J(Õs‡>.4F?‰Œé>ã„?í™<\?N˛?cī?y™q?Ƌ.?output_0.pb000066400000000000000000000002261511334557700404500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_1_sequence_1_tensor_expanded/test_data_set_0 y0. J(ö8P?jĨž?|Ї?-Ž?ˆâ>VģĄ?\†?PÁ? &ô?€Zˆ?. J(44‡?ëĖĻ?Ņƒ?e<ŋ?žöˇ=wk4?Vß!?ûŠš?`gÜ?rĸÆ?. J(S Ÿ?ëdÉ?Čéj?ĶĒŦ?‘Z >œ÷ ?eA?šâĮ?ǘģ?ˆYŒ?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/000077500000000000000000000000001511334557700300655ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/model.onnx000066400000000000000000000004141511334557700320700ustar00rootroot00000000000000 backend-test:ķ ˆ x0 x1y0" SequenceMap*m body2b  in0 in1out0"Add seq_map_bodyZ in0  NZ in1  Nb out0  N !test_sequence_map_add_2_sequencesZ x0"  NZ x1"  Nb y0"  NB onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/test_data_set_0/000077500000000000000000000000001511334557700331275ustar00rootroot00000000000000input_0.pb000066400000000000000000000001121511334557700347430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/test_data_set_0 x0JSĸ[?}åX?ߥ?ŽÍÄ>uV˜>ŪKh= J›™‹>J†ô>JęO?‘ŋõ>É>input_1.pb000066400000000000000000000001121511334557700347440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/test_data_set_0 x1JB V?1ŋŦ>˜î%?(Šŧ>u?¸> J ž^?JÁ|ō>}M?>? Ë-?output_0.pb000066400000000000000000000001121511334557700351440ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences/test_data_set_0 y0JĘÕØ?‹ĸ—?<Čĸ?ëĢ@?­™ ?ËI> JlE’?J¤†s?dyÎ?æ€?L,‰?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expanded/000077500000000000000000000000001511334557700317355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expanded/model.onnx000066400000000000000000000047061511334557700337500ustar00rootroot00000000000000 backend-test:­ y x0aSequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: €_SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : „cSequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : ´ aSequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_input_sequence_seqlen _SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_input_sequence_cond cSequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_out_sequence_0_seqemptyy0"Loop*ü body2đ Ë ]SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_cond_in^SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_cond_out"Identity: ¸ x0 _SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_itercountCSequenceMap_test_sequence_map_add_2_sequences_expanded_function_in0" SequenceAt: ¸ x1 _SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_itercountCSequenceMap_test_sequence_map_add_2_sequences_expanded_function_in1" SequenceAt: Õ CSequenceMap_test_sequence_map_add_2_sequences_expanded_function_in0 CSequenceMap_test_sequence_map_add_2_sequences_expanded_function_in1DSequenceMap_test_sequence_map_add_2_sequences_expanded_function_out0"Add — ]SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_out0_in DSequenceMap_test_sequence_map_add_2_sequences_expanded_function_out0^SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_out0_out"SequenceInsert:SequenceMap_loop_bodyZi _SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_itercount Zg ]SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_cond_in  Zp ]SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_out0_in"  Nbh ^SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_cond_out  bq ^SequenceMap_test_sequence_map_add_2_sequences_expanded_function_SequenceMap_loop_body_out0_out"  N :*test_sequence_map_add_2_sequences_expandedZ x0"  NZ x1"  Nb y0"  NB test_data_set_0/000077500000000000000000000000001511334557700347205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expandedinput_0.pb000066400000000000000000000001121511334557700366130ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expanded/test_data_set_0 x0JSĸ[?}åX?ߥ?ŽÍÄ>uV˜>ŪKh= J›™‹>J†ô>JęO?‘ŋõ>É>input_1.pb000066400000000000000000000001121511334557700366140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expanded/test_data_set_0 x1JB V?1ŋŦ>˜î%?(Šŧ>u?¸> J ž^?JÁ|ō>}M?>? Ë-?output_0.pb000066400000000000000000000001121511334557700370140ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_add_2_sequences_expanded/test_data_set_0 y0JĘÕØ?‹ĸ—?<Čĸ?ëĢ@?­™ ?ËI> JlE’?J¤†s?dyÎ?æ€?L,‰?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes/000077500000000000000000000000001511334557700300565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes/model.onnx000066400000000000000000000003701511334557700320620ustar00rootroot00000000000000 backend-test:ß | in_seqshapes" SequenceMap*] body2R  xshape"Shape seq_map_bodyZ x  H W Cb shape    test_sequence_map_extract_shapesZ# in_seq"   H W Cb shapes"  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes/test_data_set_0/000077500000000000000000000000001511334557700331205ustar00rootroot00000000000000input_0.pb000066400000000000000000000420541511334557700347470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes/test_data_set_0 in_seqËp(JĀpë Jāã JØoutput_0.pb000066400000000000000000000001521511334557700351410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes/test_data_set_0 shapesJ(J J onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes_expanded/000077500000000000000000000000001511334557700317265ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes_expanded/model.onnx000066400000000000000000000042701511334557700337350ustar00rootroot00000000000000 backend-test:Ÿ | in_seq`SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: ^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : ƒbSequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : ­ `SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_input_sequence_seqlen ^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_input_sequence_cond bSequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_out_sequence_0_seqemptyshapes"Loop*ô body2č É \SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_cond_in]SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_cond_out"Identity: ¸ in_seq ^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_itercount@SequenceMap_test_sequence_map_extract_shapes_expanded_function_x" SequenceAt:  @SequenceMap_test_sequence_map_extract_shapes_expanded_function_xDSequenceMap_test_sequence_map_extract_shapes_expanded_function_shape"Shape — ]SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_shape_in DSequenceMap_test_sequence_map_extract_shapes_expanded_function_shape^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_shape_out"SequenceInsert:SequenceMap_loop_bodyZh ^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_itercount Zf \SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_cond_in  Zo ]SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_shape_in"  bg ]SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_cond_out  bp ^SequenceMap_test_sequence_map_extract_shapes_expanded_function_SequenceMap_loop_body_shape_out"   :)test_sequence_map_extract_shapes_expandedZ# in_seq"   H W Cb shapes"  B test_data_set_0/000077500000000000000000000000001511334557700347115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes_expandedinput_0.pb000066400000000000000000000420541511334557700366170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes_expanded/test_data_set_0 in_seqËp(JĀpë Jāã JØoutput_0.pb000066400000000000000000000001521511334557700370110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_extract_shapes_expanded/test_data_set_0 shapesJ(J J onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence/000077500000000000000000000000001511334557700310025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence/model.onnx000066400000000000000000000003341511334557700330060ustar00rootroot00000000000000 backend-test:à n xy" SequenceMap*Y body2N  in0out0"Identity seq_map_bodyZ in0  Nb out0  M %test_sequence_map_identity_1_sequenceZ x"  Nb y"  NB onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence/test_data_set_0/000077500000000000000000000000001511334557700340445ustar00rootroot00000000000000input_0.pb000066400000000000000000000002251511334557700356650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence/test_data_set_0 x. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>output_0.pb000066400000000000000000000002251511334557700360660ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence/test_data_set_0 y. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor/000077500000000000000000000000001511334557700326145ustar00rootroot00000000000000model.onnx000066400000000000000000000005361511334557700345450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor backend-test:Å ē x0 x1y0y1" SequenceMap*š body2Ž  in0out0"Identity  in1out1"Identity seq_map_bodyZ in0  NZ in1  Mb out0  Nb out1  M .test_sequence_map_identity_1_sequence_1_tensorZ x0"  NZ x1  Mb y0"  Nb y1"  MB test_data_set_0/000077500000000000000000000000001511334557700355775ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensorinput_0.pb000066400000000000000000000001521511334557700374760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor/test_data_set_0 x0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>input_1.pb000066400000000000000000000000221511334557700374730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor/test_data_set_0Bx1JGė>ÃĐG?output_0.pb000066400000000000000000000001521511334557700376770ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor/test_data_set_0 y0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>output_1.pb000066400000000000000000000000661511334557700377040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor/test_data_set_0 y1JGė>ÃĐG?JGė>ÃĐG?JGė>ÃĐG?test_sequence_map_identity_1_sequence_1_tensor_expanded/000077500000000000000000000000001511334557700344055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000071201511334557700364110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expanded backend-test:ˇ † x0nSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: lSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : ‘pSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : ‘pSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_1_seqempty" SequenceEmpty* dtype : â nSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_seqlen lSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_input_sequence_cond pSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_0_seqempty pSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_out_sequence_1_seqemptyy0y1"Loop* body2 å jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_inkSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_out"Identity: Ō x0 lSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_itercountPSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_in0" SequenceAt: b x1PSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_in1"Identity: ¯ PSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_in0QSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_out0"Identity ¯ PSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_in1QSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_out1"Identity ž jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_in QSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_out0kSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_out"SequenceInsert: ž jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out1_in QSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_out1kSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out1_out"SequenceInsert:SequenceMap_loop_bodyZv lSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_itercount Zt jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_in  Z} jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_in"  NZ} jSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out1_in"  Mbu kSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_cond_out  b~ kSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out0_out"  Nb~ kSequenceMap_test_sequence_map_identity_1_sequence_1_tensor_expanded_function_SequenceMap_loop_body_out1_out"  M :7test_sequence_map_identity_1_sequence_1_tensor_expandedZ x0"  NZ x1  Mb y0"  Nb y1"  MB test_data_set_0/000077500000000000000000000000001511334557700374475ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expandedinput_0.pb000066400000000000000000000001521511334557700413460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expanded/test_data_set_0 x0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>input_1.pb000066400000000000000000000000221511334557700413430ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expanded/test_data_set_0Bx1JGė>ÃĐG?output_0.pb000066400000000000000000000001521511334557700415470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expanded/test_data_set_0 y0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>output_1.pb000066400000000000000000000000661511334557700415540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_1_tensor_expanded/test_data_set_0 y1JGė>ÃĐG?JGė>ÃĐG?JGė>ÃĐG?onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_expanded/000077500000000000000000000000001511334557700326525ustar00rootroot00000000000000model.onnx000066400000000000000000000044031511334557700346000ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_expanded backend-test:ę | xeSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: „cSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : ˆgSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : û eSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_input_sequence_seqlen cSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_input_sequence_cond gSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_out_sequence_0_seqemptyy"Loop*¸ body2Ŧ Ķ aSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_cond_inbSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_cond_out"Identity: ŋ x cSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_itercountGSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_in0" SequenceAt:  GSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_in0HSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_out0"Identity Ŗ aSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_out0_in HSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_out0bSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_out0_out"SequenceInsert:SequenceMap_loop_bodyZm cSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_itercount Zk aSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_cond_in  Zt aSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_out0_in"  Mbl bSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_cond_out  bu bSequenceMap_test_sequence_map_identity_1_sequence_expanded_function_SequenceMap_loop_body_out0_out"  M :.test_sequence_map_identity_1_sequence_expandedZ x"  Nb y"  NB test_data_set_0/000077500000000000000000000000001511334557700356355ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_expandedinput_0.pb000066400000000000000000000002251511334557700375350ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_expanded/test_data_set_0 x. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>output_0.pb000066400000000000000000000002251511334557700377360ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_1_sequence_expanded/test_data_set_0 y. J(  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>. J(~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?. J(ģ†z?¨•L?Gė>ÃĐG?Ũ9ō=ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/000077500000000000000000000000001511334557700311665ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/model.onnx000066400000000000000000000005321511334557700331720ustar00rootroot00000000000000 backend-test:Á ē x0 x1y0y1" SequenceMap*š body2Ž  in0out0"Identity  in1out1"Identity seq_map_bodyZ in0  NZ in1  Mb out0  Nb out1  M &test_sequence_map_identity_2_sequencesZ x0"  NZ x1"  Mb y0"  Nb y1"  MB test_data_set_0/000077500000000000000000000000001511334557700341515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequencesinput_0.pb000066400000000000000000000001521511334557700360500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/test_data_set_0 x0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>input_1.pb000066400000000000000000000001221511334557700360460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/test_data_set_0 x1JGė>ÃĐG?&J ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>J ĸv >ĪõĨ>^D>output_0.pb000066400000000000000000000001521511334557700362510ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/test_data_set_0 y0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>output_1.pb000066400000000000000000000001221511334557700362470ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences/test_data_set_0 y1JGė>ÃĐG?&J ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>J ĸv >ĪõĨ>^D>onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded/000077500000000000000000000000001511334557700330365ustar00rootroot00000000000000model.onnx000066400000000000000000000067041511334557700347720ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded backend-test:Ģ ~ x0fSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_input_sequence_seqlen"SequenceLength: …dSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_input_sequence_cond"Constant* value* * : ‰hSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_out_sequence_0_seqempty" SequenceEmpty* dtype : ‰hSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_out_sequence_1_seqempty" SequenceEmpty* dtype : û fSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_input_sequence_seqlen dSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_input_sequence_cond hSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_out_sequence_0_seqempty hSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_out_sequence_1_seqemptyy0y1"Loop*Æ body2ē Õ bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_cond_incSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_cond_out"Identity:  x0 dSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_itercountHSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_in0" SequenceAt:  x1 dSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_itercountHSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_in1" SequenceAt: Ÿ HSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_in0ISequenceMap_test_sequence_map_identity_2_sequences_expanded_function_out0"Identity Ÿ HSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_in1ISequenceMap_test_sequence_map_identity_2_sequences_expanded_function_out1"Identity Ļ bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out0_in ISequenceMap_test_sequence_map_identity_2_sequences_expanded_function_out0cSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out0_out"SequenceInsert: Ļ bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out1_in ISequenceMap_test_sequence_map_identity_2_sequences_expanded_function_out1cSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out1_out"SequenceInsert:SequenceMap_loop_bodyZn dSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_itercount Zl bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_cond_in  Zu bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out0_in"  NZu bSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out1_in"  Mbm cSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_cond_out  bv cSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out0_out"  Nbv cSequenceMap_test_sequence_map_identity_2_sequences_expanded_function_SequenceMap_loop_body_out1_out"  M :/test_sequence_map_identity_2_sequences_expandedZ x0"  NZ x1"  Mb y0"  Nb y1"  MB test_data_set_0/000077500000000000000000000000001511334557700360215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expandedinput_0.pb000066400000000000000000000001521511334557700377200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded/test_data_set_0 x0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>input_1.pb000066400000000000000000000001221511334557700377160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded/test_data_set_0 x1JGė>ÃĐG?&J ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>J ĸv >ĪõĨ>^D>output_0.pb000066400000000000000000000001521511334557700401210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded/test_data_set_0 y0JÍ!X?Sĸ[?}åX?ߥ?ŽÍÄ>uV˜>* J$š˛v?rRÄ>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<Ju?¸> ž^?Á|ō>output_1.pb000066400000000000000000000001221511334557700401170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_sequence_map_identity_2_sequences_expanded/test_data_set_0 y1JGė>ÃĐG?&J ŨŅ#?4Ë>ŌÕq?ڗ?’NÔ>Õs‡>.4F?‰Œé>J ĸv >ĪõĨ>^D>onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape/000077500000000000000000000000001511334557700234145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape/model.onnx000066400000000000000000000001351511334557700254170ustar00rootroot00000000000000  backend-test:E xy"Shape test_shapeZ x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape/test_data_set_0/000077500000000000000000000000001511334557700264565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700303650ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700305530ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_end/000077500000000000000000000000001511334557700252515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_end/model.onnx000066400000000000000000000001621511334557700272540ustar00rootroot00000000000000  backend-test:Z  xy"Shape* end  test_shape_clip_endZ x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_end/test_data_set_0/000077500000000000000000000000001511334557700303135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_end/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700322220ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_end/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700324100ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_start/000077500000000000000000000000001511334557700256405ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_start/model.onnx000066400000000000000000000001771511334557700276510ustar00rootroot00000000000000  backend-test:g $ xy"Shape* startö˙˙˙˙˙˙˙˙ test_shape_clip_startZ x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_start/test_data_set_0/000077500000000000000000000000001511334557700307025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_start/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700326110ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_clip_start/test_data_set_0/output_0.pb000066400000000000000000000000411511334557700327770ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_1/000077500000000000000000000000001511334557700244625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_1/model.onnx000066400000000000000000000001571511334557700264710ustar00rootroot00000000000000  backend-test:W  xy"Shape* end test_shape_end_1Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_1/test_data_set_0/000077500000000000000000000000001511334557700275245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_1/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700314330ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_1/test_data_set_0/output_0.pb000066400000000000000000000000211511334557700316170ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_negative_1/000077500000000000000000000000001511334557700263445ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_negative_1/model.onnx000066400000000000000000000002011511334557700303410ustar00rootroot00000000000000  backend-test:i " xy"Shape* end˙˙˙˙˙˙˙˙˙ test_shape_end_negative_1Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_negative_1/test_data_set_0/000077500000000000000000000000001511334557700314065ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_negative_1/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700333150ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_end_negative_1/test_data_set_0/output_0.pb000066400000000000000000000000311511334557700335020ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_example/000077500000000000000000000000001511334557700251275ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_example/model.onnx000066400000000000000000000001411511334557700271270ustar00rootroot00000000000000  backend-test:I xy"Shapetest_shape_exampleZ x   b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_example/test_data_set_0/000077500000000000000000000000001511334557700301715ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_example/test_data_set_0/input_0.pb000066400000000000000000000000431511334557700320670ustar00rootroot00000000000000BxJ€?@@@€@ @Ā@onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_example/test_data_set_0/output_0.pb000066400000000000000000000000311511334557700322650ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1/000077500000000000000000000000001511334557700250515ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1/model.onnx000066400000000000000000000001631511334557700270550ustar00rootroot00000000000000  backend-test:[  xy"Shape* start test_shape_start_1Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1/test_data_set_0/000077500000000000000000000000001511334557700301135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700320220ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1/test_data_set_0/output_0.pb000066400000000000000000000000311511334557700322070ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_2/000077500000000000000000000000001511334557700261205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_2/model.onnx000066400000000000000000000002051511334557700301210ustar00rootroot00000000000000  backend-test:m ' xy"Shape* end * start test_shape_start_1_end_2Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_2/test_data_set_0/000077500000000000000000000000001511334557700311625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_2/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700330710ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_2/test_data_set_0/output_0.pb000066400000000000000000000000211511334557700332550ustar00rootroot00000000000000ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_negative_1/000077500000000000000000000000001511334557700300015ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_negative_1/model.onnx000066400000000000000000000002271511334557700320060ustar00rootroot00000000000000  backend-test: 0 xy"Shape* end˙˙˙˙˙˙˙˙˙ * start !test_shape_start_1_end_negative_1Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_negative_1/test_data_set_0/000077500000000000000000000000001511334557700330435ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700346730ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_negative_1/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000000211511334557700350570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_1_end_negative_1/test_data_set_0ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_greater_than_end/000077500000000000000000000000001511334557700302025ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_greater_than_end/model.onnx000066400000000000000000000002161511334557700322050ustar00rootroot00000000000000  backend-test:v ' xy"Shape* end * start !test_shape_start_greater_than_endZ x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_greater_than_end/test_data_set_0/000077500000000000000000000000001511334557700332445ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700350740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_greater_than_end/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000000111511334557700352570ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_greater_than_end/test_data_set_0ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_negative_1/000077500000000000000000000000001511334557700267335ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_negative_1/model.onnx000066400000000000000000000002051511334557700307340ustar00rootroot00000000000000  backend-test:m $ xy"Shape* start˙˙˙˙˙˙˙˙˙ test_shape_start_negative_1Z x    b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_negative_1/test_data_set_0/000077500000000000000000000000001511334557700317755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_negative_1/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700337040ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000000211511334557700340110ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shape_start_negative_1/test_data_set_0ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard/000077500000000000000000000000001511334557700246105ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard/model.onnx000066400000000000000000000001551511334557700266150ustar00rootroot00000000000000 backend-test:U  xy"Shrink* lambdĀ? test_shrink_hardZ x  b y  B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard/test_data_set_0/000077500000000000000000000000001511334557700276525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard/test_data_set_0/input_0.pb000066400000000000000000000000351511334557700315510ustar00rootroot00000000000000BxJĀ€ŋ€?@onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard/test_data_set_0/output_0.pb000066400000000000000000000000351511334557700317520ustar00rootroot00000000000000ByJĀ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard_expanded_ver18/000077500000000000000000000000001511334557700275055ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_shrink_hard_expanded_ver18/model.onnx000066400000000000000000000033411511334557700315120ustar00rootroot00000000000000 backend-test:Č T/Shrink_test_shrink_hard_expanded_function_Lambd"Constant* value_floatĀ? : u /Shrink_test_shrink_hard_expanded_function_Lambd x3Shrink_test_shrink_hard_expanded_function_LambdCast"CastLike: S.Shrink_test_shrink_hard_expanded_function_Bias"Constant* value_float : s .Shrink_test_shrink_hard_expanded_function_Bias x2Shrink_test_shrink_hard_expanded_function_BiasCast"CastLike: T.Shrink_test_shrink_hard_expanded_function_Zero"Constant* value* "B : s .Shrink_test_shrink_hard_expanded_function_Zero x2Shrink_test_shrink_hard_expanded_function_ZeroCast"CastLike: p 3Shrink_test_shrink_hard_expanded_function_LambdCast2Shrink_test_shrink_hard_expanded_function_NegLmbda"Neg:  x 2Shrink_test_shrink_hard_expanded_function_NegLmbda@Shrink_test_shrink_hard_expanded_function_InputLessThanNegLambda"Less: v x 2Shrink_test_shrink_hard_expanded_function_BiasCast6Shrink_test_shrink_hard_expanded_function_InputAddBias"Add: v x 2Shrink_test_shrink_hard_expanded_function_BiasCast6Shrink_test_shrink_hard_expanded_function_InputSubBias"Sub:  3Shrink_test_shrink_hard_expanded_function_LambdCast 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v=ã,ß> }ļ>į8Ã=ēĨ=č5Ž>Đ>Ãŧ?“gŅ>Ęž>n9=>&°>æ)P>›ƒ•>ļ˙k>|{•>…!>|o>éâA>õ9D>Éc;># ×=•s>oā=މ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_1_expanded_ver18/000077500000000000000000000000001511334557700301365ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_1_expanded_ver18/model.onnx000066400000000000000000000016111511334557700321410ustar00rootroot00000000000000 backend-test:đ U2Softmax_test_softmax_axis_1_expanded_function_axes"Constant* value*: :  x 2Softmax_test_softmax_axis_1_expanded_function_axes9Softmax_test_softmax_axis_1_expanded_function_X_ReduceMax" ReduceMax* keepdims : z x 9Softmax_test_softmax_axis_1_expanded_function_X_ReduceMax3Softmax_test_softmax_axis_1_expanded_function_X_Sub"Sub: q 3Softmax_test_softmax_axis_1_expanded_function_X_Sub3Softmax_test_softmax_axis_1_expanded_function_X_Exp"Exp:  3Softmax_test_softmax_axis_1_expanded_function_X_Exp 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Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700352310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_1_expanded_ver18/test_data_set_0ByJđŦF?uî=ĮlŊ> 9?ā` ?č^v>ŦŖN>eĄ%>n‘Ž=‚Ū>o*Ö=KĢ>÷]˜>âŅą= C>/q>—˛>É.> T×=đZJ>ūō(?zžz>P(‚>G0 >]†?QCa>Ŧˆ>)>>#p™>›…„>Kĩu=_>>z;…>~)đ>õ´Ŧ=‚ v=ã,ß> }ļ>į8Ã=ēĨ=č5Ž>Đ>Ãŧ?“gŅ>Ęž>n9=>&°>æ)P>›ƒ•>ļ˙k>|{•>…!>|o>éâA>õ9D>Éc;># ×=•s>oā=މ>onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2/000077500000000000000000000000001511334557700252425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2/model.onnx000066400000000000000000000001751511334557700272510ustar00rootroot00000000000000 backend-test:e  xy"Softmax* axis test_softmax_axis_2Z x    b y    B  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Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700342050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2_expanded/test_data_set_0ByJđqg>]Ll=;¸Ō=œē>‘!€>ÁĮ–>cŧ’>c>ާû=€+>,mæ=˜ŒÕ>Á‡U>RTá=X~>Œk>‰JĶ>zæč=$¸>Ô^>Klã> %ˆ=Q¨=Ā”=ÔSĢ>ëS>“5Š=Ž4Ÿ=ۘ>sv>ÄDŽ=ķŲŲ=S5>Ü;?&\Ķ=›ØŪ=ÍŖ>ž>Ī_ >°î>ū× >ŒÚG>Ą …>ÉåŠ>DÕ =ũZë=æã„><%>‚¯ž>Rßģ=ö}>>ÖŨ,>Æá¨>ž_Õ=ņāW>"W>6Y>>'Ą„>e,J>onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2_expanded_ver18/000077500000000000000000000000001511334557700301375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2_expanded_ver18/model.onnx000066400000000000000000000016111511334557700321420ustar00rootroot00000000000000 backend-test:đ U2Softmax_test_softmax_axis_2_expanded_function_axes"Constant* value*: :  x 2Softmax_test_softmax_axis_2_expanded_function_axes9Softmax_test_softmax_axis_2_expanded_function_X_ReduceMax" ReduceMax* keepdims : z x 9Softmax_test_softmax_axis_2_expanded_function_X_ReduceMax3Softmax_test_softmax_axis_2_expanded_function_X_Sub"Sub: q 3Softmax_test_softmax_axis_2_expanded_function_X_Sub3Softmax_test_softmax_axis_2_expanded_function_X_Exp"Exp:  3Softmax_test_softmax_axis_2_expanded_function_X_Exp 2Softmax_test_softmax_axis_2_expanded_function_axes9Softmax_test_softmax_axis_2_expanded_function_X_ReduceSum" ReduceSum* keepdims : z 3Softmax_test_softmax_axis_2_expanded_function_X_Exp 9Softmax_test_softmax_axis_2_expanded_function_X_ReduceSumy"Div:"test_softmax_axis_2_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700332015ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700350310ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_axis_2_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ 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Ä?õ—?kŪæ.:ˆ=™Ũš>īb"?6šš>output_0.pb000066400000000000000000000003761511334557700354700ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_default_axis_expanded/test_data_set_0ByJđqg>]Ll=;¸Ō=œē>‘!€>ÁĮ–>cŧ’>c>ާû=€+>,mæ=˜ŒÕ>Á‡U>RTá=X~>Œk>‰JĶ>zæč=$¸>Ô^>Klã> %ˆ=Q¨=Ā”=ÔSĢ>ëS>“5Š=Ž4Ÿ=ۘ>sv>ÄDŽ=ķŲŲ=S5>Ü;?&\Ķ=›ØŪ=ÍŖ>ž>Ī_ >°î>ū× >ŒÚG>Ą …>ÉåŠ>DÕ =ũZë=æã„><%>‚¯ž>Rßģ=ö}>>ÖŨ,>Æá¨>ž_Õ=ņāW>"W>6Y>>'Ą„>e,J>onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_default_axis_expanded_ver18/000077500000000000000000000000001511334557700314225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softmax_default_axis_expanded_ver18/model.onnx000066400000000000000000000017421511334557700334320ustar00rootroot00000000000000 backend-test:É d8Softmax_test_softmax_default_axis_expanded_function_axes"Constant* value*: ˙˙˙˙˙˙˙˙˙ : œ x 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7Softsign_test_softsign_expanded_function_OneAddAbsInputy"Div:test_softsign_expanded_ver18Z x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_softsign_expanded_ver18/test_data_set_0/000077500000000000000000000000001511334557700321075ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700337370ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softsign_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700341400ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_softsign_expanded_ver18/test_data_set_0ByJđãa#?¨S’>Ė?ũ>j1?ļš&?ãũžŖrų>gž2ŋŊ”•>ßí>0ą?(CŨ>Â(Ū=Re>#€>^[?¯Q.ž˜%t>ČÚëž°ō7ŋA`Ę>Ûbí>āÚžč´1?1˛ŋÍ93=_t!žŪė?QT?+a >¨}Œ>eČđžî*ŋ>'„žEt >}7 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…”>’‡Û>áâÜ>1ŧ>Ud?onnx-onnx-bca0315/onnx/backend/test/data/node/test_spacetodepth_example/000077500000000000000000000000001511334557700265125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_spacetodepth_example/model.onnx000066400000000000000000000002251511334557700305150ustar00rootroot00000000000000 backend-test:} & xy" SpaceToDepth* blocksize test_spacetodepth_exampleZ x     b y     B  onnx-onnx-bca0315/onnx/backend/test/data/node/test_spacetodepth_example/test_data_set_0/000077500000000000000000000000001511334557700315545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_spacetodepth_example/test_data_set_0/input_0.pb000066400000000000000000000001571511334557700334600ustar00rootroot00000000000000BxJ`Ā@€?ā@@A@AAPA˜A`A A@@A€@ A @0ApA¨A€A°AˆA¸Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_spacetodepth_example/test_data_set_0/output_0.pb000066400000000000000000000001571511334557700336610ustar00rootroot00000000000000ByJ`€?@@@€@ @Ā@ā@AA A0A@APA`ApA€AˆAA˜A A¨A°A¸Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_1d_uneven_split_opset18/000077500000000000000000000000001511334557700302115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_1d_uneven_split_opset18/model.onnx000066400000000000000000000003761511334557700322230ustar00rootroot00000000000000 backend-test:å J inputoutput_1output_2output_3output_4"Split* num_outputs "test_split_1d_uneven_split_opset18Z input  b output_1  b output_2  b output_3  b output_4  B 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A0Aoutput_1.pb000066400000000000000000000000521511334557700352750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_2d_uneven_split_opset18/test_data_set_0Boutput_2J€@ @Ā@@APA`Aoutput_2.pb000066400000000000000000000000421511334557700352750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_2d_uneven_split_opset18/test_data_set_0Boutput_3Jā@ApA€Aonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_equal_parts_1d_opset13/000077500000000000000000000000001511334557700300115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_equal_parts_1d_opset13/model.onnx000066400000000000000000000003241511334557700320140ustar00rootroot00000000000000 backend-test:ģ 9 inputoutput_1output_2output_3"Split* axis !test_split_equal_parts_1d_opset13Z input  b output_1  b output_2  b output_3  B  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backend-test:Ī M inputoutput_1output_2output_3"Split* axis * num_outputs !test_split_equal_parts_1d_opset18Z input  b output_1  b output_2  b output_3  B 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A0A€AˆAonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/000077500000000000000000000000001511334557700262625ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/model.onnx000066400000000000000000000002431511334557700302650ustar00rootroot00000000000000  backend-test:Š 0 data splitseq"SplitToSequence* axis test_split_to_sequence_2Z data   Z split  b seq"  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/test_data_set_0/000077500000000000000000000000001511334557700313245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/test_data_set_0/input_0.pb000066400000000000000000000001261511334557700332240ustar00rootroot00000000000000BdataJH€?@@@€@ @Ā@ā@AA A0A@APA`ApA€AˆAonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/test_data_set_0/input_1.pb000066400000000000000000000000351511334557700332240ustar00rootroot00000000000000BsplitJonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_2/test_data_set_0/output_0.pb000066400000000000000000000001431511334557700334240ustar00rootroot00000000000000 seq J€?@@@€@ @8J0Ā@ā@AA A0A@APA`ApA€AˆAonnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_nokeepdims/000077500000000000000000000000001511334557700302575ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_split_to_sequence_nokeepdims/model.onnx000066400000000000000000000002411511334557700322600ustar00rootroot00000000000000  backend-test:ˆ : dataseq"SplitToSequence* axis * keepdims !test_split_to_sequence_nokeepdimsZ data   b seq"  B 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Bxoutput_0.pb000066400000000000000000000001051511334557700367600ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_string_delimiter/test_data_set_02hello2world2!2hello2world2!2hello2world2!B substringsoutput_1.pb000066400000000000000000000000461511334557700367650ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_string_delimiter/test_data_set_0BlengthJonnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/000077500000000000000000000000001511334557700276455ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/model.onnx000066400000000000000000000002421511334557700316470ustar00rootroot00000000000000  backend-test:‰ $ x substringslength" StringSplittest_string_split_empty_tensorZ x  b substrings   b length  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/test_data_set_0/000077500000000000000000000000001511334557700327075ustar00rootroot00000000000000input_0.pb000066400000000000000000000000071511334557700345260ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/test_data_set_0Bxoutput_0.pb000066400000000000000000000000221511334557700347240ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/test_data_set_0B substringsoutput_1.pb000066400000000000000000000000161511334557700347300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_empty_tensor/test_data_set_0BlengthJonnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/000077500000000000000000000000001511334557700267565ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/model.onnx000066400000000000000000000002751511334557700307660ustar00rootroot00000000000000  backend-test:¤ 5 x substringslength" StringSplit* maxsplit test_string_split_maxsplitZ x   b substrings    b length   B onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/test_data_set_0/000077500000000000000000000000001511334557700320205ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/test_data_set_0/input_0.pb000066400000000000000000000000751511334557700337230ustar00rootroot000000000000002 hello world2def.net2o n n x2the quick brown foxBxonnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/test_data_set_0/output_0.pb000066400000000000000000000001231511334557700341160ustar00rootroot000000000000002hello2world22def.net222o2n2n x2the2quick2 brown foxB substringsonnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_maxsplit/test_data_set_0/output_1.pb000066400000000000000000000000601511334557700341170ustar00rootroot00000000000000BlengthJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/000077500000000000000000000000001511334557700275675ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/model.onnx000066400000000000000000000002441511334557700315730ustar00rootroot00000000000000  backend-test:‹ $ x substringslength" StringSplittest_string_split_no_delimiterZ x  b substrings   b length  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/test_data_set_0/000077500000000000000000000000001511334557700326315ustar00rootroot00000000000000input_0.pb000066400000000000000000000000741511334557700344540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/test_data_set_02 hello world !2 hello world !2 hello world ! Bxoutput_0.pb000066400000000000000000000001051511334557700346500ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/test_data_set_02hello2world2!2hello2world2!2hello2world2!B substringsoutput_1.pb000066400000000000000000000000461511334557700346550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_string_split_no_delimiter/test_data_set_0BlengthJonnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_lower/000077500000000000000000000000001511334557700345125ustar00rootroot00000000000000model.onnx000066400000000000000000000003361511334557700364410ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_lower backend-test:Å j xy"StringNormalizer* case_change_action"LOWER * is_case_sensitive * stopwordsJmonday 5test_strnormalizer_export_monday_casesensintive_lowerZ x  b y  B  test_data_set_0/000077500000000000000000000000001511334557700374755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_lowerinput_0.pb000066400000000000000000000000551511334557700413760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_lower/test_data_set_02monday2tuesday2 wednesday2thursdayBxoutput_0.pb000066400000000000000000000000451511334557700415760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_lower/test_data_set_02tuesday2 wednesday2thursdayBytest_strnormalizer_export_monday_casesensintive_nochangecase/000077500000000000000000000000001511334557700357215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/nodemodel.onnx000066400000000000000000000003051511334557700377230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_strnormalizer_export_monday_casesensintive_nochangecase backend-test:Ŧ J xy"StringNormalizer* is_case_sensitive * stopwordsJmonday “Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_sub/test_data_set_0/input_1.pb000066400000000000000000000003761511334557700300570ustar00rootroot00000000000000ByJđ^&,ŋZ¸ž[*PŋÔöÜŋ3¯5>;ļÍžWĒĐŋĖņė>ĩDhŋ˛ÄT=ŽĨ:?>âב?Æžŋš˙Í>ˇO/ŋė^ŋ~/ŋЃŸžĄ f=Ą#•ŋ‘œf?Okî>ĸŖÄŋ ž?Ŧō?@â–?7>8žl‰ŋFø†?5mΞyœ? 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max_gram_length * max_skip_count * min_gram_length * mode"TF * ngram_counts@@ * ngram_indexes@@@@@@@ *$ pool_int64s@@@@@@@@@@ *test_tfidfvectorizer_tf_only_bigrams_skip0Z X   b Y  B  test_data_set_0/000077500000000000000000000000001511334557700350545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_only_bigrams_skip0input_0.pb000066400000000000000000000000711511334557700367530ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_only_bigrams_skip0/test_data_set_0 BXJ0output_0.pb000066400000000000000000000000451511334557700371550ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_only_bigrams_skip0/test_data_set_0BYJ€?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_levelempty/000077500000000000000000000000001511334557700330725ustar00rootroot00000000000000model.onnx000066400000000000000000000004521511334557700350200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_levelempty backend-test:‘ ŧ XY"TfIdfVectorizer* max_gram_length * max_skip_count * min_gram_length * mode"TF * ngram_counts@@ * ngram_indexes@@@ * pool_int64s@@@@@@ .test_tfidfvectorizer_tf_onlybigrams_levelemptyZ X   b Y  B  test_data_set_0/000077500000000000000000000000001511334557700360555ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_levelemptyinput_0.pb000066400000000000000000000000711511334557700377540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_levelempty/test_data_set_0 BXJ0output_0.pb000066400000000000000000000000251511334557700401540ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_levelempty/test_data_set_0BYJ €?€?€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_skip5/000077500000000000000000000000001511334557700317375ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_skip5/model.onnx000066400000000000000000000004651511334557700337500ustar00rootroot00000000000000 backend-test:œ Ė XY"TfIdfVectorizer* max_gram_length * max_skip_count * min_gram_length * mode"TF * ngram_counts@@ * ngram_indexes@@@@@@@ *$ pool_int64s@@@@@@@@@@ )test_tfidfvectorizer_tf_onlybigrams_skip5Z X   b Y  B  test_data_set_0/000077500000000000000000000000001511334557700347225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_skip5input_0.pb000066400000000000000000000000711511334557700366210ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_skip5/test_data_set_0 BXJ0output_0.pb000066400000000000000000000000451511334557700370230ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_onlybigrams_skip5/test_data_set_0BYJ€?@@€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_uniandbigrams_skip5/000077500000000000000000000000001511334557700322345ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_uniandbigrams_skip5/model.onnx000066400000000000000000000004671511334557700342470ustar00rootroot00000000000000 backend-test:ž Ė XY"TfIdfVectorizer* max_gram_length * max_skip_count * min_gram_length * mode"TF * ngram_counts@@ * ngram_indexes@@@@@@@ *$ pool_int64s@@@@@@@@@@ +test_tfidfvectorizer_tf_uniandbigrams_skip5Z X   b Y  B  test_data_set_0/000077500000000000000000000000001511334557700352175ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_uniandbigrams_skip5input_0.pb000066400000000000000000000000711511334557700371160ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_uniandbigrams_skip5/test_data_set_0 BXJ0output_0.pb000066400000000000000000000000451511334557700373200ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_tfidfvectorizer_tf_uniandbigrams_skip5/test_data_set_0BYJ@@€?€?@@€?onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu/000077500000000000000000000000001511334557700255115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu/model.onnx000066400000000000000000000002121511334557700275100ustar00rootroot00000000000000  backend-test:r ( xy"ThresholdedRelu* alpha@ test_thresholdedreluZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu/test_data_set_0/000077500000000000000000000000001511334557700305535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu/test_data_set_0/input_0.pb000066400000000000000000000003761511334557700324620ustar00rootroot00000000000000BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžonnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu/test_data_set_0/output_0.pb000066400000000000000000000003761511334557700326630ustar00rootroot00000000000000ByJđËj@ŠC@onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default/000077500000000000000000000000001511334557700272155ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default/model.onnx000066400000000000000000000002011511334557700312120ustar00rootroot00000000000000  backend-test:i  xy"ThresholdedRelutest_thresholdedrelu_defaultZ x    b y    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default/test_data_set_0/000077500000000000000000000000001511334557700322575ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700341070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700343100ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default/test_data_set_0ByJđxĖá?Ëj@$ ī?ĸ%ē?ü=ŋ?ŠC@2Ä?ķŧ?*z?•į™?ŗų?onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default_expanded_ver18/000077500000000000000000000000001511334557700321125ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default_expanded_ver18/model.onnx000066400000000000000000000017411511334557700341210ustar00rootroot00000000000000 backend-test:Č iDThresholdedRelu_test_thresholdedrelu_default_expanded_function_Alpha"Constant* value_float€? : Ÿ DThresholdedRelu_test_thresholdedrelu_default_expanded_function_Alpha xHThresholdedRelu_test_thresholdedrelu_default_expanded_function_AlphaCast"CastLike: iCThresholdedRelu_test_thresholdedrelu_default_expanded_function_Zero"Constant* value* "B :  CThresholdedRelu_test_thresholdedrelu_default_expanded_function_Zero xGThresholdedRelu_test_thresholdedrelu_default_expanded_function_ZeroCast"CastLike: ¤ HThresholdedRelu_test_thresholdedrelu_default_expanded_function_AlphaCast xMThresholdedRelu_test_thresholdedrelu_default_expanded_function_AlphaLessThanX"Less: § MThresholdedRelu_test_thresholdedrelu_default_expanded_function_AlphaLessThanX x GThresholdedRelu_test_thresholdedrelu_default_expanded_function_ZeroCasty"Where:+test_thresholdedrelu_default_expanded_ver18Z x    b y    B test_data_set_0/000077500000000000000000000000001511334557700350755ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default_expanded_ver18input_0.pb000066400000000000000000000003761511334557700370040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default_expanded_ver18/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_0.pb000066400000000000000000000003761511334557700372050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_default_expanded_ver18/test_data_set_0ByJđxĖá?Ëj@$ ī?ĸ%ē?ü=ŋ?ŠC@2Ä?ķŧ?*z?•į™?ŗų?onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example/000077500000000000000000000000001511334557700272245ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example/model.onnx000066400000000000000000000002021511334557700312220ustar00rootroot00000000000000  backend-test:j ( xy"ThresholdedRelu* alpha@ test_thresholdedrelu_exampleZ x  b y  B onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example/test_data_set_0/000077500000000000000000000000001511334557700322665ustar00rootroot00000000000000input_0.pb000066400000000000000000000000351511334557700341060ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example/test_data_set_0BxJĀŋš™™?@ÍĖ @output_0.pb000066400000000000000000000000351511334557700343070ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example/test_data_set_0ByJÍĖ @onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example_expanded_ver18/000077500000000000000000000000001511334557700321215ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example_expanded_ver18/model.onnx000066400000000000000000000017211511334557700341260ustar00rootroot00000000000000 backend-test:¸ iDThresholdedRelu_test_thresholdedrelu_example_expanded_function_Alpha"Constant* value_float@ : Ÿ DThresholdedRelu_test_thresholdedrelu_example_expanded_function_Alpha xHThresholdedRelu_test_thresholdedrelu_example_expanded_function_AlphaCast"CastLike: iCThresholdedRelu_test_thresholdedrelu_example_expanded_function_Zero"Constant* value* "B :  CThresholdedRelu_test_thresholdedrelu_example_expanded_function_Zero xGThresholdedRelu_test_thresholdedrelu_example_expanded_function_ZeroCast"CastLike: ¤ HThresholdedRelu_test_thresholdedrelu_example_expanded_function_AlphaCast xMThresholdedRelu_test_thresholdedrelu_example_expanded_function_AlphaLessThanX"Less: § MThresholdedRelu_test_thresholdedrelu_example_expanded_function_AlphaLessThanX x GThresholdedRelu_test_thresholdedrelu_example_expanded_function_ZeroCasty"Where:+test_thresholdedrelu_example_expanded_ver18Z x  b y  B test_data_set_0/000077500000000000000000000000001511334557700351045ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example_expanded_ver18input_0.pb000066400000000000000000000000351511334557700370030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example_expanded_ver18/test_data_set_0BxJĀŋš™™?@ÍĖ @output_0.pb000066400000000000000000000000351511334557700372040ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_thresholdedrelu_example_expanded_ver18/test_data_set_0ByJÍĖ 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio/test_data_set_0/000077500000000000000000000000001511334557700332025ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700350320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžinput_1.pb000066400000000000000000000000131511334557700350170ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio/test_data_set_0BrJinput_2.pb000066400000000000000000000000101511334557700350150ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio/test_data_set_0 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0/000077500000000000000000000000001511334557700342155ustar00rootroot00000000000000input_0.pb000066400000000000000000000003761511334557700360450ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0BxJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžinput_1.pb000066400000000000000000000000131511334557700360320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0BrJinput_2.pb000066400000000000000000000000101511334557700360300ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0 BtJoutput_0.pb000066400000000000000000000003761511334557700362460ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0ByJđxĖá?háĖ>“Žz?Ëj@$ ī?â.zŋ˙8s?bũžhdĶŊø9Ō>(€>ĸ%ē?^ĶB?Ā0ų= Bã>]×Ē>ü=ŋ?RžiJ >ĻZŋ/d#ŒS'?ąK]?‡ū=ŋŠC@¨(ēŋHm;= ­?ž2Ä?ķŧ?ŠĒ>…žÁ>íEcŋŊŠũŋ‹!˛žō >*z?•į™?ŗOÆžmĮšžü6†ŋ&ÃĩŋgÚŋŗų?‘xŋFKāž™[ ŋœ G?4”Îŋ—ØYžL=eŋÆ> Äŋõ—ŋkŪæŧQNÛ>.:ˆ=™Ũš>īb"ŋ6ššžoutput_1.pb000066400000000000000000000001111511334557700362320ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_training_dropout_zero_ratio_mask/test_data_set_0 BzJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_0/000077500000000000000000000000001511334557700302135ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_0/model.onnx000066400000000000000000000002521511334557700322160ustar00rootroot00000000000000  backend-test:‘ . data transposed" Transpose* perm@@@ !test_transpose_all_permutations_0Z data    b transposed    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_0/test_data_set_0/000077500000000000000000000000001511334557700332555ustar00rootroot00000000000000input_0.pb000066400000000000000000000001601511334557700350740ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_0/test_data_set_0BdataJ`  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?output_0.pb000066400000000000000000000001661511334557700353030ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_0/test_data_set_0B transposedJ`  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_1/000077500000000000000000000000001511334557700302145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_1/model.onnx000066400000000000000000000002521511334557700322170ustar00rootroot00000000000000  backend-test:‘ . data transposed" Transpose* perm@@@ !test_transpose_all_permutations_1Z data    b transposed    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_1/test_data_set_0/000077500000000000000000000000001511334557700332565ustar00rootroot00000000000000input_0.pb000066400000000000000000000001601511334557700350750ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_1/test_data_set_0BdataJ`  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_2/test_data_set_0/000077500000000000000000000000001511334557700332575ustar00rootroot00000000000000input_0.pb000066400000000000000000000001601511334557700350760ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_2/test_data_set_0BdataJ`  ?Ļ7?ŗN?w} ?HéØ>QY%?n ā>~ŽJ?¨e?^k?įķl?Z{‘=Ųp˛= ĄĨ<“&U?H5G?š^?ģ†z?¨•L?Gė>ÃĐG?output_0.pb000066400000000000000000000001661511334557700353050ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_transpose_all_permutations_2/test_data_set_0B transposedJ`  ?Ļ7?ŗN?w} ?^k?įķl?Z{‘=Ųp˛=HéØ>QY%?n 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onnx-onnx-bca0315/onnx/backend/test/data/node/test_where_long_example/test_data_set_0/000077500000000000000000000000001511334557700312225ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_where_long_example/test_data_set_0/input_0.pb000066400000000000000000000000271511334557700331220ustar00rootroot00000000000000 B conditionJonnx-onnx-bca0315/onnx/backend/test/data/node/test_where_long_example/test_data_set_0/input_1.pb000066400000000000000000000000531511334557700331220ustar00rootroot00000000000000BxJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_where_long_example/test_data_set_0/input_2.pb000066400000000000000000000000531511334557700331230ustar00rootroot00000000000000ByJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_where_long_example/test_data_set_0/output_0.pb000066400000000000000000000000531511334557700333220ustar00rootroot00000000000000BzJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/000077500000000000000000000000001511334557700241115ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/model.onnx000066400000000000000000000002321511334557700261120ustar00rootroot00000000000000  backend-test: " x padsy"Pad* mode"wrap  test_wrap_padZ x     Z pads  b y     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/test_data_set_0/000077500000000000000000000000001511334557700271535ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/test_data_set_0/input_0.pb000066400000000000000000000004001511334557700310460ustar00rootroot00000000000000BxJđ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/test_data_set_0/input_1.pb000066400000000000000000000001141511334557700310510ustar00rootroot00000000000000BpadsJ@onnx-onnx-bca0315/onnx/backend/test/data/node/test_wrap_pad/test_data_set_0/output_0.pb000066400000000000000000000010101511334557700312450ustar00rootroot00000000000000ByJø˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/000077500000000000000000000000001511334557700233525ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/model.onnx000066400000000000000000000001671511334557700253620ustar00rootroot00000000000000 backend-test:_  x yxor"Xor test_xor2dZ x    Z y    b xor    B onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/test_data_set_0/000077500000000000000000000000001511334557700264145ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/test_data_set_0/input_0.pb000066400000000000000000000000271511334557700303140ustar00rootroot00000000000000 BxJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/test_data_set_0/input_1.pb000066400000000000000000000000271511334557700303150ustar00rootroot00000000000000 ByJ onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor2d/test_data_set_0/output_0.pb000066400000000000000000000000311511334557700305100ustar00rootroot00000000000000 BxorJ 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ByJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor3d/test_data_set_0/output_0.pb000066400000000000000000000001131511334557700305120ustar00rootroot00000000000000 BxorJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor4d/000077500000000000000000000000001511334557700233545ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor4d/model.onnx000066400000000000000000000002171511334557700253600ustar00rootroot00000000000000 backend-test:w  x yxor"Xor test_xor4dZ x      Z y      b xor      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor4d/test_data_set_0/000077500000000000000000000000001511334557700264165ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor4d/test_data_set_0/input_0.pb000066400000000000000000000005701511334557700303210ustar00rootroot00000000000000 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backend-test:o  x yxor"Xortest_xor_bcast3v2dZ x     Z y    b xor     B onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast3v2d/test_data_set_0/000077500000000000000000000000001511334557700300415ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast3v2d/test_data_set_0/input_0.pb000066400000000000000000000001111511334557700317330ustar00rootroot00000000000000 BxJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast3v2d/test_data_set_0/input_1.pb000066400000000000000000000000371511334557700317430ustar00rootroot00000000000000 ByJonnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast3v2d/test_data_set_0/output_0.pb000066400000000000000000000001131511334557700321360ustar00rootroot00000000000000 BxorJ<onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast4v2d/000077500000000000000000000000001511334557700250005ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast4v2d/model.onnx000066400000000000000000000002171511334557700270040ustar00rootroot00000000000000 backend-test:w  x yxor"Xortest_xor_bcast4v2dZ x      Z y    b xor      B onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast4v2d/test_data_set_0/000077500000000000000000000000001511334557700300425ustar00rootroot00000000000000onnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast4v2d/test_data_set_0/input_0.pb000066400000000000000000000005701511334557700317450ustar00rootroot00000000000000 BxJčonnx-onnx-bca0315/onnx/backend/test/data/node/test_xor_bcast4v2d/test_data_set_0/input_1.pb000066400000000000000000000000511511334557700317400ustar00rootroot00000000000000 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